From 71d50fb89510c9e44d6c342e2b0438dd3931846c Mon Sep 17 00:00:00 2001 From: jafermarq Date: Sun, 25 Jun 2023 13:45:15 +0100 Subject: [PATCH 001/133] VCE with ActorPool --- src/py/flwr/server/app.py | 1 + src/py/flwr/server/server.py | 12 ++ src/py/flwr/simulation/app.py | 25 +++- .../simulation/ray_transport/ray_actor.py | 11 ++ .../ray_transport/ray_client_proxy.py | 117 ++++++++++++++++++ 5 files changed, 163 insertions(+), 3 deletions(-) create mode 100644 src/py/flwr/simulation/ray_transport/ray_actor.py diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py index fdfaba4c90bb..c90a8b3ed673 100644 --- a/src/py/flwr/server/app.py +++ b/src/py/flwr/server/app.py @@ -72,6 +72,7 @@ class ServerConfig: num_rounds: int = 1 round_timeout: Optional[float] = None + is_simulation: bool = False def start_server( # pylint: disable=too-many-arguments,too-many-locals diff --git a/src/py/flwr/server/server.py b/src/py/flwr/server/server.py index 279914e801d6..d53cc0c6d9a6 100644 --- a/src/py/flwr/server/server.py +++ b/src/py/flwr/server/server.py @@ -67,6 +67,7 @@ def __init__( ) self.strategy: Strategy = strategy if strategy is not None else FedAvg() self.max_workers: Optional[int] = None + self.is_simulation: bool = False def set_max_workers(self, max_workers: Optional[int]) -> None: """Set the max_workers used by ThreadPoolExecutor.""" @@ -231,6 +232,7 @@ def fit_round( results, failures = fit_clients( client_instructions=client_instructions, max_workers=self.max_workers, + is_simulation=self.is_simulation, timeout=timeout, ) log( @@ -325,6 +327,7 @@ def reconnect_client( def fit_clients( client_instructions: List[Tuple[ClientProxy, FitIns]], + is_simulation: bool, max_workers: Optional[int], timeout: Optional[float], ) -> FitResultsAndFailures: @@ -346,6 +349,15 @@ def fit_clients( _handle_finished_future_after_fit( future=future, results=results, failures=failures ) + + if is_simulation: + # because how the pool of actors and the client proxies interact + # a client w/ id A could have fetched the result of client w/ id B + # Therefore, we need to ensure each FiTRes is paired with its respective + # ClientProxy + print("Results from ActorPool might not be in order!") + # TODO + return results, failures diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 542982d16ce4..59c803ddd827 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -20,6 +20,7 @@ from typing import Any, Callable, Dict, List, Optional import ray +from ray.util.actor_pool import ActorPool from flwr.client import ClientLike from flwr.common import EventType, event @@ -29,7 +30,8 @@ from flwr.server.client_manager import ClientManager from flwr.server.history import History from flwr.server.strategy import Strategy -from flwr.simulation.ray_transport.ray_client_proxy import RayClientProxy +from flwr.simulation.ray_transport.ray_client_proxy import RayClientProxy, RayClientProxyForActorPool +from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActor INVALID_ARGUMENTS_START_SIMULATION = """ INVALID ARGUMENTS ERROR @@ -142,6 +144,8 @@ def start_simulation( # pylint: disable=too-many-arguments strategy=strategy, client_manager=client_manager, ) + # Setting simulation ON for server + initialized_server.is_simulation = True log( INFO, "Starting Flower simulation, config: %s", @@ -184,11 +188,26 @@ def start_simulation( # pylint: disable=too-many-arguments # Register one RayClientProxy object for each client with the ClientManager resources = client_resources if client_resources is not None else {} + + + # instantiate ActorPool + # Let's spawn a pool with as many actors as could be fit in the system + num_gpus = ray.cluster_resources()['GPU'] + num_actors = int(num_gpus / resources['num_gpus']) + actors = [VirtualClientEngineActor.options(**resources).remote(i) for i in range(num_actors)] + log( + INFO, + "Flower VCE: creating ActorPool with %s actors", + len(actors) + ) + + pool = ActorPool(actors) + for cid in cids: - client_proxy = RayClientProxy( + client_proxy = RayClientProxyForActorPool( client_fn=client_fn, cid=cid, - resources=resources, + actor_pool=pool, ) initialized_server.client_manager().register(client=client_proxy) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py new file mode 100644 index 000000000000..e65ba68d171f --- /dev/null +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -0,0 +1,11 @@ +import ray + +@ray.remote +class VirtualClientEngineActor: + + def __init__(self, actor_id: int): + + self.actor_id = actor_id + + def run(self, client_fn, client_id): + return client_id, client_fn() diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index 0543999b4178..ad219475341a 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -17,6 +17,7 @@ from logging import ERROR from typing import Callable, Dict, Optional, cast +from time import sleep import ray @@ -30,6 +31,7 @@ ) from flwr.common.logger import log from flwr.server.client_proxy import ClientProxy +from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActor ClientFn = Callable[[str], ClientLike] @@ -115,6 +117,121 @@ def reconnect( return common.DisconnectRes(reason="") # Nothing to do here (yet) +class RayClientProxyForActorPool(ClientProxy): + """Flower client proxy which delegates work using Ray.""" + + def __init__(self, client_fn: ClientFn, cid: str, actor_pool: VirtualClientEngineActor): + super().__init__(cid) + self.client_fn = client_fn + self.actor_pool = actor_pool + + def get_properties( + self, ins: common.GetPropertiesIns, timeout: Optional[float] + ) -> common.GetPropertiesRes: + """Return client's properties.""" + + def get_properties(): + client: Client = _create_client(self.client_fn, self.cid) + return maybe_call_get_properties(client=client, + get_properties_ins=ins, + ) + try: + self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), get_properties) + while not(self.actor_pool.has_next()): + sleep(0.1) + continue + cid, res = self.actor_pool.get_next() + + # print(cid, self.cid) + + except Exception as ex: + log(ERROR, ex) + raise ex + return cast( + common.GetPropertiesRes, + res, + ) + + def get_parameters( + self, ins: common.GetParametersIns, timeout: Optional[float] + ) -> common.GetParametersRes: + """Return the current local model parameters.""" + + def get_parameters(): + client: Client = _create_client(self.client_fn, self.cid) + return maybe_call_get_parameters(client=client, + get_parameters_ins=ins, + ) + try: + self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), get_parameters) + while not(self.actor_pool.has_next()): + sleep(0.1) + continue + cid, res = self.actor_pool.get_next() + # print(cid, self.cid) + except Exception as ex: + log(ERROR, ex) + raise ex + return cast( + common.GetParametersRes, + res, + ) + + def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes: + """Train model parameters on the locally held dataset.""" + def fit(): + client: Client = _create_client(self.client_fn, self.cid) + return maybe_call_fit(client=client, + fit_ins=ins, + ) + try: + self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), fit) + #! This is not ideal. A preferred solution would be to collect results from actor pool in server.fit_clients() + while not(self.actor_pool.has_next()): + sleep(0.1) + continue + cid, res = self.actor_pool.get_next() + # print(cid, self.cid) + except Exception as ex: + log(ERROR, ex) + raise ex + return cast( + common.FitRes, + res, + ) + + def evaluate( + self, ins: common.EvaluateIns, timeout: Optional[float] + ) -> common.EvaluateRes: + """Evaluate model parameters on the locally held dataset.""" + def evaluate(): + client: Client = _create_client(self.client_fn, self.cid) + #! This is not ideal. A preferred solution would be to collect results from actor pool in server.evaluate_clients() + return maybe_call_evaluate(client=client, + evaluate_ins=ins, + ) + try: + self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), evaluate) + while not(self.actor_pool.has_next()): + sleep(0.1) + continue + cid, res = self.actor_pool.get_next() + # print(cid, self.cid) + except Exception as ex: + log(ERROR, ex) + raise ex + return cast( + common.EvaluateRes, + res, + ) + + def reconnect( + self, ins: common.ReconnectIns, timeout: Optional[float] + ) -> common.DisconnectRes: + """Disconnect and (optionally) reconnect later.""" + return common.DisconnectRes(reason="") # Nothing to do here (yet) + + @ray.remote def launch_and_get_properties( client_fn: ClientFn, cid: str, get_properties_ins: common.GetPropertiesIns From 6f91e7011817cd2f9554df1eab645868d05afbf6 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Mon, 26 Jun 2023 19:51:25 +0100 Subject: [PATCH 002/133] each clientproxy gets its result (discarding other logic in server.py) --- src/py/flwr/server/app.py | 1 - src/py/flwr/server/server.py | 12 ---- src/py/flwr/simulation/app.py | 5 ++ .../simulation/ray_transport/ray_actor.py | 3 + .../ray_transport/ray_client_proxy.py | 56 +++++++++++-------- 5 files changed, 42 insertions(+), 35 deletions(-) diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py index c90a8b3ed673..fdfaba4c90bb 100644 --- a/src/py/flwr/server/app.py +++ b/src/py/flwr/server/app.py @@ -72,7 +72,6 @@ class ServerConfig: num_rounds: int = 1 round_timeout: Optional[float] = None - is_simulation: bool = False def start_server( # pylint: disable=too-many-arguments,too-many-locals diff --git a/src/py/flwr/server/server.py b/src/py/flwr/server/server.py index d53cc0c6d9a6..279914e801d6 100644 --- a/src/py/flwr/server/server.py +++ b/src/py/flwr/server/server.py @@ -67,7 +67,6 @@ def __init__( ) self.strategy: Strategy = strategy if strategy is not None else FedAvg() self.max_workers: Optional[int] = None - self.is_simulation: bool = False def set_max_workers(self, max_workers: Optional[int]) -> None: """Set the max_workers used by ThreadPoolExecutor.""" @@ -232,7 +231,6 @@ def fit_round( results, failures = fit_clients( client_instructions=client_instructions, max_workers=self.max_workers, - is_simulation=self.is_simulation, timeout=timeout, ) log( @@ -327,7 +325,6 @@ def reconnect_client( def fit_clients( client_instructions: List[Tuple[ClientProxy, FitIns]], - is_simulation: bool, max_workers: Optional[int], timeout: Optional[float], ) -> FitResultsAndFailures: @@ -349,15 +346,6 @@ def fit_clients( _handle_finished_future_after_fit( future=future, results=results, failures=failures ) - - if is_simulation: - # because how the pool of actors and the client proxies interact - # a client w/ id A could have fetched the result of client w/ id B - # Therefore, we need to ensure each FiTRes is paired with its respective - # ClientProxy - print("Results from ActorPool might not be in order!") - # TODO - return results, failures diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 59c803ddd827..2662e4247642 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -202,12 +202,17 @@ def start_simulation( # pylint: disable=too-many-arguments ) pool = ActorPool(actors) + # ClientProxies might be retrieving results from the ActorPool that belong to other clients + # when this happens, the client proxy will put that result in the cache. All clients check + # if their result is in the cache periodically. + results_cache = {} for cid in cids: client_proxy = RayClientProxyForActorPool( client_fn=client_fn, cid=cid, actor_pool=pool, + cache=results_cache, ) initialized_server.client_manager().register(client=client_proxy) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index e65ba68d171f..1452a2b6166d 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -8,4 +8,7 @@ def __init__(self, actor_id: int): self.actor_id = actor_id def run(self, client_fn, client_id): + # execute tasks and return result + # return also cid which is needed to ensure results + # from the pool are correctly assigned to each ClientProxy return client_id, client_fn() diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index ad219475341a..26b5a7511cec 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -120,10 +120,36 @@ def reconnect( class RayClientProxyForActorPool(ClientProxy): """Flower client proxy which delegates work using Ray.""" - def __init__(self, client_fn: ClientFn, cid: str, actor_pool: VirtualClientEngineActor): + def __init__(self, client_fn: ClientFn, cid: str, actor_pool: VirtualClientEngineActor, cache: Dict): super().__init__(cid) self.client_fn = client_fn self.actor_pool = actor_pool + self.cache = cache + + + def _wait_for_client_result(self): + # TODO: can we do this without while+sleep? + # Wait until one result is ready + while not(self.actor_pool.has_next()): + sleep(0.1) + continue + # get result + cid, res = self.actor_pool.get_next() + + # if it doesn't belong to this client + if cid != self.cid: + # add to cache + self.cache[cid] = res + + # wait until this clientProxy's result is in the cache + while self.cid not in self.cache.keys(): + sleep(0.1) + + # get this client's result + res = self.cache.pop(self.cid) + + return res + def get_properties( self, ins: common.GetPropertiesIns, timeout: Optional[float] @@ -137,12 +163,7 @@ def get_properties(): ) try: self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), get_properties) - while not(self.actor_pool.has_next()): - sleep(0.1) - continue - cid, res = self.actor_pool.get_next() - - # print(cid, self.cid) + res = self._wait_for_client_result() except Exception as ex: log(ERROR, ex) @@ -164,11 +185,8 @@ def get_parameters(): ) try: self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), get_parameters) - while not(self.actor_pool.has_next()): - sleep(0.1) - continue - cid, res = self.actor_pool.get_next() - # print(cid, self.cid) + res = self._wait_for_client_result() + except Exception as ex: log(ERROR, ex) raise ex @@ -187,11 +205,8 @@ def fit(): try: self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), fit) #! This is not ideal. A preferred solution would be to collect results from actor pool in server.fit_clients() - while not(self.actor_pool.has_next()): - sleep(0.1) - continue - cid, res = self.actor_pool.get_next() - # print(cid, self.cid) + res = self._wait_for_client_result() + except Exception as ex: log(ERROR, ex) raise ex @@ -212,11 +227,8 @@ def evaluate(): ) try: self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), evaluate) - while not(self.actor_pool.has_next()): - sleep(0.1) - continue - cid, res = self.actor_pool.get_next() - # print(cid, self.cid) + res = self._wait_for_client_result() + except Exception as ex: log(ERROR, ex) raise ex From f6588dd091e8d16c3d7ec9e0a82fae68180b45aa Mon Sep 17 00:00:00 2001 From: jafermarq Date: Sat, 8 Jul 2023 15:07:15 +0100 Subject: [PATCH 003/133] custom ActorPool addressable by each ClientProxy --- src/py/flwr/simulation/app.py | 13 +++-- .../simulation/ray_transport/ray_actor.py | 40 +++++++++++++++ .../ray_transport/ray_client_proxy.py | 49 +++++-------------- 3 files changed, 57 insertions(+), 45 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 2662e4247642..6390b63189e1 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -20,7 +20,6 @@ from typing import Any, Callable, Dict, List, Optional import ray -from ray.util.actor_pool import ActorPool from flwr.client import ClientLike from flwr.common import EventType, event @@ -30,8 +29,8 @@ from flwr.server.client_manager import ClientManager from flwr.server.history import History from flwr.server.strategy import Strategy -from flwr.simulation.ray_transport.ray_client_proxy import RayClientProxy, RayClientProxyForActorPool -from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActor +from flwr.simulation.ray_transport.ray_client_proxy import RayClientProxy, RayActorClientProxy +from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActor, VirtualClientEngineActorPool INVALID_ARGUMENTS_START_SIMULATION = """ INVALID ARGUMENTS ERROR @@ -192,8 +191,8 @@ def start_simulation( # pylint: disable=too-many-arguments # instantiate ActorPool # Let's spawn a pool with as many actors as could be fit in the system - num_gpus = ray.cluster_resources()['GPU'] - num_actors = int(num_gpus / resources['num_gpus']) + num_gpus = ray.cluster_resources()['CPU'] + num_actors = int(num_gpus / resources['num_cpus']) actors = [VirtualClientEngineActor.options(**resources).remote(i) for i in range(num_actors)] log( INFO, @@ -201,14 +200,14 @@ def start_simulation( # pylint: disable=too-many-arguments len(actors) ) - pool = ActorPool(actors) + pool = VirtualClientEngineActorPool(actors) # ClientProxies might be retrieving results from the ActorPool that belong to other clients # when this happens, the client proxy will put that result in the cache. All clients check # if their result is in the cache periodically. results_cache = {} for cid in cids: - client_proxy = RayClientProxyForActorPool( + client_proxy = RayActorClientProxy( client_fn=client_fn, cid=cid, actor_pool=pool, diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 1452a2b6166d..9ff29d092eeb 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -1,4 +1,7 @@ +from typing import Any, Callable + import ray +from ray.util.actor_pool import ActorPool @ray.remote class VirtualClientEngineActor: @@ -12,3 +15,40 @@ def run(self, client_fn, client_id): # return also cid which is needed to ensure results # from the pool are correctly assigned to each ClientProxy return client_id, client_fn() + + +class VirtualClientEngineActorPool(ActorPool): + + def __init__(self, actors: list): + super().__init__(actors) + + # stores completed job while keeping track to + # which VirtualClient it belongs to + self._results = {} + + def submit_client_job(self, fn: Any, value: Callable, cid: int): + self._results[cid] = None + # print(f"Submitted to pool from VirtualClient {cid}") + return super().submit(fn, value) + + def get_client_result(self, cid: int, timeout: int=3600): + + while self._results[cid] is None: + # we need a try/except because if all VirtualClients are pinging the queue for the first + # result, only one of them will be able to "fetch" it. Leaving the rest trying to fetch + # the object following a reference that doesn't exist anymore. + try: + res_cid, res = self.get_next_unordered() + # Track in dictionary + # print(f"Adding result to dict for cid: {res_cid}") + self._results[res_cid] = res + except: + continue + + # print(f"VirtualClient with cid: {cid} still needs to wait for its result...") + # ready = not(self._results[cid] is None) + # print(f"is result for {cid} read?: {ready}") + + # print(f"Returning result for client {cid}") + return self._results.pop(cid) + \ No newline at end of file diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index 26b5a7511cec..f8f635235200 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -31,7 +31,7 @@ ) from flwr.common.logger import log from flwr.server.client_proxy import ClientProxy -from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActor +from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActorPool ClientFn = Callable[[str], ClientLike] @@ -117,40 +117,15 @@ def reconnect( return common.DisconnectRes(reason="") # Nothing to do here (yet) -class RayClientProxyForActorPool(ClientProxy): +class RayActorClientProxy(ClientProxy): """Flower client proxy which delegates work using Ray.""" - def __init__(self, client_fn: ClientFn, cid: str, actor_pool: VirtualClientEngineActor, cache: Dict): + def __init__(self, client_fn: ClientFn, cid: str, actor_pool: VirtualClientEngineActorPool, cache: Dict): super().__init__(cid) self.client_fn = client_fn self.actor_pool = actor_pool self.cache = cache - - def _wait_for_client_result(self): - # TODO: can we do this without while+sleep? - # Wait until one result is ready - while not(self.actor_pool.has_next()): - sleep(0.1) - continue - # get result - cid, res = self.actor_pool.get_next() - - # if it doesn't belong to this client - if cid != self.cid: - # add to cache - self.cache[cid] = res - - # wait until this clientProxy's result is in the cache - while self.cid not in self.cache.keys(): - sleep(0.1) - - # get this client's result - res = self.cache.pop(self.cid) - - return res - - def get_properties( self, ins: common.GetPropertiesIns, timeout: Optional[float] ) -> common.GetPropertiesRes: @@ -162,8 +137,8 @@ def get_properties(): get_properties_ins=ins, ) try: - self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), get_properties) - res = self._wait_for_client_result() + self.actor_pool.submit_client_job(lambda a, v : a.run.remote(v, self.cid), get_properties, self.cid) + res = self.actor_pool.get_client_result(self.cid) except Exception as ex: log(ERROR, ex) @@ -184,8 +159,8 @@ def get_parameters(): get_parameters_ins=ins, ) try: - self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), get_parameters) - res = self._wait_for_client_result() + self.actor_pool.submit_client_job(lambda a, v : a.run.remote(v, self.cid), get_parameters, self.cid) + res = self.actor_pool.get_client_result(self.cid) except Exception as ex: log(ERROR, ex) @@ -203,9 +178,8 @@ def fit(): fit_ins=ins, ) try: - self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), fit) - #! This is not ideal. A preferred solution would be to collect results from actor pool in server.fit_clients() - res = self._wait_for_client_result() + self.actor_pool.submit_client_job(lambda a, v : a.run.remote(v, self.cid), fit, self.cid) + res = self.actor_pool.get_client_result(self.cid) except Exception as ex: log(ERROR, ex) @@ -221,13 +195,12 @@ def evaluate( """Evaluate model parameters on the locally held dataset.""" def evaluate(): client: Client = _create_client(self.client_fn, self.cid) - #! This is not ideal. A preferred solution would be to collect results from actor pool in server.evaluate_clients() return maybe_call_evaluate(client=client, evaluate_ins=ins, ) try: - self.actor_pool.submit(lambda a, v : a.run.remote(v, self.cid), evaluate) - res = self._wait_for_client_result() + self.actor_pool.submit_client_job(lambda a, v : a.run.remote(v, self.cid), evaluate, self.cid) + res = self.actor_pool.get_client_result(self.cid) except Exception as ex: log(ERROR, ex) From a1ee6306b6f679541fec3791481d70286d1100e0 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Sat, 8 Jul 2023 20:27:08 +0100 Subject: [PATCH 004/133] parse CPU/GPU resources to spawn correct size of ActorPool; other small changes --- src/py/flwr/simulation/app.py | 34 ++++++++++++------- .../ray_transport/ray_client_proxy.py | 3 +- 2 files changed, 23 insertions(+), 14 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 6390b63189e1..e5ca150c2e80 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -179,39 +179,49 @@ def start_simulation( # pylint: disable=too-many-arguments # Initialize Ray ray.init(**ray_init_args) + cluster_resources = ray.cluster_resources() log( INFO, "Flower VCE: Ray initialized with resources: %s", - ray.cluster_resources(), + cluster_resources, ) - # Register one RayClientProxy object for each client with the ClientManager - resources = client_resources if client_resources is not None else {} + # log resources for each virtual_client + # If not specified by user, Ray uses default: 1x CPU, 0x GPU + minimal_resources = {'num_cpus': 1.0, 'num_gpus': 0.0} + resources = client_resources if client_resources is not None else minimal_resources + log( + INFO, + "Flower VCE: Resources for each Virtual Client: %s", + resources, + ) + + # determine how many actors can be added to the pool. + # this a function of the total resources visible to Ray and + # the resources allocated for each virtual client + num_cpus = cluster_resources['CPU'] + num_gpus = cluster_resources.get('GPU', 0) # there might not be GPU + num_actors = int(num_cpus / resources['num_cpus']) + if num_gpus: + num_actors = min(num_actors, int(num_gpus / resources['num_gpus']) ) + # instantiate ActorPool - # Let's spawn a pool with as many actors as could be fit in the system - num_gpus = ray.cluster_resources()['CPU'] - num_actors = int(num_gpus / resources['num_cpus']) actors = [VirtualClientEngineActor.options(**resources).remote(i) for i in range(num_actors)] log( INFO, - "Flower VCE: creating ActorPool with %s actors", + "Flower VCE: Creating ActorPool with %s actors", len(actors) ) pool = VirtualClientEngineActorPool(actors) - # ClientProxies might be retrieving results from the ActorPool that belong to other clients - # when this happens, the client proxy will put that result in the cache. All clients check - # if their result is in the cache periodically. - results_cache = {} for cid in cids: client_proxy = RayActorClientProxy( client_fn=client_fn, cid=cid, actor_pool=pool, - cache=results_cache, ) initialized_server.client_manager().register(client=client_proxy) diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index f8f635235200..c7384689e52d 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -120,11 +120,10 @@ def reconnect( class RayActorClientProxy(ClientProxy): """Flower client proxy which delegates work using Ray.""" - def __init__(self, client_fn: ClientFn, cid: str, actor_pool: VirtualClientEngineActorPool, cache: Dict): + def __init__(self, client_fn: ClientFn, cid: str, actor_pool: VirtualClientEngineActorPool): super().__init__(cid) self.client_fn = client_fn self.actor_pool = actor_pool - self.cache = cache def get_properties( self, ins: common.GetPropertiesIns, timeout: Optional[float] From 8b09717cb44a97dae155ee6da534160ea28801ab Mon Sep 17 00:00:00 2001 From: jafermarq Date: Tue, 11 Jul 2023 12:54:05 +0100 Subject: [PATCH 005/133] handling clinents failure --- src/py/flwr/simulation/app.py | 42 ++++++++------ .../simulation/ray_transport/ray_actor.py | 44 +++++++++++---- .../ray_transport/ray_client_proxy.py | 56 +++++++++++++------ 3 files changed, 96 insertions(+), 46 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index e5ca150c2e80..2c027647f4dd 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -29,8 +29,14 @@ from flwr.server.client_manager import ClientManager from flwr.server.history import History from flwr.server.strategy import Strategy -from flwr.simulation.ray_transport.ray_client_proxy import RayClientProxy, RayActorClientProxy -from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActor, VirtualClientEngineActorPool +from flwr.simulation.ray_transport.ray_actor import ( + VirtualClientEngineActor, + VirtualClientEngineActorPool, +) +from flwr.simulation.ray_transport.ray_client_proxy import ( + RayActorClientProxy, + RayClientProxy, +) INVALID_ARGUMENTS_START_SIMULATION = """ INVALID ARGUMENTS ERROR @@ -188,8 +194,10 @@ def start_simulation( # pylint: disable=too-many-arguments # log resources for each virtual_client # If not specified by user, Ray uses default: 1x CPU, 0x GPU - minimal_resources = {'num_cpus': 1.0, 'num_gpus': 0.0} - resources = client_resources if client_resources is not None else minimal_resources + resources = {"num_cpus": 1.0, "num_gpus": 0.0} + if client_resources: + for k, v in client_resources.items(): + resources[k] = v log( INFO, "Flower VCE: Resources for each Virtual Client: %s", @@ -199,22 +207,22 @@ def start_simulation( # pylint: disable=too-many-arguments # determine how many actors can be added to the pool. # this a function of the total resources visible to Ray and # the resources allocated for each virtual client - num_cpus = cluster_resources['CPU'] - num_gpus = cluster_resources.get('GPU', 0) # there might not be GPU + num_cpus = cluster_resources["CPU"] + num_gpus = cluster_resources.get("GPU", 0) # there might not be GPU - num_actors = int(num_cpus / resources['num_cpus']) + num_actors = int(num_cpus / resources["num_cpus"]) + + # if a GPU is present and client resources do require one + if num_gpus and resources["num_gpus"] > 0.0: + num_actors = min(num_actors, int(num_gpus / resources["num_gpus"])) - if num_gpus: - num_actors = min(num_actors, int(num_gpus / resources['num_gpus']) ) - # instantiate ActorPool - actors = [VirtualClientEngineActor.options(**resources).remote(i) for i in range(num_actors)] - log( - INFO, - "Flower VCE: Creating ActorPool with %s actors", - len(actors) - ) - + actors = [ + VirtualClientEngineActor.options(**resources).remote(i) + for i in range(num_actors) + ] + log(INFO, "Flower VCE: Creating ActorPool with %s actors", len(actors)) + pool = VirtualClientEngineActorPool(actors) for cid in cids: diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 9ff29d092eeb..7f7bd9f90d93 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -1,24 +1,42 @@ +import traceback from typing import Any, Callable import ray from ray.util.actor_pool import ActorPool + +class ClientException(Exception): + """Raised when client side logic crashes with an exception.""" + + def __init__(self, message): + self.message = f"\n{'>'*7} A ClientException occurred." + message + super().__init__(self.message) + + @ray.remote class VirtualClientEngineActor: - def __init__(self, actor_id: int): - self.actor_id = actor_id - + def run(self, client_fn, client_id): # execute tasks and return result # return also cid which is needed to ensure results # from the pool are correctly assigned to each ClientProxy - return client_id, client_fn() + try: + client_results = client_fn() + except Exception as ex: + client_trace = traceback.format_exc() + message = ( + "\n\tSomething went wrong when running your client workload." + f"\n\tClient {client_id} crashed when the {self.__class__.__name__} was running its workload." + f" \n\tThis is the exception triggered on the client side: {client_trace}" + ) + raise ClientException(message) + return client_id, client_results -class VirtualClientEngineActorPool(ActorPool): +class VirtualClientEngineActorPool(ActorPool): def __init__(self, actors: list): super().__init__(actors) @@ -31,24 +49,28 @@ def submit_client_job(self, fn: Any, value: Callable, cid: int): # print(f"Submitted to pool from VirtualClient {cid}") return super().submit(fn, value) - def get_client_result(self, cid: int, timeout: int=3600): - + def get_client_result(self, cid: int, timeout: int = 3600): while self._results[cid] is None: # we need a try/except because if all VirtualClients are pinging the queue for the first # result, only one of them will be able to "fetch" it. Leaving the rest trying to fetch # the object following a reference that doesn't exist anymore. try: - res_cid, res = self.get_next_unordered() + res_cid, res = self.get_next_unordered(timeout=timeout) # Track in dictionary # print(f"Adding result to dict for cid: {res_cid}") self._results[res_cid] = res - except: + except KeyError as ex: + # result was already fetched, that's fine, continue continue # print(f"VirtualClient with cid: {cid} still needs to wait for its result...") # ready = not(self._results[cid] is None) # print(f"is result for {cid} read?: {ready}") - # print(f"Returning result for client {cid}") + # print(f"Returning result for client {cid}, is it none: {self._results[cid] is None}") + if self._results[cid] is None: + raise RuntimeError( + f"Client {cid} failed, no result is available in the VirtualClientEngine." + ) + return self._results.pop(cid) - \ No newline at end of file diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index c7384689e52d..f944662bf92c 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -16,8 +16,8 @@ from logging import ERROR -from typing import Callable, Dict, Optional, cast from time import sleep +from typing import Callable, Dict, Optional, cast import ray @@ -120,7 +120,9 @@ def reconnect( class RayActorClientProxy(ClientProxy): """Flower client proxy which delegates work using Ray.""" - def __init__(self, client_fn: ClientFn, cid: str, actor_pool: VirtualClientEngineActorPool): + def __init__( + self, client_fn: ClientFn, cid: str, actor_pool: VirtualClientEngineActorPool + ): super().__init__(cid) self.client_fn = client_fn self.actor_pool = actor_pool @@ -132,11 +134,15 @@ def get_properties( def get_properties(): client: Client = _create_client(self.client_fn, self.cid) - return maybe_call_get_properties(client=client, - get_properties_ins=ins, - ) + return maybe_call_get_properties( + client=client, + get_properties_ins=ins, + ) + try: - self.actor_pool.submit_client_job(lambda a, v : a.run.remote(v, self.cid), get_properties, self.cid) + self.actor_pool.submit_client_job( + lambda a, v: a.run.remote(v, self.cid), get_properties, self.cid + ) res = self.actor_pool.get_client_result(self.cid) except Exception as ex: @@ -154,11 +160,15 @@ def get_parameters( def get_parameters(): client: Client = _create_client(self.client_fn, self.cid) - return maybe_call_get_parameters(client=client, - get_parameters_ins=ins, - ) + return maybe_call_get_parameters( + client=client, + get_parameters_ins=ins, + ) + try: - self.actor_pool.submit_client_job(lambda a, v : a.run.remote(v, self.cid), get_parameters, self.cid) + self.actor_pool.submit_client_job( + lambda a, v: a.run.remote(v, self.cid), get_parameters, self.cid + ) res = self.actor_pool.get_client_result(self.cid) except Exception as ex: @@ -171,13 +181,18 @@ def get_parameters(): def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes: """Train model parameters on the locally held dataset.""" + def fit(): client: Client = _create_client(self.client_fn, self.cid) - return maybe_call_fit(client=client, - fit_ins=ins, - ) + return maybe_call_fit( + client=client, + fit_ins=ins, + ) + try: - self.actor_pool.submit_client_job(lambda a, v : a.run.remote(v, self.cid), fit, self.cid) + self.actor_pool.submit_client_job( + lambda a, v: a.run.remote(v, self.cid), fit, self.cid + ) res = self.actor_pool.get_client_result(self.cid) except Exception as ex: @@ -192,13 +207,18 @@ def evaluate( self, ins: common.EvaluateIns, timeout: Optional[float] ) -> common.EvaluateRes: """Evaluate model parameters on the locally held dataset.""" + def evaluate(): client: Client = _create_client(self.client_fn, self.cid) - return maybe_call_evaluate(client=client, - evaluate_ins=ins, - ) + return maybe_call_evaluate( + client=client, + evaluate_ins=ins, + ) + try: - self.actor_pool.submit_client_job(lambda a, v : a.run.remote(v, self.cid), evaluate, self.cid) + self.actor_pool.submit_client_job( + lambda a, v: a.run.remote(v, self.cid), evaluate, self.cid + ) res = self.actor_pool.get_client_result(self.cid) except Exception as ex: From 248cba5a856578c5de75ebb3ec33fb7aa0abbb96 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Tue, 11 Jul 2023 15:02:00 +0100 Subject: [PATCH 006/133] formatting --- src/py/flwr/simulation/app.py | 5 +- .../simulation/ray_transport/ray_actor.py | 46 +++++++++++++------ .../ray_transport/ray_client_proxy.py | 1 - 3 files changed, 34 insertions(+), 18 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 2c027647f4dd..a6fff530d37c 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -33,10 +33,7 @@ VirtualClientEngineActor, VirtualClientEngineActorPool, ) -from flwr.simulation.ray_transport.ray_client_proxy import ( - RayActorClientProxy, - RayClientProxy, -) +from flwr.simulation.ray_transport.ray_client_proxy import RayActorClientProxy INVALID_ARGUMENTS_START_SIMULATION = """ INVALID ARGUMENTS ERROR diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 7f7bd9f90d93..e0e353648f30 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -1,3 +1,19 @@ +# Copyright 2020 Adap GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Ray-based Flower Actor and ActorPool implementation.""" + import traceback from typing import Any, Callable @@ -15,10 +31,13 @@ def __init__(self, message): @ray.remote class VirtualClientEngineActor: + """A Ray Actor class that runs client workloads.""" + def __init__(self, actor_id: int): self.actor_id = actor_id def run(self, client_fn, client_id): + """Run a client workload.""" # execute tasks and return result # return also cid which is needed to ensure results # from the pool are correctly assigned to each ClientProxy @@ -28,15 +47,18 @@ def run(self, client_fn, client_id): client_trace = traceback.format_exc() message = ( "\n\tSomething went wrong when running your client workload." - f"\n\tClient {client_id} crashed when the {self.__class__.__name__} was running its workload." - f" \n\tThis is the exception triggered on the client side: {client_trace}" + f"\n\tClient {client_id} crashed when the {self.__class__.__name__}" + " was running its workload." + f"\n\tException triggered on the client side: {client_trace}" ) - raise ClientException(message) + raise ClientException(message) from ex return client_id, client_results class VirtualClientEngineActorPool(ActorPool): + """A pool of VirtualClientEngine Actors.""" + def __init__(self, actors: list): super().__init__(actors) @@ -45,32 +67,30 @@ def __init__(self, actors: list): self._results = {} def submit_client_job(self, fn: Any, value: Callable, cid: int): + """Submit a job to the pool.""" self._results[cid] = None # print(f"Submitted to pool from VirtualClient {cid}") return super().submit(fn, value) def get_client_result(self, cid: int, timeout: int = 3600): + """Fetch the result submitted by the specified VirtualClient.""" while self._results[cid] is None: - # we need a try/except because if all VirtualClients are pinging the queue for the first - # result, only one of them will be able to "fetch" it. Leaving the rest trying to fetch - # the object following a reference that doesn't exist anymore. + # we need a try/except because if all VirtualClients are pinging the queue + # for the first result, only one of them will be able to "fetch" it. + # Leaving the rest trying to fetch the object following a reference that + # doesn't exist anymore. try: res_cid, res = self.get_next_unordered(timeout=timeout) # Track in dictionary # print(f"Adding result to dict for cid: {res_cid}") self._results[res_cid] = res - except KeyError as ex: + except KeyError: # result was already fetched, that's fine, continue continue - # print(f"VirtualClient with cid: {cid} still needs to wait for its result...") - # ready = not(self._results[cid] is None) - # print(f"is result for {cid} read?: {ready}") - - # print(f"Returning result for client {cid}, is it none: {self._results[cid] is None}") if self._results[cid] is None: raise RuntimeError( - f"Client {cid} failed, no result is available in the VirtualClientEngine." + f"No result is available in the VirtualClientEngine for client {cid}" ) return self._results.pop(cid) diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index f944662bf92c..38ec704f07f3 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -16,7 +16,6 @@ from logging import ERROR -from time import sleep from typing import Callable, Dict, Optional, cast import ray From 5ff115a51af3baf9c636e292f78992bf60e1c8a4 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Wed, 12 Jul 2023 23:00:26 +0100 Subject: [PATCH 007/133] client failure OK; actor restarted if it fails; after N fails actor is removed from pool --- src/py/flwr/simulation/app.py | 8 +- .../simulation/ray_transport/ray_actor.py | 175 ++++++++++++++---- .../ray_transport/ray_client_proxy.py | 29 ++- 3 files changed, 171 insertions(+), 41 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index a6fff530d37c..4d180b8c5af5 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -147,7 +147,6 @@ def start_simulation( # pylint: disable=too-many-arguments client_manager=client_manager, ) # Setting simulation ON for server - initialized_server.is_simulation = True log( INFO, "Starting Flower simulation, config: %s", @@ -214,8 +213,13 @@ def start_simulation( # pylint: disable=too-many-arguments num_actors = min(num_actors, int(num_gpus / resources["num_gpus"])) # instantiate ActorPool + # TODO: maybe we want `max_restarts` to be user-defined ? + max_restarts = 1 # how many times an actor that crashes should be restarted + # after these many restarts, it will be removed from the pool actors = [ - VirtualClientEngineActor.options(**resources).remote(i) + VirtualClientEngineActor.options(**resources, max_restarts=max_restarts).remote( + i + ) for i in range(num_actors) ] log(INFO, "Flower VCE: Creating ActorPool with %s actors", len(actors)) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index e0e353648f30..294fec761881 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -14,8 +14,9 @@ # ============================================================================== """Ray-based Flower Actor and ActorPool implementation.""" +import threading import traceback -from typing import Any, Callable +from typing import Any, Callable, List, Set import ray from ray.util.actor_pool import ActorPool @@ -24,7 +25,7 @@ class ClientException(Exception): """Raised when client side logic crashes with an exception.""" - def __init__(self, message): + def __init__(self, message: str): self.message = f"\n{'>'*7} A ClientException occurred." + message super().__init__(self.message) @@ -36,7 +37,7 @@ class VirtualClientEngineActor: def __init__(self, actor_id: int): self.actor_id = actor_id - def run(self, client_fn, client_id): + def run(self, client_fn: Callable, client_id): """Run a client workload.""" # execute tasks and return result # return also cid which is needed to ensure results @@ -59,38 +60,144 @@ def run(self, client_fn, client_id): class VirtualClientEngineActorPool(ActorPool): """A pool of VirtualClientEngine Actors.""" - def __init__(self, actors: list): + def __init__(self, actors: List[VirtualClientEngineActor]): super().__init__(actors) - # stores completed job while keeping track to - # which VirtualClient it belongs to - self._results = {} - - def submit_client_job(self, fn: Any, value: Callable, cid: int): - """Submit a job to the pool.""" - self._results[cid] = None - # print(f"Submitted to pool from VirtualClient {cid}") - return super().submit(fn, value) - - def get_client_result(self, cid: int, timeout: int = 3600): - """Fetch the result submitted by the specified VirtualClient.""" - while self._results[cid] is None: - # we need a try/except because if all VirtualClients are pinging the queue - # for the first result, only one of them will be able to "fetch" it. - # Leaving the rest trying to fetch the object following a reference that - # doesn't exist anymore. + self._cid_to_future = {} # a dict + self.actor_to_remove: Set[str] = set() # a set + + self.lock = threading.RLock() + + def __reduce__(self): + """Make this class serialisable (needed due to lock).""" + return VirtualClientEngineActorPool, (self._idle_actors,) + + def submit(self, fn: Any, value: Callable, cid: str) -> None: + """Take idle actor and assign it a client workload.""" + actor = self._idle_actors.pop() + if self._check_and_remove_actor_from_pool(actor): + future = fn(actor, value) + future_key = tuple(future) if isinstance(future, List) else future + self._future_to_actor[future_key] = (self._next_task_index, actor, cid) + self._next_task_index += 1 + + # creating cid to future mapping + self._reset_cid_to_future_dict(cid) + self._cid_to_future[cid]["future"] = future_key + + def submit_client_job(self, fn: Any, value: Callable, cid: str) -> None: + """Submit a job while tracking client ids.""" + # We need to put this behind a lock since .submit() involves + # removing and adding elements from a dictionary. Which creates + # issues in multi-threaded settings + with self.lock: + if self._idle_actors: + # submit job since there is an Actor that's available + self.submit(fn, value, cid) + else: + # no actors are available, append to list of jobs to run later + self._pending_submits.append((fn, value, cid)) + + def _flag_future_as_ready(self, cid) -> None: + """Flag future for VirtualClient as ready.""" + self._cid_to_future[cid]["ready"] = True + + def _reset_cid_to_future_dict(self, cid: str) -> None: + """Reset cid:future mapping info.""" + if cid not in self._cid_to_future.keys(): + self._cid_to_future[cid] = {} + + self._cid_to_future[cid]["future"] = None + self._cid_to_future[cid]["ready"] = False + + def _is_future_ready(self, cid: str) -> bool: + """Return status of future for this VirtualClient.""" + if cid not in self._cid_to_future.keys(): + return False + else: + return self._cid_to_future[cid]["ready"] + + def _fetch_future_result(self, cid: str) -> Any: + """Fetch result for VirtualClient from Object Store.""" + res_cid, res = ray.get(self._cid_to_future[cid]["future"]) + + # sanity check: was the result fetched generated by a client with cid=cid? + assert ( + res_cid != res + ), f"The VirtualClient {cid} got result from client {res_cid}" + + # reset mapping + self._reset_cid_to_future_dict(cid) + + return res + + def flag_actor_for_removal(self, actor_id_hex: str) -> None: + """Flag actor that should be removed from pool.""" + with self.lock: + self.actor_to_remove.add(actor_id_hex) + print(f"Actor({actor_id_hex}) will be remove from pool.") + + def _check_and_remove_actor_from_pool( + self, actor: VirtualClientEngineActor + ) -> bool: + """Check if actor in set of those that should be removed. + + Remove the actor if so. + """ + with self.lock: + actor_id = actor._actor_id.hex() + # print(f"{self.actor_to_remove = }") + if actor_id in self.actor_to_remove: + # the actor should be removed + print(f"REMOVED actor {actor_id} from pool") + self.actor_to_remove.remove(actor_id) + return False + else: + # print(f"actor: {actor_id} should not be killed") + return True + + def process_unordered_future(self, timeout=None, ignore_if_timedout=False) -> None: + """Similar to parent's get_next_unordered() but without final ray.get().""" + if not self.has_next(): + raise StopIteration("No more results to get") + res, _ = ray.wait(list(self._future_to_actor), num_returns=1, timeout=timeout) + timeout_msg = "Timed out waiting for result" + raise_timeout_after_ignore = False + if res: + [future] = res + else: + if not ignore_if_timedout: + raise TimeoutError(timeout_msg) + else: + raise_timeout_after_ignore = True + + # it is highly likely that all VirtuaLClientEngine instances were waiting for + # the first result to be avaialbe, but only one VCE can do .pop() and fetch the + # actor we put this behind a lock since we are removing and adding elements to + # dictionaries note that ._return_actor will run .submit() internally + with self.lock: + _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) + if a is not None: + # this thread did .pop() --> flag actor as available and submit new job + if self._check_and_remove_actor_from_pool(a): + self._return_actor(a) + # flag future as ready + self._flag_future_as_ready(cid) + # print(self._cid_to_future[cid]) + + if raise_timeout_after_ignore: + raise TimeoutError(timeout_msg + f". The task {future} has been ignored.") + + def get_client_result(self, cid: str, timeout: int = 3600) -> Any: + """Get result from VirtualClient with specific cid.""" + # loop until all jobs submitted to the pool are completed. Break early + # if the result for the ClientProxy running this method is ready + while self.has_next() and not (self._is_future_ready(cid)): try: - res_cid, res = self.get_next_unordered(timeout=timeout) - # Track in dictionary - # print(f"Adding result to dict for cid: {res_cid}") - self._results[res_cid] = res - except KeyError: - # result was already fetched, that's fine, continue - continue - - if self._results[cid] is None: - raise RuntimeError( - f"No result is available in the VirtualClientEngine for client {cid}" - ) + self.process_unordered_future(timeout=timeout) + except StopIteration: + # there are no pending jobs in the pool + break - return self._results.pop(cid) + # Fetch result belonging to the VirtualClient calling this method + return self._fetch_future_result(cid) diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index 38ec704f07f3..b9c835381130 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -15,7 +15,7 @@ """Ray-based Flower ClientProxy implementation.""" -from logging import ERROR +from logging import ERROR, WARNING from typing import Callable, Dict, Optional, cast import ray @@ -131,7 +131,7 @@ def get_properties( ) -> common.GetPropertiesRes: """Return client's properties.""" - def get_properties(): + def get_properties() -> common.GetPropertiesRes: client: Client = _create_client(self.client_fn, self.cid) return maybe_call_get_properties( client=client, @@ -144,6 +144,10 @@ def get_properties(): ) res = self.actor_pool.get_client_result(self.cid) + except ray.exceptions.RayActorError as ex: + log(WARNING, ex) + if hasattr(ex, "actor_id"): + self.actor_pool.flag_actor_for_removal(ex.actor_id) except Exception as ex: log(ERROR, ex) raise ex @@ -157,7 +161,7 @@ def get_parameters( ) -> common.GetParametersRes: """Return the current local model parameters.""" - def get_parameters(): + def get_parameters() -> common.GetParametersRes: client: Client = _create_client(self.client_fn, self.cid) return maybe_call_get_parameters( client=client, @@ -170,6 +174,11 @@ def get_parameters(): ) res = self.actor_pool.get_client_result(self.cid) + except ray.exceptions.RayActorError as ex: + log(WARNING, ex) + if hasattr(ex, "actor_id"): + self.actor_pool.flag_actor_for_removal(ex.actor_id) + except Exception as ex: log(ERROR, ex) raise ex @@ -181,7 +190,7 @@ def get_parameters(): def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes: """Train model parameters on the locally held dataset.""" - def fit(): + def fit() -> common.FitRes: client: Client = _create_client(self.client_fn, self.cid) return maybe_call_fit( client=client, @@ -194,6 +203,11 @@ def fit(): ) res = self.actor_pool.get_client_result(self.cid) + except ray.exceptions.RayActorError as ex: + log(WARNING, ex) + if hasattr(ex, "actor_id"): + self.actor_pool.flag_actor_for_removal(ex.actor_id) + except Exception as ex: log(ERROR, ex) raise ex @@ -207,7 +221,7 @@ def evaluate( ) -> common.EvaluateRes: """Evaluate model parameters on the locally held dataset.""" - def evaluate(): + def evaluate() -> common.EvaluateRes: client: Client = _create_client(self.client_fn, self.cid) return maybe_call_evaluate( client=client, @@ -220,6 +234,11 @@ def evaluate(): ) res = self.actor_pool.get_client_result(self.cid) + except ray.exceptions.RayActorError as ex: + log(WARNING, ex) + if hasattr(ex, "actor_id"): + self.actor_pool.flag_actor_for_removal(ex.actor_id) + except Exception as ex: log(ERROR, ex) raise ex From e15eb452bab3a9181248bae6851496dc57ce6004 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Thu, 13 Jul 2023 22:09:55 +0100 Subject: [PATCH 008/133] fix --- src/py/flwr/simulation/ray_transport/ray_actor.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 294fec761881..4efbabc2f2b5 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -81,8 +81,7 @@ def submit(self, fn: Any, value: Callable, cid: str) -> None: self._future_to_actor[future_key] = (self._next_task_index, actor, cid) self._next_task_index += 1 - # creating cid to future mapping - self._reset_cid_to_future_dict(cid) + # update with future self._cid_to_future[cid]["future"] = future_key def submit_client_job(self, fn: Any, value: Callable, cid: str) -> None: @@ -91,6 +90,8 @@ def submit_client_job(self, fn: Any, value: Callable, cid: str) -> None: # removing and adding elements from a dictionary. Which creates # issues in multi-threaded settings with self.lock: + # creating cid to future mapping + self._reset_cid_to_future_dict(cid) if self._idle_actors: # submit job since there is an Actor that's available self.submit(fn, value, cid) From 72a7bde878f43344ea9fffe929e532dfca088daf Mon Sep 17 00:00:00 2001 From: jafermarq Date: Fri, 14 Jul 2023 18:44:06 +0100 Subject: [PATCH 009/133] tolerant to full node disconnect --- src/py/flwr/simulation/app.py | 35 ++++++--- .../simulation/ray_transport/ray_actor.py | 62 ++++++++++++---- .../ray_transport/ray_client_proxy.py | 72 ++++++------------- 3 files changed, 97 insertions(+), 72 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 4d180b8c5af5..7827ea0806f5 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -203,14 +203,27 @@ def start_simulation( # pylint: disable=too-many-arguments # determine how many actors can be added to the pool. # this a function of the total resources visible to Ray and # the resources allocated for each virtual client - num_cpus = cluster_resources["CPU"] - num_gpus = cluster_resources.get("GPU", 0) # there might not be GPU + # TODO: this function needs a better name + def pool_actor_size(): + """Calculate number of Actors that fit in pool given the resources in the. - num_actors = int(num_cpus / resources["num_cpus"]) + cluster and those required per client. + """ + cluster_resources = ray.cluster_resources() + num_cpus = cluster_resources["CPU"] + num_gpus = cluster_resources.get("GPU", 0) # there might not be GPU + num_actors = int(num_cpus / resources["num_cpus"]) + # if a GPU is present and client resources do require one + if resources["num_gpus"] > 0.0: + if num_gpus: + # if there are gpus in the cluster + num_actors = min(num_actors, int(num_gpus / resources["num_gpus"])) + else: + num_actors = 0 - # if a GPU is present and client resources do require one - if num_gpus and resources["num_gpus"] > 0.0: - num_actors = min(num_actors, int(num_gpus / resources["num_gpus"])) + return num_actors + + num_actors = pool_actor_size() # instantiate ActorPool # TODO: maybe we want `max_restarts` to be user-defined ? @@ -222,9 +235,15 @@ def start_simulation( # pylint: disable=too-many-arguments ) for i in range(num_actors) ] - log(INFO, "Flower VCE: Creating ActorPool with %s actors", len(actors)) + log( + INFO, + "Flower VCE: Creating %s with %s %s", + VirtualClientEngineActorPool.__name__, + len(actors), + VirtualClientEngineActor.__class__.__name__, + ) - pool = VirtualClientEngineActorPool(actors) + pool = VirtualClientEngineActorPool(actors, pool_size_fn=pool_actor_size) for cid in cids: client_proxy = RayActorClientProxy( diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 4efbabc2f2b5..428cd9ef8668 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -16,11 +16,14 @@ import threading import traceback +from logging import ERROR, WARNING from typing import Any, Callable, List, Set import ray from ray.util.actor_pool import ActorPool +from flwr.common.logger import log + class ClientException(Exception): """Raised when client side logic crashes with an exception.""" @@ -37,6 +40,11 @@ class VirtualClientEngineActor: def __init__(self, actor_id: int): self.actor_id = actor_id + def terminate(self): + """Terminate Actor.""" + log(WARNING, f"Manually terminating {self.__class__.__name__}") + ray.actor.exit_actor() + def run(self, client_fn: Callable, client_id): """Run a client workload.""" # execute tasks and return result @@ -60,17 +68,19 @@ def run(self, client_fn: Callable, client_id): class VirtualClientEngineActorPool(ActorPool): """A pool of VirtualClientEngine Actors.""" - def __init__(self, actors: List[VirtualClientEngineActor]): + def __init__(self, actors: List[VirtualClientEngineActor], pool_size_fn: Callable): super().__init__(actors) self._cid_to_future = {} # a dict self.actor_to_remove: Set[str] = set() # a set + self.pool_size = pool_size_fn + self.num_actors = self.pool_size() self.lock = threading.RLock() def __reduce__(self): """Make this class serialisable (needed due to lock).""" - return VirtualClientEngineActorPool, (self._idle_actors,) + return VirtualClientEngineActorPool, (self._idle_actors, self.pool_size) def submit(self, fn: Any, value: Callable, cid: str) -> None: """Take idle actor and assign it a client workload.""" @@ -123,9 +133,9 @@ def _fetch_future_result(self, cid: str) -> Any: res_cid, res = ray.get(self._cid_to_future[cid]["future"]) # sanity check: was the result fetched generated by a client with cid=cid? - assert ( - res_cid != res - ), f"The VirtualClient {cid} got result from client {res_cid}" + assert res_cid != res, log( + ERROR, f"The VirtualClient {cid} got result from client {res_cid}" + ) # reset mapping self._reset_cid_to_future_dict(cid) @@ -136,7 +146,7 @@ def flag_actor_for_removal(self, actor_id_hex: str) -> None: """Flag actor that should be removed from pool.""" with self.lock: self.actor_to_remove.add(actor_id_hex) - print(f"Actor({actor_id_hex}) will be remove from pool.") + log(WARNING, f"Actor({actor_id_hex}) will be remove from pool.") def _check_and_remove_actor_from_pool( self, actor: VirtualClientEngineActor @@ -150,13 +160,36 @@ def _check_and_remove_actor_from_pool( # print(f"{self.actor_to_remove = }") if actor_id in self.actor_to_remove: # the actor should be removed - print(f"REMOVED actor {actor_id} from pool") + log(WARNING, f"REMOVED actor {actor_id} from pool") self.actor_to_remove.remove(actor_id) return False else: # print(f"actor: {actor_id} should not be killed") return True + def _check_actor_fits_in_pool(self) -> bool: + """Determine if available resources are haven't changed. + + If true, allow the actor to be added back to the pool. Else don't allow it + (effectively reducing the size of the pool). + """ + num_actors_updated = self.pool_size() + + if num_actors_updated < self.num_actors: + log( + WARNING, + "Cluster resources have changed. Number of actors in the pool should be" + "reduced from {self.num_actors} down to {num_actors_updated} -- This" + "might take several intermediate steps", + ) + # we are preventing one actor to be added back in the queue, so we just + # decreated the number of actors by one eventually `self.num_actors` + # should be equal what self.pool_size() returns + self.num_actors -= 1 + return False + else: + return True + def process_unordered_future(self, timeout=None, ignore_if_timedout=False) -> None: """Similar to parent's get_next_unordered() but without final ray.get().""" if not self.has_next(): @@ -179,12 +212,15 @@ def process_unordered_future(self, timeout=None, ignore_if_timedout=False) -> No with self.lock: _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) if a is not None: - # this thread did .pop() --> flag actor as available and submit new job - if self._check_and_remove_actor_from_pool(a): - self._return_actor(a) - # flag future as ready - self._flag_future_as_ready(cid) - # print(self._cid_to_future[cid]) + # still space in queue ? (no if a node died) + if self._check_actor_fits_in_pool(): + if self._check_and_remove_actor_from_pool(a): + self._return_actor(a) + # flag future as ready + self._flag_future_as_ready(cid) + # print(self._cid_to_future[cid]) + else: + a.terminate.remote() if raise_timeout_after_ignore: raise TimeoutError(timeout_msg + f". The task {future} has been ignored.") diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index b9c835381130..f32ce2d716d7 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -126,6 +126,23 @@ def __init__( self.client_fn = client_fn self.actor_pool = actor_pool + def _submit_job(self, job: Callable): + try: + self.actor_pool.submit_client_job( + lambda a, v: a.run.remote(v, self.cid), job, self.cid + ) + res = self.actor_pool.get_client_result(self.cid) + + except ray.exceptions.RayActorError as ex: + log(WARNING, ex) + if hasattr(ex, "actor_id"): + self.actor_pool.flag_actor_for_removal(ex.actor_id) + except Exception as ex: + log(ERROR, ex) + raise ex + + return res + def get_properties( self, ins: common.GetPropertiesIns, timeout: Optional[float] ) -> common.GetPropertiesRes: @@ -138,19 +155,8 @@ def get_properties() -> common.GetPropertiesRes: get_properties_ins=ins, ) - try: - self.actor_pool.submit_client_job( - lambda a, v: a.run.remote(v, self.cid), get_properties, self.cid - ) - res = self.actor_pool.get_client_result(self.cid) + res = self._submit_job(get_properties) - except ray.exceptions.RayActorError as ex: - log(WARNING, ex) - if hasattr(ex, "actor_id"): - self.actor_pool.flag_actor_for_removal(ex.actor_id) - except Exception as ex: - log(ERROR, ex) - raise ex return cast( common.GetPropertiesRes, res, @@ -168,20 +174,8 @@ def get_parameters() -> common.GetParametersRes: get_parameters_ins=ins, ) - try: - self.actor_pool.submit_client_job( - lambda a, v: a.run.remote(v, self.cid), get_parameters, self.cid - ) - res = self.actor_pool.get_client_result(self.cid) + res = self._submit_job(get_parameters) - except ray.exceptions.RayActorError as ex: - log(WARNING, ex) - if hasattr(ex, "actor_id"): - self.actor_pool.flag_actor_for_removal(ex.actor_id) - - except Exception as ex: - log(ERROR, ex) - raise ex return cast( common.GetParametersRes, res, @@ -197,20 +191,8 @@ def fit() -> common.FitRes: fit_ins=ins, ) - try: - self.actor_pool.submit_client_job( - lambda a, v: a.run.remote(v, self.cid), fit, self.cid - ) - res = self.actor_pool.get_client_result(self.cid) - - except ray.exceptions.RayActorError as ex: - log(WARNING, ex) - if hasattr(ex, "actor_id"): - self.actor_pool.flag_actor_for_removal(ex.actor_id) + res = self._submit_job(fit) - except Exception as ex: - log(ERROR, ex) - raise ex return cast( common.FitRes, res, @@ -228,20 +210,8 @@ def evaluate() -> common.EvaluateRes: evaluate_ins=ins, ) - try: - self.actor_pool.submit_client_job( - lambda a, v: a.run.remote(v, self.cid), evaluate, self.cid - ) - res = self.actor_pool.get_client_result(self.cid) + res = self._submit_job(evaluate) - except ray.exceptions.RayActorError as ex: - log(WARNING, ex) - if hasattr(ex, "actor_id"): - self.actor_pool.flag_actor_for_removal(ex.actor_id) - - except Exception as ex: - log(ERROR, ex) - raise ex return cast( common.EvaluateRes, res, From 45b4021c4f6e1ad84e38b5cfa8c1d123dd9caf2e Mon Sep 17 00:00:00 2001 From: jafermarq Date: Sat, 15 Jul 2023 13:54:38 +0100 Subject: [PATCH 010/133] better --- src/py/flwr/simulation/ray_transport/ray_actor.py | 14 ++++++++++---- .../simulation/ray_transport/ray_client_proxy.py | 10 ++++++---- 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 428cd9ef8668..18a18443db8c 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -74,7 +74,7 @@ def __init__(self, actors: List[VirtualClientEngineActor], pool_size_fn: Callabl self._cid_to_future = {} # a dict self.actor_to_remove: Set[str] = set() # a set self.pool_size = pool_size_fn - self.num_actors = self.pool_size() + self.num_actors = len(actors) self.lock = threading.RLock() @@ -130,7 +130,11 @@ def _is_future_ready(self, cid: str) -> bool: def _fetch_future_result(self, cid: str) -> Any: """Fetch result for VirtualClient from Object Store.""" - res_cid, res = ray.get(self._cid_to_future[cid]["future"]) + try: + res_cid, res = ray.get(self._cid_to_future[cid]["future"]) + except ray.exceptions.RayActorError as ex: + log(ERROR, ex) + self._flag_actor_for_removal(ex.actor_id) # sanity check: was the result fetched generated by a client with cid=cid? assert res_cid != res, log( @@ -142,7 +146,7 @@ def _fetch_future_result(self, cid: str) -> Any: return res - def flag_actor_for_removal(self, actor_id_hex: str) -> None: + def _flag_actor_for_removal(self, actor_id_hex: str) -> None: """Flag actor that should be removed from pool.""" with self.lock: self.actor_to_remove.add(actor_id_hex) @@ -160,8 +164,10 @@ def _check_and_remove_actor_from_pool( # print(f"{self.actor_to_remove = }") if actor_id in self.actor_to_remove: # the actor should be removed - log(WARNING, f"REMOVED actor {actor_id} from pool") self.actor_to_remove.remove(actor_id) + self.num_actors -= 1 + log(WARNING, f"REMOVED actor {actor_id} from pool") + log(WARNING, f"Pool size: {self.num_actors}") return False else: # print(f"actor: {actor_id} should not be killed") diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index f32ce2d716d7..909d3e12dabb 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -133,11 +133,13 @@ def _submit_job(self, job: Callable): ) res = self.actor_pool.get_client_result(self.cid) - except ray.exceptions.RayActorError as ex: - log(WARNING, ex) - if hasattr(ex, "actor_id"): - self.actor_pool.flag_actor_for_removal(ex.actor_id) except Exception as ex: + if self.actor_pool.num_actors == 0: + # At this point we want to stop the simulation. + # since no more client workloads will be executed + log(ERROR, "ActorPool is empty!!! Disconnecting VirtualClient") + # TODO: the below does nothing? + self.reconnect() log(ERROR, ex) raise ex From 94564700f4d0d4eac571864517edfee1a61f2ffd Mon Sep 17 00:00:00 2001 From: jafermarq Date: Sat, 15 Jul 2023 14:14:36 +0100 Subject: [PATCH 011/133] consolidate actorpool and utilities --- src/py/flwr/simulation/app.py | 47 +++---------------- .../simulation/ray_transport/ray_actor.py | 47 +++++++++++++++---- .../ray_transport/ray_client_proxy.py | 2 +- 3 files changed, 46 insertions(+), 50 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 7827ea0806f5..0083f0ebfdfe 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -29,10 +29,7 @@ from flwr.server.client_manager import ClientManager from flwr.server.history import History from flwr.server.strategy import Strategy -from flwr.simulation.ray_transport.ray_actor import ( - VirtualClientEngineActor, - VirtualClientEngineActorPool, -) +from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActorPool from flwr.simulation.ray_transport.ray_client_proxy import RayActorClientProxy INVALID_ARGUMENTS_START_SIMULATION = """ @@ -200,51 +197,21 @@ def start_simulation( # pylint: disable=too-many-arguments resources, ) - # determine how many actors can be added to the pool. - # this a function of the total resources visible to Ray and - # the resources allocated for each virtual client - # TODO: this function needs a better name - def pool_actor_size(): - """Calculate number of Actors that fit in pool given the resources in the. - - cluster and those required per client. - """ - cluster_resources = ray.cluster_resources() - num_cpus = cluster_resources["CPU"] - num_gpus = cluster_resources.get("GPU", 0) # there might not be GPU - num_actors = int(num_cpus / resources["num_cpus"]) - # if a GPU is present and client resources do require one - if resources["num_gpus"] > 0.0: - if num_gpus: - # if there are gpus in the cluster - num_actors = min(num_actors, int(num_gpus / resources["num_gpus"])) - else: - num_actors = 0 - - return num_actors - - num_actors = pool_actor_size() - # instantiate ActorPool # TODO: maybe we want `max_restarts` to be user-defined ? max_restarts = 1 # how many times an actor that crashes should be restarted # after these many restarts, it will be removed from the pool - actors = [ - VirtualClientEngineActor.options(**resources, max_restarts=max_restarts).remote( - i - ) - for i in range(num_actors) - ] + + pool = VirtualClientEngineActorPool(resources, max_restarts) + log( INFO, "Flower VCE: Creating %s with %s %s", - VirtualClientEngineActorPool.__name__, - len(actors), - VirtualClientEngineActor.__class__.__name__, + pool.__class__.__name__, + pool.num_actors, + pool._idle_actors[0].__class__.__name__, ) - pool = VirtualClientEngineActorPool(actors, pool_size_fn=pool_actor_size) - for cid in cids: client_proxy = RayActorClientProxy( client_fn=client_fn, diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 18a18443db8c..74e71c0a019e 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -17,7 +17,7 @@ import threading import traceback from logging import ERROR, WARNING -from typing import Any, Callable, List, Set +from typing import Any, Callable, Dict, List, Set import ray from ray.util.actor_pool import ActorPool @@ -37,9 +37,6 @@ def __init__(self, message: str): class VirtualClientEngineActor: """A Ray Actor class that runs client workloads.""" - def __init__(self, actor_id: int): - self.actor_id = actor_id - def terminate(self): """Terminate Actor.""" log(WARNING, f"Manually terminating {self.__class__.__name__}") @@ -65,22 +62,54 @@ def run(self, client_fn: Callable, client_id): return client_id, client_results +def pool_size_from_resources(client_resources: Dict): + """Calculate number of Actors that fit in pool given the resources in the. + + cluster and those required per client. + """ + cluster_resources = ray.cluster_resources() + num_cpus = cluster_resources["CPU"] + num_gpus = cluster_resources.get("GPU", 0) # there might not be GPU + num_actors = int(num_cpus / client_resources["num_cpus"]) + # if a GPU is present and client resources do require one + if client_resources["num_gpus"] > 0.0: + if num_gpus: + # if there are gpus in the cluster + num_actors = min(num_actors, int(num_gpus / client_resources["num_gpus"])) + else: + num_actors = 0 + + return num_actors + + class VirtualClientEngineActorPool(ActorPool): """A pool of VirtualClientEngine Actors.""" - def __init__(self, actors: List[VirtualClientEngineActor], pool_size_fn: Callable): + def __init__(self, client_resources: Dict, max_restarts: int = 1): + self.client_resources = client_resources + self.actor_max_restarts = max_restarts + num_actors = pool_size_from_resources(client_resources) + actors = [ + VirtualClientEngineActor.options( + **client_resources, max_restarts=max_restarts + ).remote() + for _ in range(num_actors) + ] + super().__init__(actors) self._cid_to_future = {} # a dict self.actor_to_remove: Set[str] = set() # a set - self.pool_size = pool_size_fn self.num_actors = len(actors) self.lock = threading.RLock() def __reduce__(self): """Make this class serialisable (needed due to lock).""" - return VirtualClientEngineActorPool, (self._idle_actors, self.pool_size) + return VirtualClientEngineActorPool, ( + self.client_resources, + self.actor_max_restarts, + ) def submit(self, fn: Any, value: Callable, cid: str) -> None: """Take idle actor and assign it a client workload.""" @@ -179,7 +208,7 @@ def _check_actor_fits_in_pool(self) -> bool: If true, allow the actor to be added back to the pool. Else don't allow it (effectively reducing the size of the pool). """ - num_actors_updated = self.pool_size() + num_actors_updated = pool_size_from_resources(self.client_resources) if num_actors_updated < self.num_actors: log( @@ -190,7 +219,7 @@ def _check_actor_fits_in_pool(self) -> bool: ) # we are preventing one actor to be added back in the queue, so we just # decreated the number of actors by one eventually `self.num_actors` - # should be equal what self.pool_size() returns + # should be equal what pool_size_from_resources(self.resources) returns self.num_actors -= 1 return False else: diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index 909d3e12dabb..40c45c57416a 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -15,7 +15,7 @@ """Ray-based Flower ClientProxy implementation.""" -from logging import ERROR, WARNING +from logging import ERROR from typing import Callable, Dict, Optional, cast import ray From ea967c4fc2242460f270fc2bbe957c9476611c4b Mon Sep 17 00:00:00 2001 From: jafermarq Date: Sat, 15 Jul 2023 14:44:46 +0100 Subject: [PATCH 012/133] better handling of exception when actor dies for good --- src/py/flwr/simulation/ray_transport/ray_actor.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 74e71c0a019e..756f92a7b56e 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -163,7 +163,12 @@ def _fetch_future_result(self, cid: str) -> Any: res_cid, res = ray.get(self._cid_to_future[cid]["future"]) except ray.exceptions.RayActorError as ex: log(ERROR, ex) - self._flag_actor_for_removal(ex.actor_id) + log(ERROR, traceback.format_exc()) + if hasattr(ex, 'actor_id'): + # RayActorError only contains the actor_id attribute + # if the actor won't be restarted again. + self._flag_actor_for_removal(ex.actor_id) + raise ex # sanity check: was the result fetched generated by a client with cid=cid? assert res_cid != res, log( @@ -214,8 +219,8 @@ def _check_actor_fits_in_pool(self) -> bool: log( WARNING, "Cluster resources have changed. Number of actors in the pool should be" - "reduced from {self.num_actors} down to {num_actors_updated} -- This" - "might take several intermediate steps", + f" reduced from {self.num_actors} down to {num_actors_updated}. This" + " might take several intermediate steps", ) # we are preventing one actor to be added back in the queue, so we just # decreated the number of actors by one eventually `self.num_actors` From 5386bffbe083f52fdb086984b2382deb713c7261 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Sat, 15 Jul 2023 15:12:53 +0100 Subject: [PATCH 013/133] w/ previous --- src/py/flwr/simulation/ray_transport/ray_actor.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 756f92a7b56e..b4e3d4be5f2c 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -104,6 +104,8 @@ def __init__(self, client_resources: Dict, max_restarts: int = 1): self.lock = threading.RLock() + # TODO: asyncio check every N seconds if cluster has grown --> add more actors to the pool if so + def __reduce__(self): """Make this class serialisable (needed due to lock).""" return VirtualClientEngineActorPool, ( @@ -164,8 +166,8 @@ def _fetch_future_result(self, cid: str) -> Any: except ray.exceptions.RayActorError as ex: log(ERROR, ex) log(ERROR, traceback.format_exc()) - if hasattr(ex, 'actor_id'): - # RayActorError only contains the actor_id attribute + if hasattr(ex, "actor_id"): + # RayActorError only contains the actor_id attribute # if the actor won't be restarted again. self._flag_actor_for_removal(ex.actor_id) raise ex From 591e667656f9872db0431ac99d93f321912f58fe Mon Sep 17 00:00:00 2001 From: jafermarq Date: Mon, 17 Jul 2023 09:13:37 +0100 Subject: [PATCH 014/133] minor changes --- .../simulation/ray_transport/ray_actor.py | 34 ++++++++----------- .../ray_transport/ray_client_proxy.py | 4 +-- 2 files changed, 17 insertions(+), 21 deletions(-) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index b4e3d4be5f2c..e7d881735a97 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -16,7 +16,7 @@ import threading import traceback -from logging import ERROR, WARNING +from logging import ERROR, WARNING, INFO from typing import Any, Callable, Dict, List, Set import ray @@ -225,7 +225,7 @@ def _check_actor_fits_in_pool(self) -> bool: " might take several intermediate steps", ) # we are preventing one actor to be added back in the queue, so we just - # decreated the number of actors by one eventually `self.num_actors` + # decrease the number of actors by one eventually `self.num_actors` # should be equal what pool_size_from_resources(self.resources) returns self.num_actors -= 1 return False @@ -247,22 +247,17 @@ def process_unordered_future(self, timeout=None, ignore_if_timedout=False) -> No else: raise_timeout_after_ignore = True - # it is highly likely that all VirtuaLClientEngine instances were waiting for - # the first result to be avaialbe, but only one VCE can do .pop() and fetch the - # actor we put this behind a lock since we are removing and adding elements to - # dictionaries note that ._return_actor will run .submit() internally - with self.lock: - _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) - if a is not None: - # still space in queue ? (no if a node died) - if self._check_actor_fits_in_pool(): - if self._check_and_remove_actor_from_pool(a): - self._return_actor(a) - # flag future as ready - self._flag_future_as_ready(cid) - # print(self._cid_to_future[cid]) - else: - a.terminate.remote() + _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) + if a is not None: + # still space in queue ? (no if a node died) + if self._check_actor_fits_in_pool(): + if self._check_and_remove_actor_from_pool(a): + self._return_actor(a) + # flag future as ready + self._flag_future_as_ready(cid) + # print(self._cid_to_future[cid]) + else: + a.terminate.remote() if raise_timeout_after_ignore: raise TimeoutError(timeout_msg + f". The task {future} has been ignored.") @@ -273,7 +268,8 @@ def get_client_result(self, cid: str, timeout: int = 3600) -> Any: # if the result for the ClientProxy running this method is ready while self.has_next() and not (self._is_future_ready(cid)): try: - self.process_unordered_future(timeout=timeout) + with self.lock: + self.process_unordered_future(timeout=timeout) except StopIteration: # there are no pending jobs in the pool break diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index 40c45c57416a..f7c55d0ca8f7 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -14,7 +14,7 @@ # ============================================================================== """Ray-based Flower ClientProxy implementation.""" - +import traceback from logging import ERROR from typing import Callable, Dict, Optional, cast @@ -115,7 +115,6 @@ def reconnect( """Disconnect and (optionally) reconnect later.""" return common.DisconnectRes(reason="") # Nothing to do here (yet) - class RayActorClientProxy(ClientProxy): """Flower client proxy which delegates work using Ray.""" @@ -140,6 +139,7 @@ def _submit_job(self, job: Callable): log(ERROR, "ActorPool is empty!!! Disconnecting VirtualClient") # TODO: the below does nothing? self.reconnect() + log(ERROR, traceback.format_exc()) log(ERROR, ex) raise ex From 3e9d1a84aa05328debbaa97341e35a0b3cfa36b9 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Mon, 17 Jul 2023 13:37:59 +0100 Subject: [PATCH 015/133] suport for node abrupt disconnect --- src/py/flwr/simulation/app.py | 3 +-- .../flwr/simulation/ray_transport/ray_actor.py | 17 ++++++++++------- .../ray_transport/ray_client_proxy.py | 1 + 3 files changed, 12 insertions(+), 9 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 0083f0ebfdfe..0215788b90ec 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -206,10 +206,9 @@ def start_simulation( # pylint: disable=too-many-arguments log( INFO, - "Flower VCE: Creating %s with %s %s", + "Flower VCE: Creating %s with %s actors", pool.__class__.__name__, pool.num_actors, - pool._idle_actors[0].__class__.__name__, ) for cid in cids: diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index e7d881735a97..65ed23ca5ef1 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -16,7 +16,7 @@ import threading import traceback -from logging import ERROR, WARNING, INFO +from logging import ERROR, INFO, WARNING from typing import Any, Callable, Dict, List, Set import ray @@ -238,14 +238,14 @@ def process_unordered_future(self, timeout=None, ignore_if_timedout=False) -> No raise StopIteration("No more results to get") res, _ = ray.wait(list(self._future_to_actor), num_returns=1, timeout=timeout) timeout_msg = "Timed out waiting for result" - raise_timeout_after_ignore = False if res: [future] = res else: if not ignore_if_timedout: raise TimeoutError(timeout_msg) else: - raise_timeout_after_ignore = True + # Treat as if nothing happened. + return _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) if a is not None: @@ -259,9 +259,6 @@ def process_unordered_future(self, timeout=None, ignore_if_timedout=False) -> No else: a.terminate.remote() - if raise_timeout_after_ignore: - raise TimeoutError(timeout_msg + f". The task {future} has been ignored.") - def get_client_result(self, cid: str, timeout: int = 3600) -> Any: """Get result from VirtualClient with specific cid.""" # loop until all jobs submitted to the pool are completed. Break early @@ -269,7 +266,13 @@ def get_client_result(self, cid: str, timeout: int = 3600) -> Any: while self.has_next() and not (self._is_future_ready(cid)): try: with self.lock: - self.process_unordered_future(timeout=timeout) + # in multi-node settings, if one node goes down abruptly, the + # ray.wait() in the method below might wait forever... to get + # around this, we set small timeout (1second). We ignore the + # TimeOutException when this happens. Note none of this is + # strictly necessary if the users decides to manually de-register + # a node from Ray (i.e. via `ray stop` in the cli). + self.process_unordered_future(timeout=1, ignore_if_timedout=True) except StopIteration: # there are no pending jobs in the pool break diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index f7c55d0ca8f7..d74230dd987e 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -115,6 +115,7 @@ def reconnect( """Disconnect and (optionally) reconnect later.""" return common.DisconnectRes(reason="") # Nothing to do here (yet) + class RayActorClientProxy(ClientProxy): """Flower client proxy which delegates work using Ray.""" From 18de9e4b028864bf2f0cd6a5679e80162fd3b55a Mon Sep 17 00:00:00 2001 From: jafermarq Date: Mon, 17 Jul 2023 14:32:11 +0100 Subject: [PATCH 016/133] reverting lock positioning --- .../simulation/ray_transport/ray_actor.py | 38 +++++++++---------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 65ed23ca5ef1..05ece9901763 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -247,17 +247,18 @@ def process_unordered_future(self, timeout=None, ignore_if_timedout=False) -> No # Treat as if nothing happened. return - _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) - if a is not None: - # still space in queue ? (no if a node died) - if self._check_actor_fits_in_pool(): - if self._check_and_remove_actor_from_pool(a): - self._return_actor(a) - # flag future as ready - self._flag_future_as_ready(cid) - # print(self._cid_to_future[cid]) - else: - a.terminate.remote() + with self.lock: + _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) + if a is not None: + # still space in queue ? (no if a node died) + if self._check_actor_fits_in_pool(): + if self._check_and_remove_actor_from_pool(a): + self._return_actor(a) + # flag future as ready + self._flag_future_as_ready(cid) + # print(self._cid_to_future[cid]) + else: + a.terminate.remote() def get_client_result(self, cid: str, timeout: int = 3600) -> Any: """Get result from VirtualClient with specific cid.""" @@ -265,14 +266,13 @@ def get_client_result(self, cid: str, timeout: int = 3600) -> Any: # if the result for the ClientProxy running this method is ready while self.has_next() and not (self._is_future_ready(cid)): try: - with self.lock: - # in multi-node settings, if one node goes down abruptly, the - # ray.wait() in the method below might wait forever... to get - # around this, we set small timeout (1second). We ignore the - # TimeOutException when this happens. Note none of this is - # strictly necessary if the users decides to manually de-register - # a node from Ray (i.e. via `ray stop` in the cli). - self.process_unordered_future(timeout=1, ignore_if_timedout=True) + # in multi-node settings, if one node goes down abruptly, the + # ray.wait() in the method below might wait forever... to get + # around this, we set small timeout (1second). We ignore the + # TimeOutException when this happens. Note none of this is + # strictly necessary if the users decides to manually de-register + # a node from Ray (i.e. via `ray stop` in the cli). + self.process_unordered_future(timeout=1, ignore_if_timedout=True) except StopIteration: # there are no pending jobs in the pool break From 0213d9da788953a4c80d520d684bbd9e192010eb Mon Sep 17 00:00:00 2001 From: jafermarq Date: Mon, 17 Jul 2023 15:02:52 +0100 Subject: [PATCH 017/133] minor changes --- src/py/flwr/simulation/app.py | 26 ++++++++++--------- .../simulation/ray_transport/ray_actor.py | 21 +++++---------- 2 files changed, 20 insertions(+), 27 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 0215788b90ec..1b2534a20c60 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -94,11 +94,11 @@ def start_simulation( # pylint: disable=too-many-arguments List `client_id`s for each client. This is only required if `num_clients` is not set. Setting both `num_clients` and `clients_ids` with `len(clients_ids)` not equal to `num_clients` generates an error. - client_resources : Optional[Dict[str, float]] (default: None) - CPU and GPU resources for a single client. Supported keys are - `num_cpus` and `num_gpus`. Example: `{"num_cpus": 4, "num_gpus": 1}`. - To understand the GPU utilization caused by `num_gpus`, consult the Ray - documentation on GPU support. + client_resources : Optional[Dict[str, float]] (default: `{"num_cpus": 1, + "num_gpus": 0.0}` CPU and GPU resources for a single client. Supported keys + are `num_cpus` and `num_gpus`. To understand the GPU utilization caused by + `num_gpus`, as well as using custom resources, please consult the Ray + documentation. server : Optional[flwr.server.Server] (default: None). An implementation of the abstract base class `flwr.server.Server`. If no instance is provided, then `start_server` will create one. @@ -186,15 +186,17 @@ def start_simulation( # pylint: disable=too-many-arguments ) # log resources for each virtual_client - # If not specified by user, Ray uses default: 1x CPU, 0x GPU - resources = {"num_cpus": 1.0, "num_gpus": 0.0} - if client_resources: - for k, v in client_resources.items(): - resources[k] = v + if client_resources is None: + client_resources = {"num_cpus": 1, "num_gpus": 0.0} + log( + INFO, + "No `client_resources` specified. Using minimal resources for clients.", + ) + log( INFO, "Flower VCE: Resources for each Virtual Client: %s", - resources, + client_resources, ) # instantiate ActorPool @@ -202,7 +204,7 @@ def start_simulation( # pylint: disable=too-many-arguments max_restarts = 1 # how many times an actor that crashes should be restarted # after these many restarts, it will be removed from the pool - pool = VirtualClientEngineActorPool(resources, max_restarts) + pool = VirtualClientEngineActorPool(client_resources, max_restarts) log( INFO, diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 05ece9901763..4765a109e13f 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -16,7 +16,7 @@ import threading import traceback -from logging import ERROR, INFO, WARNING +from logging import ERROR, WARNING from typing import Any, Callable, Dict, List, Set import ray @@ -104,7 +104,8 @@ def __init__(self, client_resources: Dict, max_restarts: int = 1): self.lock = threading.RLock() - # TODO: asyncio check every N seconds if cluster has grown --> add more actors to the pool if so + # TODO: asyncio check every N seconds if cluster has grown + # --> add more actors to the pool if so def __reduce__(self): """Make this class serialisable (needed due to lock).""" @@ -232,7 +233,7 @@ def _check_actor_fits_in_pool(self) -> bool: else: return True - def process_unordered_future(self, timeout=None, ignore_if_timedout=False) -> None: + def process_unordered_future(self, timeout=None) -> None: """Similar to parent's get_next_unordered() but without final ray.get().""" if not self.has_next(): raise StopIteration("No more results to get") @@ -241,11 +242,7 @@ def process_unordered_future(self, timeout=None, ignore_if_timedout=False) -> No if res: [future] = res else: - if not ignore_if_timedout: - raise TimeoutError(timeout_msg) - else: - # Treat as if nothing happened. - return + raise TimeoutError(timeout_msg) with self.lock: _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) @@ -266,13 +263,7 @@ def get_client_result(self, cid: str, timeout: int = 3600) -> Any: # if the result for the ClientProxy running this method is ready while self.has_next() and not (self._is_future_ready(cid)): try: - # in multi-node settings, if one node goes down abruptly, the - # ray.wait() in the method below might wait forever... to get - # around this, we set small timeout (1second). We ignore the - # TimeOutException when this happens. Note none of this is - # strictly necessary if the users decides to manually de-register - # a node from Ray (i.e. via `ray stop` in the cli). - self.process_unordered_future(timeout=1, ignore_if_timedout=True) + self.process_unordered_future(timeout=timeout) except StopIteration: # there are no pending jobs in the pool break From 7a3c79183366b092bd8480e94f4be05a89f6d746 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Mon, 17 Jul 2023 17:34:12 +0100 Subject: [PATCH 018/133] minor changes; hints on how to try if simulation fails --- src/py/flwr/simulation/app.py | 29 ++++++++++++++++--- .../simulation/ray_transport/ray_actor.py | 28 +++++++++++------- .../ray_transport/ray_client_proxy.py | 12 ++++---- 3 files changed, 49 insertions(+), 20 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 1b2534a20c60..0c033593a5bf 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -222,10 +222,31 @@ def start_simulation( # pylint: disable=too-many-arguments initialized_server.client_manager().register(client=client_proxy) # Start training - hist = run_fl( - server=initialized_server, - config=initialized_config, - ) + try: + hist = run_fl( + server=initialized_server, + config=initialized_config, + ) + except Exception as ex: + log(ERROR, ex) + log( + ERROR, + "Your simulation crashed :(. This could be because of several reasons." + "The most common are: " + "\n\t > Your system couldn't fit a single VirtualClient: try lowering " + "`client_resources`." + "\n\t > All the actors in your pool crashed. This could be because: " + "\n\t\t - You clients hit an out-of-memory (OOM) error and actors couldn't " + "recover from it. Try launching your simulation with more generous " + f"`client_resources` setting (i.e. it seems {client_resources} is " + "not enough for your workload). Use fewer concurrent actors. " + "\n\t\t - You were running a multi-node simulation and all worker nodes " + "disconnected. The head node might still be alive but cannot accommodate " + f"any actor with resources: {client_resources}." + "\n\t\t - Your Actors crashed because of an unknown reason and all their " + f"restarts attempts ({max_restarts=}) have been exhausted." + ) + hist = None event(EventType.START_SIMULATION_LEAVE) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 4765a109e13f..428d90e38b37 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -79,6 +79,14 @@ def pool_size_from_resources(client_resources: Dict): else: num_actors = 0 + if num_actors == 0: + log( + WARNING, + f"Your ActorPool is empty. Your system ({num_cpus = }, {num_gpus = }) " + "does not meet the criteria to host at least one client with resources:" + f" {client_resources}. Consider lowering your `client_resources`", + ) + return num_actors @@ -142,7 +150,7 @@ def submit_client_job(self, fn: Any, value: Callable, cid: str) -> None: self._pending_submits.append((fn, value, cid)) def _flag_future_as_ready(self, cid) -> None: - """Flag future for VirtualClient as ready.""" + """Flag future for VirtualClient with cid=cid as ready.""" self._cid_to_future[cid]["ready"] = True def _reset_cid_to_future_dict(self, cid: str) -> None: @@ -154,7 +162,7 @@ def _reset_cid_to_future_dict(self, cid: str) -> None: self._cid_to_future[cid]["ready"] = False def _is_future_ready(self, cid: str) -> bool: - """Return status of future for this VirtualClient.""" + """Return status of future associated to the given client id (cid).""" if cid not in self._cid_to_future.keys(): return False else: @@ -166,7 +174,6 @@ def _fetch_future_result(self, cid: str) -> Any: res_cid, res = ray.get(self._cid_to_future[cid]["future"]) except ray.exceptions.RayActorError as ex: log(ERROR, ex) - log(ERROR, traceback.format_exc()) if hasattr(ex, "actor_id"): # RayActorError only contains the actor_id attribute # if the actor won't be restarted again. @@ -207,11 +214,10 @@ def _check_and_remove_actor_from_pool( log(WARNING, f"Pool size: {self.num_actors}") return False else: - # print(f"actor: {actor_id} should not be killed") return True def _check_actor_fits_in_pool(self) -> bool: - """Determine if available resources are haven't changed. + """Determine if available resources haven't changed. If true, allow the actor to be added back to the pool. Else don't allow it (effectively reducing the size of the pool). @@ -226,7 +232,7 @@ def _check_actor_fits_in_pool(self) -> bool: " might take several intermediate steps", ) # we are preventing one actor to be added back in the queue, so we just - # decrease the number of actors by one eventually `self.num_actors` + # decrease the number of actors by one. Eventually `self.num_actors` # should be equal what pool_size_from_resources(self.resources) returns self.num_actors -= 1 return False @@ -238,13 +244,14 @@ def process_unordered_future(self, timeout=None) -> None: if not self.has_next(): raise StopIteration("No more results to get") res, _ = ray.wait(list(self._future_to_actor), num_returns=1, timeout=timeout) - timeout_msg = "Timed out waiting for result" + if res: [future] = res else: - raise TimeoutError(timeout_msg) + raise TimeoutError("Timed out waiting for result") with self.lock: + # get actor that completed a job _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) if a is not None: # still space in queue ? (no if a node died) @@ -253,14 +260,15 @@ def process_unordered_future(self, timeout=None) -> None: self._return_actor(a) # flag future as ready self._flag_future_as_ready(cid) - # print(self._cid_to_future[cid]) else: + # the actor doesn't fit in the pool anymore. + # Manually terminate the actor a.terminate.remote() def get_client_result(self, cid: str, timeout: int = 3600) -> Any: """Get result from VirtualClient with specific cid.""" # loop until all jobs submitted to the pool are completed. Break early - # if the result for the ClientProxy running this method is ready + # if the result for the ClientProxy calling this method is ready while self.has_next() and not (self._is_future_ready(cid)): try: self.process_unordered_future(timeout=timeout) diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index d74230dd987e..859eafe0cee8 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -126,12 +126,12 @@ def __init__( self.client_fn = client_fn self.actor_pool = actor_pool - def _submit_job(self, job: Callable): + def _submit_job(self, job: Callable, timeout: Optional[float]): try: self.actor_pool.submit_client_job( lambda a, v: a.run.remote(v, self.cid), job, self.cid ) - res = self.actor_pool.get_client_result(self.cid) + res = self.actor_pool.get_client_result(self.cid, timeout) except Exception as ex: if self.actor_pool.num_actors == 0: @@ -158,7 +158,7 @@ def get_properties() -> common.GetPropertiesRes: get_properties_ins=ins, ) - res = self._submit_job(get_properties) + res = self._submit_job(get_properties, timeout) return cast( common.GetPropertiesRes, @@ -177,7 +177,7 @@ def get_parameters() -> common.GetParametersRes: get_parameters_ins=ins, ) - res = self._submit_job(get_parameters) + res = self._submit_job(get_parameters, timeout) return cast( common.GetParametersRes, @@ -194,7 +194,7 @@ def fit() -> common.FitRes: fit_ins=ins, ) - res = self._submit_job(fit) + res = self._submit_job(fit, timeout) return cast( common.FitRes, @@ -213,7 +213,7 @@ def evaluate() -> common.EvaluateRes: evaluate_ins=ins, ) - res = self._submit_job(evaluate) + res = self._submit_job(evaluate, timeout) return cast( common.EvaluateRes, From c47780f9d85e26cfdb172eaec83e9ec3b718fcf0 Mon Sep 17 00:00:00 2001 From: "Daniel J. Beutel" Date: Fri, 11 Aug 2023 17:32:53 +0200 Subject: [PATCH 019/133] Format --- src/py/flwr/simulation/app.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 0c033593a5bf..ec4b3466480d 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -244,7 +244,7 @@ def start_simulation( # pylint: disable=too-many-arguments "disconnected. The head node might still be alive but cannot accommodate " f"any actor with resources: {client_resources}." "\n\t\t - Your Actors crashed because of an unknown reason and all their " - f"restarts attempts ({max_restarts=}) have been exhausted." + f"restarts attempts ({max_restarts=}) have been exhausted.", ) hist = None From 36ba7e79803f353d07a957819d52646155cf3d63 Mon Sep 17 00:00:00 2001 From: Javier Date: Sat, 12 Aug 2023 14:08:26 +0100 Subject: [PATCH 020/133] Update src/py/flwr/simulation/ray_transport/ray_actor.py Co-authored-by: Daniel J. Beutel --- src/py/flwr/simulation/ray_transport/ray_actor.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 428d90e38b37..24ea69628b6a 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -14,6 +14,7 @@ # ============================================================================== """Ray-based Flower Actor and ActorPool implementation.""" + import threading import traceback from logging import ERROR, WARNING From b0238b94735f6fc16ca1ebef5697ce37b6234697 Mon Sep 17 00:00:00 2001 From: Javier Date: Sat, 12 Aug 2023 14:15:47 +0100 Subject: [PATCH 021/133] Apply suggestions from code review Co-authored-by: Daniel J. Beutel --- src/py/flwr/simulation/app.py | 9 +++++---- src/py/flwr/simulation/ray_transport/ray_actor.py | 8 ++++---- src/py/flwr/simulation/ray_transport/ray_client_proxy.py | 3 ++- 3 files changed, 11 insertions(+), 9 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index ec4b3466480d..8d636b85d116 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -185,13 +185,13 @@ def start_simulation( # pylint: disable=too-many-arguments cluster_resources, ) - # log resources for each virtual_client + # Log the resources that a single client will be able to use if client_resources is None: - client_resources = {"num_cpus": 1, "num_gpus": 0.0} log( INFO, "No `client_resources` specified. Using minimal resources for clients.", ) + client_resources = {"num_cpus": 1, "num_gpus": 0.0} log( INFO, @@ -199,10 +199,11 @@ def start_simulation( # pylint: disable=too-many-arguments client_resources, ) - # instantiate ActorPool + # Instantiate ActorPool # TODO: maybe we want `max_restarts` to be user-defined ? - max_restarts = 1 # how many times an actor that crashes should be restarted + # `max_restarts` determines how many times an actor that crashes should be restarted # after these many restarts, it will be removed from the pool + max_restarts = 1 pool = VirtualClientEngineActorPool(client_resources, max_restarts) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 24ea69628b6a..b8cbd913e052 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -43,13 +43,13 @@ def terminate(self): log(WARNING, f"Manually terminating {self.__class__.__name__}") ray.actor.exit_actor() - def run(self, client_fn: Callable, client_id): + def run(self, job_fn: Callable, cid: str): """Run a client workload.""" # execute tasks and return result # return also cid which is needed to ensure results # from the pool are correctly assigned to each ClientProxy try: - client_results = client_fn() + job_results = job_fn() except Exception as ex: client_trace = traceback.format_exc() message = ( @@ -123,7 +123,7 @@ def __reduce__(self): self.actor_max_restarts, ) - def submit(self, fn: Any, value: Callable, cid: str) -> None: + def submit(self, fn: Any, job_fn: Callable, cid: str) -> None: """Take idle actor and assign it a client workload.""" actor = self._idle_actors.pop() if self._check_and_remove_actor_from_pool(actor): @@ -135,7 +135,7 @@ def submit(self, fn: Any, value: Callable, cid: str) -> None: # update with future self._cid_to_future[cid]["future"] = future_key - def submit_client_job(self, fn: Any, value: Callable, cid: str) -> None: + def submit_client_job(self, fn: Any, job_fn: Callable, cid: str) -> None: """Submit a job while tracking client ids.""" # We need to put this behind a lock since .submit() involves # removing and adding elements from a dictionary. Which creates diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index 859eafe0cee8..1d03f359eac6 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -14,6 +14,7 @@ # ============================================================================== """Ray-based Flower ClientProxy implementation.""" + import traceback from logging import ERROR from typing import Callable, Dict, Optional, cast @@ -126,7 +127,7 @@ def __init__( self.client_fn = client_fn self.actor_pool = actor_pool - def _submit_job(self, job: Callable, timeout: Optional[float]): + def _submit_job(self, job_fn: Callable, timeout: Optional[float]): try: self.actor_pool.submit_client_job( lambda a, v: a.run.remote(v, self.cid), job, self.cid From 73ef8365e7de15fa50332499b29e46a1e7d572b8 Mon Sep 17 00:00:00 2001 From: javier Date: Sat, 12 Aug 2023 13:47:08 +0000 Subject: [PATCH 022/133] tweaks post-comments; fix to pool_size_from_resources --- src/py/flwr/simulation/app.py | 3 ++- .../simulation/ray_transport/ray_actor.py | 22 +++++++++++-------- .../ray_transport/ray_client_proxy.py | 2 +- 3 files changed, 16 insertions(+), 11 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 8d636b85d116..0b16fa7325d5 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -143,7 +143,7 @@ def start_simulation( # pylint: disable=too-many-arguments strategy=strategy, client_manager=client_manager, ) - # Setting simulation ON for server + log( INFO, "Starting Flower simulation, config: %s", @@ -214,6 +214,7 @@ def start_simulation( # pylint: disable=too-many-arguments pool.num_actors, ) + # Register one RayClientProxy object for each client with the ClientManager for cid in cids: client_proxy = RayActorClientProxy( client_fn=client_fn, diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index b8cbd913e052..804f2ace62b0 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -1,4 +1,4 @@ -# Copyright 2020 Adap GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -54,13 +54,13 @@ def run(self, job_fn: Callable, cid: str): client_trace = traceback.format_exc() message = ( "\n\tSomething went wrong when running your client workload." - f"\n\tClient {client_id} crashed when the {self.__class__.__name__}" + f"\n\tClient {cid} crashed when the {self.__class__.__name__}" " was running its workload." f"\n\tException triggered on the client side: {client_trace}" ) raise ClientException(message) from ex - return client_id, client_results + return cid, job_results def pool_size_from_resources(client_resources: Dict): @@ -73,7 +73,7 @@ def pool_size_from_resources(client_resources: Dict): num_gpus = cluster_resources.get("GPU", 0) # there might not be GPU num_actors = int(num_cpus / client_resources["num_cpus"]) # if a GPU is present and client resources do require one - if client_resources["num_gpus"] > 0.0: + if "num_gpus" in client_resources.keys() and client_resources["num_gpus"] > 0.0: if num_gpus: # if there are gpus in the cluster num_actors = min(num_actors, int(num_gpus / client_resources["num_gpus"])) @@ -87,7 +87,9 @@ def pool_size_from_resources(client_resources: Dict): "does not meet the criteria to host at least one client with resources:" f" {client_resources}. Consider lowering your `client_resources`", ) - + raise ValueError(f"ActorPool is empty. Stopping Simulation."\ + "Check 'client_resources'") + return num_actors @@ -107,7 +109,9 @@ def __init__(self, client_resources: Dict, max_restarts: int = 1): super().__init__(actors) - self._cid_to_future = {} # a dict + # A dict that maps cid to another dict containing: a reference to the remote job + # and its status (i.e. whether it is ready or not) + self._cid_to_future: Dict[str, Dict[str, Any]] = {} self.actor_to_remove: Set[str] = set() # a set self.num_actors = len(actors) @@ -127,7 +131,7 @@ def submit(self, fn: Any, job_fn: Callable, cid: str) -> None: """Take idle actor and assign it a client workload.""" actor = self._idle_actors.pop() if self._check_and_remove_actor_from_pool(actor): - future = fn(actor, value) + future = fn(actor, job_fn) future_key = tuple(future) if isinstance(future, List) else future self._future_to_actor[future_key] = (self._next_task_index, actor, cid) self._next_task_index += 1 @@ -145,10 +149,10 @@ def submit_client_job(self, fn: Any, job_fn: Callable, cid: str) -> None: self._reset_cid_to_future_dict(cid) if self._idle_actors: # submit job since there is an Actor that's available - self.submit(fn, value, cid) + self.submit(fn, job_fn, cid) else: # no actors are available, append to list of jobs to run later - self._pending_submits.append((fn, value, cid)) + self._pending_submits.append((fn, job_fn, cid)) def _flag_future_as_ready(self, cid) -> None: """Flag future for VirtualClient with cid=cid as ready.""" diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index 1d03f359eac6..6c9d4d7cd036 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -130,7 +130,7 @@ def __init__( def _submit_job(self, job_fn: Callable, timeout: Optional[float]): try: self.actor_pool.submit_client_job( - lambda a, v: a.run.remote(v, self.cid), job, self.cid + lambda a, v: a.run.remote(v, self.cid), job_fn, self.cid ) res = self.actor_pool.get_client_result(self.cid, timeout) From 6f2c374bb7342e0c127de51c275db6f5e5713480 Mon Sep 17 00:00:00 2001 From: javier Date: Sat, 12 Aug 2023 14:48:52 +0000 Subject: [PATCH 023/133] Added TF actor with GPU fix; now you can specify which VCEActor to use --- src/py/flwr/simulation/app.py | 21 +++++- .../simulation/ray_transport/ray_actor.py | 70 ++++++++++++++++--- 2 files changed, 78 insertions(+), 13 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 0b16fa7325d5..94544e6ebf05 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -29,7 +29,7 @@ from flwr.server.client_manager import ClientManager from flwr.server.history import History from flwr.server.strategy import Strategy -from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActorPool +from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActorPool, VirtualClientEngineActor, DefaultActor from flwr.simulation.ray_transport.ray_client_proxy import RayActorClientProxy INVALID_ARGUMENTS_START_SIMULATION = """ @@ -72,6 +72,8 @@ def start_simulation( # pylint: disable=too-many-arguments client_manager: Optional[ClientManager] = None, ray_init_args: Optional[Dict[str, Any]] = None, keep_initialised: Optional[bool] = False, + actor_type: Optional[VirtualClientEngineActor] = DefaultActor, + actor_kwargs: Optional[Dict[str, Any]] = {}, ) -> History: """Start a Ray-based Flower simulation server. @@ -124,6 +126,18 @@ def start_simulation( # pylint: disable=too-many-arguments arguments from being passed to ray.init. keep_initialised: Optional[bool] (default: False) Set to True to prevent `ray.shutdown()` in case `ray.is_initialized()=True`. + + actor_type: Optional[VirtualClientEngineActor] (default: DefaultActor) + + Optionally specify the type of actor to use. The actor object, which + persist throughout the simulation will be the process in charged of + running the clients' jobs (i.e. their fit() method). If you are using + Tensorflow, you should use type `DefaultActor_TF` which will set TF's + GPU memory growth to True at initialisation (preventing premature OOM). + + actor_kwargs: Optional[Dict[str, Any]] (default: {}) + If you want to create your own Actor classes, you might need to pass + some input argument. You can use this dictionary for such purpose. Returns ------- @@ -205,7 +219,10 @@ def start_simulation( # pylint: disable=too-many-arguments # after these many restarts, it will be removed from the pool max_restarts = 1 - pool = VirtualClientEngineActorPool(client_resources, max_restarts) + pool = VirtualClientEngineActorPool(client_resources, + actor_type, + actor_kwargs, + max_restarts) log( INFO, diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 804f2ace62b0..b4672b3b6a11 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -17,8 +17,9 @@ import threading import traceback +from abc import ABC from logging import ERROR, WARNING -from typing import Any, Callable, Dict, List, Set +from typing import Any, Callable, Dict, List, Set, Union import ray from ray.util.actor_pool import ActorPool @@ -34,15 +35,14 @@ def __init__(self, message: str): super().__init__(self.message) -@ray.remote -class VirtualClientEngineActor: - """A Ray Actor class that runs client workloads.""" +class VirtualClientEngineActor(ABC): + """Abstract base class for VirtualClientEngine Actors""" def terminate(self): - """Terminate Actor.""" + """Manually terminate Actor object.""" log(WARNING, f"Manually terminating {self.__class__.__name__}") ray.actor.exit_actor() - + def run(self, job_fn: Callable, cid: str): """Run a client workload.""" # execute tasks and return result @@ -63,6 +63,45 @@ def run(self, job_fn: Callable, cid: str): return cid, job_results +@ray.remote +class DefaultActor(VirtualClientEngineActor): + """A Ray Actor class that runs client workloads.""" + + +@ray.remote +class DefaultActor_TF(VirtualClientEngineActor): + """A Ray Actor class that runs TF client workloads. + + It enables GPU memory growth to prevent premature OOM.""" + + def __init__(self): + super().__init__() + # By default, TF attempts maps all GPU memory to the process. + # We don't this behaviour in simulation, since it prevents us + # from having multiple Actors (and therefore Flower clients) sharing + # the same GPU. + # Luckily we can disable this behaviour by enabling memory growth + # on the GPU. In this way, VRAM allocated to the processes grows based + # on the needs for the workload. (this is for instance the default + # behaviour in Pytorch) + try: + import tensorflow as tf + gpus = tf.config.list_physical_devices('GPU') + if gpus: + try: + # Currently, memory growth needs to be the same across GPUs + for gpu in gpus: + tf.config.experimental.set_memory_growth(gpu, True) + logical_gpus = tf.config.list_logical_devices('GPU') + except RuntimeError as e: + # Memory growth must be set before GPUs have been initialized + print(e) + except Exception as e: + log(ERROR, "Do you have Tensorflow installed?") + raise e + + + def pool_size_from_resources(client_resources: Dict): """Calculate number of Actors that fit in pool given the resources in the. @@ -87,23 +126,30 @@ def pool_size_from_resources(client_resources: Dict): "does not meet the criteria to host at least one client with resources:" f" {client_resources}. Consider lowering your `client_resources`", ) - raise ValueError(f"ActorPool is empty. Stopping Simulation."\ + raise ValueError(f"ActorPool is empty. Stopping Simulation." \ "Check 'client_resources'") - + return num_actors class VirtualClientEngineActorPool(ActorPool): """A pool of VirtualClientEngine Actors.""" - def __init__(self, client_resources: Dict, max_restarts: int = 1): + def __init__(self, + client_resources: Dict[str, Union[int, float]], + actor_type: VirtualClientEngineActor, + actor_kwargs: Dict[str, Any], + max_restarts: int, + ): self.client_resources = client_resources + self.actor_type = actor_type + self.actor_kwargs = actor_kwargs self.actor_max_restarts = max_restarts num_actors = pool_size_from_resources(client_resources) actors = [ - VirtualClientEngineActor.options( + actor_type.options( **client_resources, max_restarts=max_restarts - ).remote() + ).remote(**actor_kwargs) for _ in range(num_actors) ] @@ -124,6 +170,8 @@ def __reduce__(self): """Make this class serialisable (needed due to lock).""" return VirtualClientEngineActorPool, ( self.client_resources, + self.actor_type, + self.actor_kwargs, self.actor_max_restarts, ) From 58d6722d997fba410d551758c1a3f097e817b2ca Mon Sep 17 00:00:00 2001 From: javier Date: Sat, 12 Aug 2023 15:13:21 +0000 Subject: [PATCH 024/133] minor tweaks --- src/py/flwr/simulation/ray_transport/ray_actor.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index b4672b3b6a11..2c719a183f6d 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -76,7 +76,7 @@ class DefaultActor_TF(VirtualClientEngineActor): def __init__(self): super().__init__() - # By default, TF attempts maps all GPU memory to the process. + # By default, TF maps all GPU memory to the process. # We don't this behaviour in simulation, since it prevents us # from having multiple Actors (and therefore Flower clients) sharing # the same GPU. @@ -86,13 +86,14 @@ def __init__(self): # behaviour in Pytorch) try: import tensorflow as tf + # this bit of code follows the guidelines for GPU usage + # in https://www.tensorflow.org/guide/gpu gpus = tf.config.list_physical_devices('GPU') if gpus: try: # Currently, memory growth needs to be the same across GPUs for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) - logical_gpus = tf.config.list_logical_devices('GPU') except RuntimeError as e: # Memory growth must be set before GPUs have been initialized print(e) From b6f5fa7a5f95dfd4b04d15cc01dbca39f376e5e7 Mon Sep 17 00:00:00 2001 From: javier Date: Tue, 15 Aug 2023 13:50:45 +0000 Subject: [PATCH 025/133] option to specify actor scheduling strategy --- src/py/flwr/simulation/app.py | 24 ++++++++++++++----- .../simulation/ray_transport/ray_actor.py | 7 +++++- 2 files changed, 24 insertions(+), 7 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 94544e6ebf05..19b1fdd5f6a3 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -17,9 +17,10 @@ import sys from logging import ERROR, INFO -from typing import Any, Callable, Dict, List, Optional +from typing import Any, Callable, Dict, List, Optional, Union import ray +from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy from flwr.client import ClientLike from flwr.common import EventType, event @@ -74,6 +75,9 @@ def start_simulation( # pylint: disable=too-many-arguments keep_initialised: Optional[bool] = False, actor_type: Optional[VirtualClientEngineActor] = DefaultActor, actor_kwargs: Optional[Dict[str, Any]] = {}, + actor_scheduling: Optional[Union[str, + NodeAffinitySchedulingStrategy] + ] = "DEFAULT", ) -> History: """Start a Ray-based Flower simulation server. @@ -126,7 +130,7 @@ def start_simulation( # pylint: disable=too-many-arguments arguments from being passed to ray.init. keep_initialised: Optional[bool] (default: False) Set to True to prevent `ray.shutdown()` in case `ray.is_initialized()=True`. - + actor_type: Optional[VirtualClientEngineActor] (default: DefaultActor) Optionally specify the type of actor to use. The actor object, which @@ -139,6 +143,13 @@ def start_simulation( # pylint: disable=too-many-arguments If you want to create your own Actor classes, you might need to pass some input argument. You can use this dictionary for such purpose. + actor_scheduling: Optional[Union[str, NodeAffinitySchedulingStrategy]] (default: "DEFAULT") + Optional string ("DEFAULT" or "SPREAD") for the VCE to choose in which + node the actor is placed. If you are an advanced user needed more control + you can use lower-level scheduling strategies to pin actors to specific + compute nodes (e.g. via NodeAffinitySchedulingStrategy). Please note this + is an advanced feature. For all details, please refer to the Ray documentation: + https://docs.ray.io/en/latest/ray-core/scheduling/index.html Returns ------- hist : flwr.server.history.History @@ -219,10 +230,11 @@ def start_simulation( # pylint: disable=too-many-arguments # after these many restarts, it will be removed from the pool max_restarts = 1 - pool = VirtualClientEngineActorPool(client_resources, - actor_type, - actor_kwargs, - max_restarts) + pool = VirtualClientEngineActorPool(client_resources=client_resources, + actor_type=actor_type, + actor_kwargs=actor_kwargs, + actor_scheduling=actor_scheduling, + max_restarts=max_restarts) log( INFO, diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 2c719a183f6d..59be16722948 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -140,16 +140,20 @@ def __init__(self, client_resources: Dict[str, Union[int, float]], actor_type: VirtualClientEngineActor, actor_kwargs: Dict[str, Any], + actor_scheduling: str, max_restarts: int, ): self.client_resources = client_resources self.actor_type = actor_type self.actor_kwargs = actor_kwargs + self.actor_scheduling = actor_scheduling self.actor_max_restarts = max_restarts num_actors = pool_size_from_resources(client_resources) actors = [ actor_type.options( - **client_resources, max_restarts=max_restarts + **client_resources, + scheduling_strategy=actor_scheduling, + max_restarts=max_restarts ).remote(**actor_kwargs) for _ in range(num_actors) ] @@ -173,6 +177,7 @@ def __reduce__(self): self.client_resources, self.actor_type, self.actor_kwargs, + self.actor_scheduling, self.actor_max_restarts, ) From 8980a4463151eb270eed4af5ffb01fb9481606cf Mon Sep 17 00:00:00 2001 From: jafermarq Date: Tue, 15 Aug 2023 16:53:19 +0100 Subject: [PATCH 026/133] format --- src/py/flwr/simulation/app.py | 33 ++++++++------- .../simulation/ray_transport/ray_actor.py | 42 +++++++++++-------- 2 files changed, 43 insertions(+), 32 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 19b1fdd5f6a3..342e94ea97e4 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -30,7 +30,11 @@ from flwr.server.client_manager import ClientManager from flwr.server.history import History from flwr.server.strategy import Strategy -from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActorPool, VirtualClientEngineActor, DefaultActor +from flwr.simulation.ray_transport.ray_actor import ( + DefaultActor, + VirtualClientEngineActor, + VirtualClientEngineActorPool, +) from flwr.simulation.ray_transport.ray_client_proxy import RayActorClientProxy INVALID_ARGUMENTS_START_SIMULATION = """ @@ -74,10 +78,8 @@ def start_simulation( # pylint: disable=too-many-arguments ray_init_args: Optional[Dict[str, Any]] = None, keep_initialised: Optional[bool] = False, actor_type: Optional[VirtualClientEngineActor] = DefaultActor, - actor_kwargs: Optional[Dict[str, Any]] = {}, - actor_scheduling: Optional[Union[str, - NodeAffinitySchedulingStrategy] - ] = "DEFAULT", + actor_kwargs: Optional[Dict[str, Any]] = None, + actor_scheduling: Optional[Union[str, NodeAffinitySchedulingStrategy]] = "DEFAULT", ) -> History: """Start a Ray-based Flower simulation server. @@ -139,11 +141,12 @@ def start_simulation( # pylint: disable=too-many-arguments Tensorflow, you should use type `DefaultActor_TF` which will set TF's GPU memory growth to True at initialisation (preventing premature OOM). - actor_kwargs: Optional[Dict[str, Any]] (default: {}) + actor_kwargs: Optional[Dict[str, Any]] (default: None) If you want to create your own Actor classes, you might need to pass some input argument. You can use this dictionary for such purpose. - actor_scheduling: Optional[Union[str, NodeAffinitySchedulingStrategy]] (default: "DEFAULT") + actor_scheduling: Optional[Union[str, NodeAffinitySchedulingStrategy]] + (default: "DEFAULT") Optional string ("DEFAULT" or "SPREAD") for the VCE to choose in which node the actor is placed. If you are an advanced user needed more control you can use lower-level scheduling strategies to pin actors to specific @@ -228,13 +231,15 @@ def start_simulation( # pylint: disable=too-many-arguments # TODO: maybe we want `max_restarts` to be user-defined ? # `max_restarts` determines how many times an actor that crashes should be restarted # after these many restarts, it will be removed from the pool - max_restarts = 1 - - pool = VirtualClientEngineActorPool(client_resources=client_resources, - actor_type=actor_type, - actor_kwargs=actor_kwargs, - actor_scheduling=actor_scheduling, - max_restarts=max_restarts) + max_restarts = 1 + + pool = VirtualClientEngineActorPool( + client_resources=client_resources, + actor_type=actor_type, + actor_kwargs=actor_kwargs, + actor_scheduling=actor_scheduling, + max_restarts=max_restarts, + ) log( INFO, diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 59be16722948..ee4be1b4923a 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -36,13 +36,13 @@ def __init__(self, message: str): class VirtualClientEngineActor(ABC): - """Abstract base class for VirtualClientEngine Actors""" + """Abstract base class for VirtualClientEngine Actors.""" def terminate(self): """Manually terminate Actor object.""" log(WARNING, f"Manually terminating {self.__class__.__name__}") ray.actor.exit_actor() - + def run(self, job_fn: Callable, cid: str): """Run a client workload.""" # execute tasks and return result @@ -71,8 +71,9 @@ class DefaultActor(VirtualClientEngineActor): @ray.remote class DefaultActor_TF(VirtualClientEngineActor): """A Ray Actor class that runs TF client workloads. - - It enables GPU memory growth to prevent premature OOM.""" + + It enables GPU memory growth to prevent premature OOM. + """ def __init__(self): super().__init__() @@ -82,13 +83,14 @@ def __init__(self): # the same GPU. # Luckily we can disable this behaviour by enabling memory growth # on the GPU. In this way, VRAM allocated to the processes grows based - # on the needs for the workload. (this is for instance the default + # on the needs for the workload. (this is for instance the default # behaviour in Pytorch) try: import tensorflow as tf + # this bit of code follows the guidelines for GPU usage # in https://www.tensorflow.org/guide/gpu - gpus = tf.config.list_physical_devices('GPU') + gpus = tf.config.list_physical_devices("GPU") if gpus: try: # Currently, memory growth needs to be the same across GPUs @@ -102,7 +104,6 @@ def __init__(self): raise e - def pool_size_from_resources(client_resources: Dict): """Calculate number of Actors that fit in pool given the resources in the. @@ -127,8 +128,9 @@ def pool_size_from_resources(client_resources: Dict): "does not meet the criteria to host at least one client with resources:" f" {client_resources}. Consider lowering your `client_resources`", ) - raise ValueError(f"ActorPool is empty. Stopping Simulation." \ - "Check 'client_resources'") + raise ValueError( + "ActorPool is empty. Stopping Simulation. Check 'client_resources'" + ) return num_actors @@ -136,25 +138,29 @@ def pool_size_from_resources(client_resources: Dict): class VirtualClientEngineActorPool(ActorPool): """A pool of VirtualClientEngine Actors.""" - def __init__(self, - client_resources: Dict[str, Union[int, float]], - actor_type: VirtualClientEngineActor, - actor_kwargs: Dict[str, Any], - actor_scheduling: str, - max_restarts: int, - ): + def __init__( + self, + client_resources: Dict[str, Union[int, float]], + actor_type: VirtualClientEngineActor, + actor_kwargs: Dict[str, Any], + actor_scheduling: str, + max_restarts: int, + ): self.client_resources = client_resources self.actor_type = actor_type self.actor_kwargs = actor_kwargs self.actor_scheduling = actor_scheduling self.actor_max_restarts = max_restarts num_actors = pool_size_from_resources(client_resources) + + args = actor_kwargs if actor_kwargs is not None else {} + actors = [ actor_type.options( **client_resources, scheduling_strategy=actor_scheduling, - max_restarts=max_restarts - ).remote(**actor_kwargs) + max_restarts=max_restarts, + ).remote(**args) for _ in range(num_actors) ] From d851f4c12c9c8b762e21d3b4da9f8454b1ecc494 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Tue, 15 Aug 2023 17:06:40 +0100 Subject: [PATCH 027/133] . --- src/py/flwr/simulation/app.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 342e94ea97e4..1b4c4fe3ab7b 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -153,6 +153,7 @@ def start_simulation( # pylint: disable=too-many-arguments compute nodes (e.g. via NodeAffinitySchedulingStrategy). Please note this is an advanced feature. For all details, please refer to the Ray documentation: https://docs.ray.io/en/latest/ray-core/scheduling/index.html + Returns ------- hist : flwr.server.history.History From c4883f374555a4a3214070370b321624964cb122 Mon Sep 17 00:00:00 2001 From: javier Date: Wed, 16 Aug 2023 12:55:14 +0000 Subject: [PATCH 028/133] tweaks --- src/py/flwr/simulation/app.py | 10 +-- .../simulation/ray_transport/ray_actor.py | 87 ++++++++++--------- 2 files changed, 45 insertions(+), 52 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 1b4c4fe3ab7b..c038aed8e594 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -229,17 +229,11 @@ def start_simulation( # pylint: disable=too-many-arguments ) # Instantiate ActorPool - # TODO: maybe we want `max_restarts` to be user-defined ? - # `max_restarts` determines how many times an actor that crashes should be restarted - # after these many restarts, it will be removed from the pool - max_restarts = 1 - pool = VirtualClientEngineActorPool( client_resources=client_resources, actor_type=actor_type, actor_kwargs=actor_kwargs, actor_scheduling=actor_scheduling, - max_restarts=max_restarts, ) log( @@ -279,9 +273,7 @@ def start_simulation( # pylint: disable=too-many-arguments "not enough for your workload). Use fewer concurrent actors. " "\n\t\t - You were running a multi-node simulation and all worker nodes " "disconnected. The head node might still be alive but cannot accommodate " - f"any actor with resources: {client_resources}." - "\n\t\t - Your Actors crashed because of an unknown reason and all their " - f"restarts attempts ({max_restarts=}) have been exhausted.", + f"any actor with resources: {client_resources}.", ) hist = None diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index ee4be1b4923a..e64d96a6f55a 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -19,13 +19,19 @@ import traceback from abc import ABC from logging import ERROR, WARNING -from typing import Any, Callable, Dict, List, Set, Union +from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union import ray from ray.util.actor_pool import ActorPool +from flwr import common from flwr.common.logger import log +# All possible returns by a client +ClientRes = Union[ + common.GetPropertiesRes, common.GetParametersRes, common.FitRes, common.EvaluateRes +] + class ClientException(Exception): """Raised when client side logic crashes with an exception.""" @@ -38,14 +44,14 @@ def __init__(self, message: str): class VirtualClientEngineActor(ABC): """Abstract base class for VirtualClientEngine Actors.""" - def terminate(self): + def terminate(self) -> None: """Manually terminate Actor object.""" log(WARNING, f"Manually terminating {self.__class__.__name__}") ray.actor.exit_actor() - def run(self, job_fn: Callable, cid: str): + def run(self, job_fn: Callable[[], ClientRes], cid: str) -> Tuple[str, Any]: """Run a client workload.""" - # execute tasks and return result + # Execute tasks and return result # return also cid which is needed to ensure results # from the pool are correctly assigned to each ClientProxy try: @@ -75,7 +81,7 @@ class DefaultActor_TF(VirtualClientEngineActor): It enables GPU memory growth to prevent premature OOM. """ - def __init__(self): + def __init__(self) -> None: super().__init__() # By default, TF maps all GPU memory to the process. # We don't this behaviour in simulation, since it prevents us @@ -88,7 +94,7 @@ def __init__(self): try: import tensorflow as tf - # this bit of code follows the guidelines for GPU usage + # This bit of code follows the guidelines for GPU usage # in https://www.tensorflow.org/guide/gpu gpus = tf.config.list_physical_devices("GPU") if gpus: @@ -104,19 +110,19 @@ def __init__(self): raise e -def pool_size_from_resources(client_resources: Dict): +def pool_size_from_resources(client_resources: Dict) -> int: """Calculate number of Actors that fit in pool given the resources in the. cluster and those required per client. """ cluster_resources = ray.cluster_resources() num_cpus = cluster_resources["CPU"] - num_gpus = cluster_resources.get("GPU", 0) # there might not be GPU + num_gpus = cluster_resources.get("GPU", 0) # There might not be GPU num_actors = int(num_cpus / client_resources["num_cpus"]) - # if a GPU is present and client resources do require one + # If a GPU is present and client resources do require one if "num_gpus" in client_resources.keys() and client_resources["num_gpus"] > 0.0: if num_gpus: - # if there are gpus in the cluster + # If there are gpus in the cluster num_actors = min(num_actors, int(num_gpus / client_resources["num_gpus"])) else: num_actors = 0 @@ -144,22 +150,19 @@ def __init__( actor_type: VirtualClientEngineActor, actor_kwargs: Dict[str, Any], actor_scheduling: str, - max_restarts: int, ): self.client_resources = client_resources self.actor_type = actor_type self.actor_kwargs = actor_kwargs self.actor_scheduling = actor_scheduling - self.actor_max_restarts = max_restarts num_actors = pool_size_from_resources(client_resources) args = actor_kwargs if actor_kwargs is not None else {} actors = [ - actor_type.options( + actor_type.options( # type: ignore **client_resources, scheduling_strategy=actor_scheduling, - max_restarts=max_restarts, ).remote(**args) for _ in range(num_actors) ] @@ -168,23 +171,19 @@ def __init__( # A dict that maps cid to another dict containing: a reference to the remote job # and its status (i.e. whether it is ready or not) - self._cid_to_future: Dict[str, Dict[str, Any]] = {} + self._cid_to_future: Dict[str, Dict[str, Union[bool, Any]]] = {} self.actor_to_remove: Set[str] = set() # a set self.num_actors = len(actors) self.lock = threading.RLock() - # TODO: asyncio check every N seconds if cluster has grown - # --> add more actors to the pool if so - - def __reduce__(self): + def __reduce__(self): # type: ignore """Make this class serialisable (needed due to lock).""" return VirtualClientEngineActorPool, ( self.client_resources, self.actor_type, self.actor_kwargs, self.actor_scheduling, - self.actor_max_restarts, ) def submit(self, fn: Any, job_fn: Callable, cid: str) -> None: @@ -204,17 +203,19 @@ def submit_client_job(self, fn: Any, job_fn: Callable, cid: str) -> None: # We need to put this behind a lock since .submit() involves # removing and adding elements from a dictionary. Which creates # issues in multi-threaded settings + with self.lock: - # creating cid to future mapping + #TODO: w/ timestamp check, call ray.resources() and add more actors to pool if more resources available + # Creating cid to future mapping self._reset_cid_to_future_dict(cid) if self._idle_actors: - # submit job since there is an Actor that's available + # Submit job since there is an Actor that's available self.submit(fn, job_fn, cid) else: - # no actors are available, append to list of jobs to run later + # No actors are available, append to list of jobs to run later self._pending_submits.append((fn, job_fn, cid)) - def _flag_future_as_ready(self, cid) -> None: + def _flag_future_as_ready(self, cid: str) -> None: """Flag future for VirtualClient with cid=cid as ready.""" self._cid_to_future[cid]["ready"] = True @@ -233,7 +234,7 @@ def _is_future_ready(self, cid: str) -> bool: else: return self._cid_to_future[cid]["ready"] - def _fetch_future_result(self, cid: str) -> Any: + def _fetch_future_result(self, cid: str) -> ClientRes: """Fetch result for VirtualClient from Object Store.""" try: res_cid, res = ray.get(self._cid_to_future[cid]["future"]) @@ -245,12 +246,12 @@ def _fetch_future_result(self, cid: str) -> Any: self._flag_actor_for_removal(ex.actor_id) raise ex - # sanity check: was the result fetched generated by a client with cid=cid? - assert res_cid != res, log( + # Sanity check: was the result fetched generated by a client with cid=cid? + assert res_cid == cid, log( ERROR, f"The VirtualClient {cid} got result from client {res_cid}" ) - # reset mapping + # Reset mapping self._reset_cid_to_future_dict(cid) return res @@ -269,10 +270,10 @@ def _check_and_remove_actor_from_pool( Remove the actor if so. """ with self.lock: - actor_id = actor._actor_id.hex() - # print(f"{self.actor_to_remove = }") + actor_id = actor._actor_id.hex() # type: ignore + if actor_id in self.actor_to_remove: - # the actor should be removed + # The actor should be removed self.actor_to_remove.remove(actor_id) self.num_actors -= 1 log(WARNING, f"REMOVED actor {actor_id} from pool") @@ -296,7 +297,7 @@ def _check_actor_fits_in_pool(self) -> bool: f" reduced from {self.num_actors} down to {num_actors_updated}. This" " might take several intermediate steps", ) - # we are preventing one actor to be added back in the queue, so we just + # We are preventing one actor to be added back in the queue, so we just # decrease the number of actors by one. Eventually `self.num_actors` # should be equal what pool_size_from_resources(self.resources) returns self.num_actors -= 1 @@ -304,9 +305,9 @@ def _check_actor_fits_in_pool(self) -> bool: else: return True - def process_unordered_future(self, timeout=None) -> None: + def process_unordered_future(self, timeout: Optional[float] = None) -> None: """Similar to parent's get_next_unordered() but without final ray.get().""" - if not self.has_next(): + if not self.has_next(): # type: ignore raise StopIteration("No more results to get") res, _ = ray.wait(list(self._future_to_actor), num_returns=1, timeout=timeout) @@ -316,29 +317,29 @@ def process_unordered_future(self, timeout=None) -> None: raise TimeoutError("Timed out waiting for result") with self.lock: - # get actor that completed a job + # Get actor that completed a job _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) if a is not None: - # still space in queue ? (no if a node died) + # Still space in queue ? (no if a node died) if self._check_actor_fits_in_pool(): if self._check_and_remove_actor_from_pool(a): - self._return_actor(a) - # flag future as ready + self._return_actor(a) # type: ignore + # Flag future as ready self._flag_future_as_ready(cid) else: - # the actor doesn't fit in the pool anymore. + # The actor doesn't fit in the pool anymore. # Manually terminate the actor a.terminate.remote() - def get_client_result(self, cid: str, timeout: int = 3600) -> Any: + def get_client_result(self, cid: str, timeout: Optional[float]) -> ClientRes: """Get result from VirtualClient with specific cid.""" - # loop until all jobs submitted to the pool are completed. Break early + # Loop until all jobs submitted to the pool are completed. Break early # if the result for the ClientProxy calling this method is ready - while self.has_next() and not (self._is_future_ready(cid)): + while self.has_next() and not (self._is_future_ready(cid)): # type: ignore try: self.process_unordered_future(timeout=timeout) except StopIteration: - # there are no pending jobs in the pool + # There are no pending jobs in the pool break # Fetch result belonging to the VirtualClient calling this method From 9b2d27e473728040583eb235fbef0741c227e398 Mon Sep 17 00:00:00 2001 From: javier Date: Wed, 16 Aug 2023 14:19:26 +0000 Subject: [PATCH 029/133] tweaks, fixes for serialisation --- src/py/flwr/simulation/app.py | 31 +---- .../simulation/ray_transport/ray_actor.py | 111 ++++++++++-------- 2 files changed, 68 insertions(+), 74 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index c038aed8e594..d185af74076f 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -137,9 +137,7 @@ def start_simulation( # pylint: disable=too-many-arguments Optionally specify the type of actor to use. The actor object, which persist throughout the simulation will be the process in charged of - running the clients' jobs (i.e. their fit() method). If you are using - Tensorflow, you should use type `DefaultActor_TF` which will set TF's - GPU memory growth to True at initialisation (preventing premature OOM). + running the clients' jobs (i.e. their fit() method). actor_kwargs: Optional[Dict[str, Any]] (default: None) If you want to create your own Actor classes, you might need to pass @@ -253,29 +251,10 @@ def start_simulation( # pylint: disable=too-many-arguments initialized_server.client_manager().register(client=client_proxy) # Start training - try: - hist = run_fl( - server=initialized_server, - config=initialized_config, - ) - except Exception as ex: - log(ERROR, ex) - log( - ERROR, - "Your simulation crashed :(. This could be because of several reasons." - "The most common are: " - "\n\t > Your system couldn't fit a single VirtualClient: try lowering " - "`client_resources`." - "\n\t > All the actors in your pool crashed. This could be because: " - "\n\t\t - You clients hit an out-of-memory (OOM) error and actors couldn't " - "recover from it. Try launching your simulation with more generous " - f"`client_resources` setting (i.e. it seems {client_resources} is " - "not enough for your workload). Use fewer concurrent actors. " - "\n\t\t - You were running a multi-node simulation and all worker nodes " - "disconnected. The head node might still be alive but cannot accommodate " - f"any actor with resources: {client_resources}.", - ) - hist = None + hist = run_fl( + server=initialized_server, + config=initialized_config, + ) event(EventType.START_SIMULATION_LEAVE) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index e64d96a6f55a..ff20cf8639ae 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -23,6 +23,7 @@ import ray from ray.util.actor_pool import ActorPool +from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy from flwr import common from flwr.common.logger import log @@ -74,42 +75,6 @@ class DefaultActor(VirtualClientEngineActor): """A Ray Actor class that runs client workloads.""" -@ray.remote -class DefaultActor_TF(VirtualClientEngineActor): - """A Ray Actor class that runs TF client workloads. - - It enables GPU memory growth to prevent premature OOM. - """ - - def __init__(self) -> None: - super().__init__() - # By default, TF maps all GPU memory to the process. - # We don't this behaviour in simulation, since it prevents us - # from having multiple Actors (and therefore Flower clients) sharing - # the same GPU. - # Luckily we can disable this behaviour by enabling memory growth - # on the GPU. In this way, VRAM allocated to the processes grows based - # on the needs for the workload. (this is for instance the default - # behaviour in Pytorch) - try: - import tensorflow as tf - - # This bit of code follows the guidelines for GPU usage - # in https://www.tensorflow.org/guide/gpu - gpus = tf.config.list_physical_devices("GPU") - if gpus: - try: - # Currently, memory growth needs to be the same across GPUs - for gpu in gpus: - tf.config.experimental.set_memory_growth(gpu, True) - except RuntimeError as e: - # Memory growth must be set before GPUs have been initialized - print(e) - except Exception as e: - log(ERROR, "Do you have Tensorflow installed?") - raise e - - def pool_size_from_resources(client_resources: Dict) -> int: """Calculate number of Actors that fit in pool given the resources in the. @@ -142,14 +107,48 @@ def pool_size_from_resources(client_resources: Dict) -> int: class VirtualClientEngineActorPool(ActorPool): - """A pool of VirtualClientEngine Actors.""" + """A pool of VirtualClientEngine Actors. + + + Parameters + ---------- + client_resources : Dict[str, Union[int, float]] + A dictionary specifying the system resources that each + actor should have access. This will be used to calculate + the number of actors that fit in your cluster. Supported keys + are `num_cpus` and `num_gpus`. E.g. {`num_cpus`: 2, `num_gpus`: 0.5} + would allocate two Actors per GPU in your system assuming you have + enough CPUs. To understand the GPU utilization caused by `num_gpus`, + as well as using custom resources, please consult the Ray documentation. + + actor_type : VirtualClientEngineActor + A class defining how your Actor behaves. It should have the @ray.remote + decorator. Recall actors run workloads of virtual/simulated clients. In + this way, an actor would first "spawn" a particular client, then run the + method that indicated by the strategy (e.g. `fit()`), then it will return + the results back to the client proxy. + + actor_kwargs : Dict[str, Any] + If you implement your own Actor class, you might want to pass it some arguments + for initialization. You should use this argument to achieve so. + + actor_scheduling : Union[str, NodeAffinitySchedulingStrategy] + This allows you to control how Actors are scheduled/placed in the nodes + available to your simulation. + + actor_lists: List[VirtualClientEngineActor] (default: None) + This argument should not be used. It's only needed for serialization purposes + (see the `__reduce__` method). Each time it is executed, we want to retain + the same list of actors. + """ def __init__( self, client_resources: Dict[str, Union[int, float]], actor_type: VirtualClientEngineActor, actor_kwargs: Dict[str, Any], - actor_scheduling: str, + actor_scheduling: Union[str, NodeAffinitySchedulingStrategy], + actor_list: List[VirtualClientEngineActor] = None, ): self.client_resources = client_resources self.actor_type = actor_type @@ -159,13 +158,18 @@ def __init__( args = actor_kwargs if actor_kwargs is not None else {} - actors = [ - actor_type.options( # type: ignore - **client_resources, - scheduling_strategy=actor_scheduling, - ).remote(**args) - for _ in range(num_actors) - ] + if actor_list is None: + # When __reduce__ is executed, we don't want to created + # a new list of actors again. + actors = [ + actor_type.options( # type: ignore + **client_resources, + scheduling_strategy=actor_scheduling, + ).remote(**args) + for _ in range(num_actors) + ] + else: + actors = actor_list super().__init__(actors) @@ -184,6 +188,7 @@ def __reduce__(self): # type: ignore self.actor_type, self.actor_kwargs, self.actor_scheduling, + self._idle_actors, # Pass existing actors to avoid killing/re-creating ) def submit(self, fn: Any, job_fn: Callable, cid: str) -> None: @@ -230,12 +235,19 @@ def _reset_cid_to_future_dict(self, cid: str) -> None: def _is_future_ready(self, cid: str) -> bool: """Return status of future associated to the given client id (cid).""" if cid not in self._cid_to_future.keys(): + # With the current ClientProxy<-->ActorPool interaction + # we should never be hitting this condition. + log(WARNING, "This shouldn't be happening") return False else: return self._cid_to_future[cid]["ready"] def _fetch_future_result(self, cid: str) -> ClientRes: - """Fetch result for VirtualClient from Object Store.""" + """Fetch result for VirtualClient from Object Store. + + The job submitted by the ClientProxy interfacing with client with + cid=cid is ready. Here we fetch it from the object store and return. + """ try: res_cid, res = ray.get(self._cid_to_future[cid]["future"]) except ray.exceptions.RayActorError as ex: @@ -309,6 +321,7 @@ def process_unordered_future(self, timeout: Optional[float] = None) -> None: """Similar to parent's get_next_unordered() but without final ray.get().""" if not self.has_next(): # type: ignore raise StopIteration("No more results to get") + # Block until one result is ready res, _ = ray.wait(list(self._future_to_actor), num_returns=1, timeout=timeout) if res: @@ -320,11 +333,13 @@ def process_unordered_future(self, timeout: Optional[float] = None) -> None: # Get actor that completed a job _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) if a is not None: - # Still space in queue ? (no if a node died) + # Still space in queue ? (no if a node in the cluster died) if self._check_actor_fits_in_pool(): if self._check_and_remove_actor_from_pool(a): self._return_actor(a) # type: ignore - # Flag future as ready + # Flag future as ready so ClientProxy with cid + # can break from the while loop (in `get_client_result()`) + # and fetch its result self._flag_future_as_ready(cid) else: # The actor doesn't fit in the pool anymore. From ff48ee144d1892003facd346f97b43b851cca2f0 Mon Sep 17 00:00:00 2001 From: javier Date: Wed, 16 Aug 2023 19:48:23 +0000 Subject: [PATCH 030/133] types and more --- src/py/flwr/simulation/app.py | 18 ++++-- .../simulation/ray_transport/ray_actor.py | 56 +++++++++++-------- .../ray_transport/ray_client_proxy.py | 15 +++-- 3 files changed, 56 insertions(+), 33 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 4d880f58e679..c6d8d29c2901 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -77,9 +77,9 @@ def start_simulation( # pylint: disable=too-many-arguments client_manager: Optional[ClientManager] = None, ray_init_args: Optional[Dict[str, Any]] = None, keep_initialised: Optional[bool] = False, - actor_type: Optional[VirtualClientEngineActor] = DefaultActor, + actor_type: type[VirtualClientEngineActor] = DefaultActor, actor_kwargs: Optional[Dict[str, Any]] = None, - actor_scheduling: Optional[Union[str, NodeAffinitySchedulingStrategy]] = "DEFAULT", + actor_scheduling: Union[str, NodeAffinitySchedulingStrategy] = "DEFAULT", ) -> History: """Start a Ray-based Flower simulation server. @@ -133,7 +133,7 @@ def start_simulation( # pylint: disable=too-many-arguments keep_initialised: Optional[bool] (default: False) Set to True to prevent `ray.shutdown()` in case `ray.is_initialized()=True`. - actor_type: Optional[VirtualClientEngineActor] (default: DefaultActor) + actor_type: VirtualClientEngineActor (default: DefaultActor) Optionally specify the type of actor to use. The actor object, which persist throughout the simulation will be the process in charged of @@ -264,9 +264,15 @@ def start_simulation( # pylint: disable=too-many-arguments "Your simulation crashed :(. This could be because of several reasons." "The most common are: " "\n\t > Your system couldn't fit a single VirtualClient: try lowering " - "`client_resources`. You used: %s" - "\n\t > Too many VirtualClients were spawned causing an issue: try raising " - "`client_resources`. You used: %s", + "`client_resources`." + "\n\t > All the actors in your pool crashed. This could be because: " + "\n\t\t - You clients hit an out-of-memory (OOM) error and actors couldn't " + "recover from it. Try launching your simulation with more generous " + "`client_resources` setting (i.e. it seems %s is " + "not enough for your workload). Use fewer concurrent actors. " + "\n\t\t - You were running a multi-node simulation and all worker nodes " + "disconnected. The head node might still be alive but cannot accommodate " + "any actor with resources: %s.", client_resources, client_resources, ) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index ff20cf8639ae..11d8f0bc6568 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -22,6 +22,7 @@ from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union import ray +from ray import ObjectRef from ray.util.actor_pool import ActorPool from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy @@ -50,7 +51,7 @@ def terminate(self) -> None: log(WARNING, f"Manually terminating {self.__class__.__name__}") ray.actor.exit_actor() - def run(self, job_fn: Callable[[], ClientRes], cid: str) -> Tuple[str, Any]: + def run(self, job_fn: Callable[[], ClientRes], cid: str) -> Tuple[str, ClientRes]: """Run a client workload.""" # Execute tasks and return result # return also cid which is needed to ensure results @@ -75,7 +76,7 @@ class DefaultActor(VirtualClientEngineActor): """A Ray Actor class that runs client workloads.""" -def pool_size_from_resources(client_resources: Dict) -> int: +def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> int: """Calculate number of Actors that fit in pool given the resources in the. cluster and those required per client. @@ -108,8 +109,7 @@ def pool_size_from_resources(client_resources: Dict) -> int: class VirtualClientEngineActorPool(ActorPool): """A pool of VirtualClientEngine Actors. - - + Parameters ---------- client_resources : Dict[str, Union[int, float]] @@ -127,7 +127,7 @@ class VirtualClientEngineActorPool(ActorPool): this way, an actor would first "spawn" a particular client, then run the method that indicated by the strategy (e.g. `fit()`), then it will return the results back to the client proxy. - + actor_kwargs : Dict[str, Any] If you implement your own Actor class, you might want to pass it some arguments for initialization. You should use this argument to achieve so. @@ -135,7 +135,7 @@ class VirtualClientEngineActorPool(ActorPool): actor_scheduling : Union[str, NodeAffinitySchedulingStrategy] This allows you to control how Actors are scheduled/placed in the nodes available to your simulation. - + actor_lists: List[VirtualClientEngineActor] (default: None) This argument should not be used. It's only needed for serialization purposes (see the `__reduce__` method). Each time it is executed, we want to retain @@ -145,10 +145,10 @@ class VirtualClientEngineActorPool(ActorPool): def __init__( self, client_resources: Dict[str, Union[int, float]], - actor_type: VirtualClientEngineActor, - actor_kwargs: Dict[str, Any], + actor_type: type[VirtualClientEngineActor], + actor_kwargs: Optional[Dict[str, Any]], actor_scheduling: Union[str, NodeAffinitySchedulingStrategy], - actor_list: List[VirtualClientEngineActor] = None, + actor_list: Optional[List[VirtualClientEngineActor]] = None, ): self.client_resources = client_resources self.actor_type = actor_type @@ -175,24 +175,32 @@ def __init__( # A dict that maps cid to another dict containing: a reference to the remote job # and its status (i.e. whether it is ready or not) - self._cid_to_future: Dict[str, Dict[str, Union[bool, Any]]] = {} + self._cid_to_future: Dict[ + str, Dict[str, Union[bool, Optional[ObjectRef[Any]]]] + ] = {} self.actor_to_remove: Set[str] = set() # a set self.num_actors = len(actors) self.lock = threading.RLock() def __reduce__(self): # type: ignore - """Make this class serialisable (needed due to lock).""" + """Make this class serializable (needed due to lock).""" return VirtualClientEngineActorPool, ( self.client_resources, self.actor_type, self.actor_kwargs, self.actor_scheduling, - self._idle_actors, # Pass existing actors to avoid killing/re-creating + self._idle_actors, # Pass existing actors to avoid killing/re-creating ) - def submit(self, fn: Any, job_fn: Callable, cid: str) -> None: - """Take idle actor and assign it a client workload.""" + def submit( # type: ignore[override] + self, fn: Any, job_fn: Callable[[], ClientRes], cid: str + ) -> None: + """Take idle actor and assign it a client workload. + + Submit a job to an actor by first removing it from the list of idle actors, then + check if this actor was flagged to be removed from the pool + """ actor = self._idle_actors.pop() if self._check_and_remove_actor_from_pool(actor): future = fn(actor, job_fn) @@ -203,14 +211,16 @@ def submit(self, fn: Any, job_fn: Callable, cid: str) -> None: # update with future self._cid_to_future[cid]["future"] = future_key - def submit_client_job(self, fn: Any, job_fn: Callable, cid: str) -> None: + def submit_client_job( + self, fn: Any, job_fn: Callable[[], ClientRes], cid: str + ) -> None: """Submit a job while tracking client ids.""" # We need to put this behind a lock since .submit() involves # removing and adding elements from a dictionary. Which creates # issues in multi-threaded settings with self.lock: - #TODO: w/ timestamp check, call ray.resources() and add more actors to pool if more resources available + # TODO: w/ timestamp check, call ray.resources() # Creating cid to future mapping self._reset_cid_to_future_dict(cid) if self._idle_actors: @@ -240,16 +250,18 @@ def _is_future_ready(self, cid: str) -> bool: log(WARNING, "This shouldn't be happening") return False else: - return self._cid_to_future[cid]["ready"] + is_ready: bool = self._cid_to_future[cid]["ready"] # type: ignore + return is_ready def _fetch_future_result(self, cid: str) -> ClientRes: """Fetch result for VirtualClient from Object Store. - - The job submitted by the ClientProxy interfacing with client with - cid=cid is ready. Here we fetch it from the object store and return. + + The job submitted by the ClientProxy interfacing with client with cid=cid is + ready. Here we fetch it from the object store and return. """ try: - res_cid, res = ray.get(self._cid_to_future[cid]["future"]) + future: ObjectRef[Any] = self._cid_to_future[cid]["future"] # type: ignore + res_cid, res = ray.get(future) # type: (str, ClientRes) except ray.exceptions.RayActorError as ex: log(ERROR, ex) if hasattr(ex, "actor_id"): @@ -350,7 +362,7 @@ def get_client_result(self, cid: str, timeout: Optional[float]) -> ClientRes: """Get result from VirtualClient with specific cid.""" # Loop until all jobs submitted to the pool are completed. Break early # if the result for the ClientProxy calling this method is ready - while self.has_next() and not (self._is_future_ready(cid)): # type: ignore + while self.has_next() and not self._is_future_ready(cid): # type: ignore try: self.process_unordered_future(timeout=timeout) except StopIteration: diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index 6c9d4d7cd036..fe34dde73a7c 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -17,7 +17,7 @@ import traceback from logging import ERROR -from typing import Callable, Dict, Optional, cast +from typing import Callable, Dict, Optional, Union, cast import ray @@ -34,6 +34,9 @@ from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActorPool ClientFn = Callable[[str], ClientLike] +ClientRes = Union[ + common.GetPropertiesRes, common.GetParametersRes, common.FitRes, common.EvaluateRes +] class RayClientProxy(ClientProxy): @@ -127,7 +130,9 @@ def __init__( self.client_fn = client_fn self.actor_pool = actor_pool - def _submit_job(self, job_fn: Callable, timeout: Optional[float]): + def _submit_job( + self, job_fn: Callable[[], ClientRes], timeout: Optional[float] + ) -> ClientRes: try: self.actor_pool.submit_client_job( lambda a, v: a.run.remote(v, self.cid), job_fn, self.cid @@ -138,9 +143,9 @@ def _submit_job(self, job_fn: Callable, timeout: Optional[float]): if self.actor_pool.num_actors == 0: # At this point we want to stop the simulation. # since no more client workloads will be executed - log(ERROR, "ActorPool is empty!!! Disconnecting VirtualClient") - # TODO: the below does nothing? - self.reconnect() + log(ERROR, "ActorPool is empty!!!") + # TODO: Figure out how to disconnect ClientProxy + # self.reconnect() log(ERROR, traceback.format_exc()) log(ERROR, ex) raise ex From 400e89d205b9a604df694ff8201bddb4328cc6cd Mon Sep 17 00:00:00 2001 From: javier Date: Wed, 16 Aug 2023 19:54:23 +0000 Subject: [PATCH 031/133] w/ previous --- src/py/flwr/simulation/app.py | 4 ++-- src/py/flwr/simulation/ray_transport/ray_actor.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index c6d8d29c2901..5e9a56170f17 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -17,7 +17,7 @@ import sys from logging import ERROR, INFO -from typing import Any, Callable, Dict, List, Optional, Union +from typing import Any, Callable, Dict, List, Optional, Type, Union import ray from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy @@ -77,7 +77,7 @@ def start_simulation( # pylint: disable=too-many-arguments client_manager: Optional[ClientManager] = None, ray_init_args: Optional[Dict[str, Any]] = None, keep_initialised: Optional[bool] = False, - actor_type: type[VirtualClientEngineActor] = DefaultActor, + actor_type: Type[VirtualClientEngineActor] = DefaultActor, actor_kwargs: Optional[Dict[str, Any]] = None, actor_scheduling: Union[str, NodeAffinitySchedulingStrategy] = "DEFAULT", ) -> History: diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 11d8f0bc6568..c12336e68de3 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -19,7 +19,7 @@ import traceback from abc import ABC from logging import ERROR, WARNING -from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union +from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union import ray from ray import ObjectRef @@ -145,7 +145,7 @@ class VirtualClientEngineActorPool(ActorPool): def __init__( self, client_resources: Dict[str, Union[int, float]], - actor_type: type[VirtualClientEngineActor], + actor_type: Type[VirtualClientEngineActor], actor_kwargs: Optional[Dict[str, Any]], actor_scheduling: Union[str, NodeAffinitySchedulingStrategy], actor_list: Optional[List[VirtualClientEngineActor]] = None, From ed5b01df9af42464181af4965f1b2cf91366ce73 Mon Sep 17 00:00:00 2001 From: javier Date: Wed, 16 Aug 2023 20:11:34 +0000 Subject: [PATCH 032/133] p37 --- .../simulation/ray_transport/ray_actor.py | 35 ++++++++++++------- 1 file changed, 22 insertions(+), 13 deletions(-) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index c12336e68de3..34151e71f87a 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -39,7 +39,8 @@ class ClientException(Exception): """Raised when client side logic crashes with an exception.""" def __init__(self, message: str): - self.message = f"\n{'>'*7} A ClientException occurred." + message + div = ">" * 7 + self.message = "\n" + div + "A ClientException occurred." + message super().__init__(self.message) @@ -48,7 +49,7 @@ class VirtualClientEngineActor(ABC): def terminate(self) -> None: """Manually terminate Actor object.""" - log(WARNING, f"Manually terminating {self.__class__.__name__}") + log(WARNING, "Manually terminating %s}", self.__class__.__name__) ray.actor.exit_actor() def run(self, job_fn: Callable[[], ClientRes], cid: str) -> Tuple[str, ClientRes]: @@ -62,11 +63,14 @@ def run(self, job_fn: Callable[[], ClientRes], cid: str) -> Tuple[str, ClientRes client_trace = traceback.format_exc() message = ( "\n\tSomething went wrong when running your client workload." - f"\n\tClient {cid} crashed when the {self.__class__.__name__}" - " was running its workload." - f"\n\tException triggered on the client side: {client_trace}" + "\n\tClient " + + cid + + " crashed when the " + + self.__class__.__name__ + + " was running its workload." + "\n\tException triggered on the client side: " + client_trace, ) - raise ClientException(message) from ex + raise ClientException(str(message)) from ex return cid, job_results @@ -96,9 +100,12 @@ def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> if num_actors == 0: log( WARNING, - f"Your ActorPool is empty. Your system ({num_cpus = }, {num_gpus = }) " + "Your ActorPool is empty. Your system (%s, %s) " "does not meet the criteria to host at least one client with resources:" - f" {client_resources}. Consider lowering your `client_resources`", + " %s. Consider lowering your `client_resources`", + num_cpus, + num_gpus, + client_resources, ) raise ValueError( "ActorPool is empty. Stopping Simulation. Check 'client_resources'" @@ -272,7 +279,7 @@ def _fetch_future_result(self, cid: str) -> ClientRes: # Sanity check: was the result fetched generated by a client with cid=cid? assert res_cid == cid, log( - ERROR, f"The VirtualClient {cid} got result from client {res_cid}" + ERROR, "The VirtualClient %s got result from client %s", cid, res_cid ) # Reset mapping @@ -284,7 +291,7 @@ def _flag_actor_for_removal(self, actor_id_hex: str) -> None: """Flag actor that should be removed from pool.""" with self.lock: self.actor_to_remove.add(actor_id_hex) - log(WARNING, f"Actor({actor_id_hex}) will be remove from pool.") + log(WARNING, "Actor(%s) will be remove from pool.", actor_id_hex) def _check_and_remove_actor_from_pool( self, actor: VirtualClientEngineActor @@ -300,8 +307,8 @@ def _check_and_remove_actor_from_pool( # The actor should be removed self.actor_to_remove.remove(actor_id) self.num_actors -= 1 - log(WARNING, f"REMOVED actor {actor_id} from pool") - log(WARNING, f"Pool size: {self.num_actors}") + log(WARNING, "REMOVED actor %s from pool", actor_id) + log(WARNING, "Pool size: %s", self.num_actors) return False else: return True @@ -318,8 +325,10 @@ def _check_actor_fits_in_pool(self) -> bool: log( WARNING, "Cluster resources have changed. Number of actors in the pool should be" - f" reduced from {self.num_actors} down to {num_actors_updated}. This" + " reduced from %s down to %s. This" " might take several intermediate steps", + self.num_actors, + num_actors_updated, ) # We are preventing one actor to be added back in the queue, so we just # decrease the number of actors by one. Eventually `self.num_actors` From fcb42a16aa50c2bb6fcb3185e207137044184688 Mon Sep 17 00:00:00 2001 From: javier Date: Wed, 16 Aug 2023 20:36:37 +0000 Subject: [PATCH 033/133] yes --- .../simulation/ray_transport/ray_actor.py | 49 ++++++++++--------- .../ray_transport/ray_client_proxy.py | 4 +- 2 files changed, 28 insertions(+), 25 deletions(-) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 34151e71f87a..0c7ba38cf012 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -114,7 +114,9 @@ def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> return num_actors -class VirtualClientEngineActorPool(ActorPool): +class VirtualClientEngineActorPool( + ActorPool +): # pylint: disable=[too-many-instance-attributes, too-many-arguments] """A pool of VirtualClientEngine Actors. Parameters @@ -200,14 +202,13 @@ def __reduce__(self): # type: ignore self._idle_actors, # Pass existing actors to avoid killing/re-creating ) - def submit( # type: ignore[override] - self, fn: Any, job_fn: Callable[[], ClientRes], cid: str - ) -> None: + def submit(self, fn: Any, value: Tuple[Callable[[], ClientRes], str]) -> None: """Take idle actor and assign it a client workload. Submit a job to an actor by first removing it from the list of idle actors, then check if this actor was flagged to be removed from the pool """ + job_fn, cid = value actor = self._idle_actors.pop() if self._check_and_remove_actor_from_pool(actor): future = fn(actor, job_fn) @@ -219,23 +220,24 @@ def submit( # type: ignore[override] self._cid_to_future[cid]["future"] = future_key def submit_client_job( - self, fn: Any, job_fn: Callable[[], ClientRes], cid: str + self, actor_fn: Any, job: Tuple[Callable[[], ClientRes], str] ) -> None: """Submit a job while tracking client ids.""" # We need to put this behind a lock since .submit() involves # removing and adding elements from a dictionary. Which creates # issues in multi-threaded settings + _, cid = job with self.lock: - # TODO: w/ timestamp check, call ray.resources() + # TODO: w/ timestamp check, call ray.resources() # pylint: disable=fixme # Creating cid to future mapping self._reset_cid_to_future_dict(cid) if self._idle_actors: # Submit job since there is an Actor that's available - self.submit(fn, job_fn, cid) + self.submit(actor_fn, job) else: # No actors are available, append to list of jobs to run later - self._pending_submits.append((fn, job_fn, cid)) + self._pending_submits.append((actor_fn, job)) def _flag_future_as_ready(self, cid: str) -> None: """Flag future for VirtualClient with cid=cid as ready.""" @@ -243,7 +245,7 @@ def _flag_future_as_ready(self, cid: str) -> None: def _reset_cid_to_future_dict(self, cid: str) -> None: """Reset cid:future mapping info.""" - if cid not in self._cid_to_future.keys(): + if cid not in self._cid_to_future: self._cid_to_future[cid] = {} self._cid_to_future[cid]["future"] = None @@ -251,14 +253,13 @@ def _reset_cid_to_future_dict(self, cid: str) -> None: def _is_future_ready(self, cid: str) -> bool: """Return status of future associated to the given client id (cid).""" - if cid not in self._cid_to_future.keys(): + if cid not in self._cid_to_future: # With the current ClientProxy<-->ActorPool interaction # we should never be hitting this condition. log(WARNING, "This shouldn't be happening") return False - else: - is_ready: bool = self._cid_to_future[cid]["ready"] # type: ignore - return is_ready + + return self._cid_to_future[cid]["ready"] # type: ignore def _fetch_future_result(self, cid: str) -> ClientRes: """Fetch result for VirtualClient from Object Store. @@ -301,7 +302,9 @@ def _check_and_remove_actor_from_pool( Remove the actor if so. """ with self.lock: - actor_id = actor._actor_id.hex() # type: ignore + actor_id = ( + actor._actor_id.hex() # type: ignore # pylint: disable=protected-access + ) if actor_id in self.actor_to_remove: # The actor should be removed @@ -310,8 +313,8 @@ def _check_and_remove_actor_from_pool( log(WARNING, "REMOVED actor %s from pool", actor_id) log(WARNING, "Pool size: %s", self.num_actors) return False - else: - return True + + return True def _check_actor_fits_in_pool(self) -> bool: """Determine if available resources haven't changed. @@ -335,8 +338,8 @@ def _check_actor_fits_in_pool(self) -> bool: # should be equal what pool_size_from_resources(self.resources) returns self.num_actors -= 1 return False - else: - return True + + return True def process_unordered_future(self, timeout: Optional[float] = None) -> None: """Similar to parent's get_next_unordered() but without final ray.get().""" @@ -352,12 +355,12 @@ def process_unordered_future(self, timeout: Optional[float] = None) -> None: with self.lock: # Get actor that completed a job - _, a, cid = self._future_to_actor.pop(future, (None, None, -1)) - if a is not None: + _, actor, cid = self._future_to_actor.pop(future, (None, None, -1)) + if actor is not None: # Still space in queue ? (no if a node in the cluster died) if self._check_actor_fits_in_pool(): - if self._check_and_remove_actor_from_pool(a): - self._return_actor(a) # type: ignore + if self._check_and_remove_actor_from_pool(actor): + self._return_actor(actor) # type: ignore # Flag future as ready so ClientProxy with cid # can break from the while loop (in `get_client_result()`) # and fetch its result @@ -365,7 +368,7 @@ def process_unordered_future(self, timeout: Optional[float] = None) -> None: else: # The actor doesn't fit in the pool anymore. # Manually terminate the actor - a.terminate.remote() + actor.terminate.remote() def get_client_result(self, cid: str, timeout: Optional[float]) -> ClientRes: """Get result from VirtualClient with specific cid.""" diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index fe34dde73a7c..dcdcaefc377e 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -135,7 +135,7 @@ def _submit_job( ) -> ClientRes: try: self.actor_pool.submit_client_job( - lambda a, v: a.run.remote(v, self.cid), job_fn, self.cid + lambda a, v: a.run.remote(v, self.cid), (job_fn, self.cid) ) res = self.actor_pool.get_client_result(self.cid, timeout) @@ -144,7 +144,7 @@ def _submit_job( # At this point we want to stop the simulation. # since no more client workloads will be executed log(ERROR, "ActorPool is empty!!!") - # TODO: Figure out how to disconnect ClientProxy + # TODO: Implement ClientProxy disconnect # pylint: disable=fixme # self.reconnect() log(ERROR, traceback.format_exc()) log(ERROR, ex) From 151a507b1c69141b0654f1bf9618ea89c403097a Mon Sep 17 00:00:00 2001 From: javier Date: Thu, 17 Aug 2023 09:00:26 +0000 Subject: [PATCH 034/133] actor generator; periodically check for cluster growth --- src/py/flwr/simulation/app.py | 37 ++++++++++- .../simulation/ray_transport/ray_actor.py | 63 +++++++------------ 2 files changed, 57 insertions(+), 43 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 5e9a56170f17..4c860035c5b2 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -16,6 +16,7 @@ import sys +import threading from logging import ERROR, INFO from typing import Any, Callable, Dict, List, Optional, Type, Union @@ -34,6 +35,7 @@ DefaultActor, VirtualClientEngineActor, VirtualClientEngineActorPool, + pool_size_from_resources, ) from flwr.simulation.ray_transport.ray_client_proxy import RayActorClientProxy @@ -226,14 +228,40 @@ def start_simulation( # pylint: disable=too-many-arguments client_resources, ) + actor_args = {} if actor_kwargs is None else actor_kwargs + + # An actor generator. This is called N times to add N actors + # to the pool. If at some point the pool can accommodate more actors + # this will be called again. + def create_actor_fn() -> Type[VirtualClientEngineActor]: + return actor_type.options( # type: ignore + **client_resources, + scheduling_strategy=actor_scheduling, + ).remote(**actor_args) + # Instantiate ActorPool pool = VirtualClientEngineActorPool( + create_actor_fn=create_actor_fn, client_resources=client_resources, - actor_type=actor_type, - actor_kwargs=actor_kwargs, - actor_scheduling=actor_scheduling, ) + f_stop = threading.Event() + + # Periodically, we want to check if the cluster has grown (i.e. a new + # node has been added). When this happens, we likely want to enlarge + # the actor pool by adding more Actors to it. + def update_resources(f_stop: threading.Event) -> None: + if not f_stop.is_set(): + num_max_actors = pool_size_from_resources(client_resources) + if num_max_actors > pool.num_actors: + num = num_max_actors - pool.num_actors + log(INFO, "The cluster expanded. Adding %s actors to the pool.", num) + pool.add_actors_to_pool(num_actors=num) + + threading.Timer(10, update_resources, [f_stop]).start() + + update_resources(f_stop) + log( INFO, "Flower VCE: Creating %s with %s actors", @@ -278,6 +306,9 @@ def start_simulation( # pylint: disable=too-many-arguments ) hist = History() + # Stop time monitoring resources in cluster + f_stop.set() + event(EventType.START_SIMULATION_LEAVE) return hist diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 0c7ba38cf012..1ca32bfb2313 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -24,7 +24,6 @@ import ray from ray import ObjectRef from ray.util.actor_pool import ActorPool -from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy from flwr import common from flwr.common.logger import log @@ -114,13 +113,14 @@ def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> return num_actors -class VirtualClientEngineActorPool( - ActorPool -): # pylint: disable=[too-many-instance-attributes, too-many-arguments] +class VirtualClientEngineActorPool(ActorPool): """A pool of VirtualClientEngine Actors. Parameters ---------- + create_actor_fn : Callable[[], Type[VirtualClientEngineActor]] + A function that returns an actor that can be added to the pool. + client_resources : Dict[str, Union[int, float]] A dictionary specifying the system resources that each actor should have access. This will be used to calculate @@ -130,21 +130,6 @@ class VirtualClientEngineActorPool( enough CPUs. To understand the GPU utilization caused by `num_gpus`, as well as using custom resources, please consult the Ray documentation. - actor_type : VirtualClientEngineActor - A class defining how your Actor behaves. It should have the @ray.remote - decorator. Recall actors run workloads of virtual/simulated clients. In - this way, an actor would first "spawn" a particular client, then run the - method that indicated by the strategy (e.g. `fit()`), then it will return - the results back to the client proxy. - - actor_kwargs : Dict[str, Any] - If you implement your own Actor class, you might want to pass it some arguments - for initialization. You should use this argument to achieve so. - - actor_scheduling : Union[str, NodeAffinitySchedulingStrategy] - This allows you to control how Actors are scheduled/placed in the nodes - available to your simulation. - actor_lists: List[VirtualClientEngineActor] (default: None) This argument should not be used. It's only needed for serialization purposes (see the `__reduce__` method). Each time it is executed, we want to retain @@ -153,31 +138,21 @@ class VirtualClientEngineActorPool( def __init__( self, + create_actor_fn: Callable[[], Type[VirtualClientEngineActor]], client_resources: Dict[str, Union[int, float]], - actor_type: Type[VirtualClientEngineActor], - actor_kwargs: Optional[Dict[str, Any]], - actor_scheduling: Union[str, NodeAffinitySchedulingStrategy], - actor_list: Optional[List[VirtualClientEngineActor]] = None, + actor_list: Optional[List[Type[VirtualClientEngineActor]]] = None, ): self.client_resources = client_resources - self.actor_type = actor_type - self.actor_kwargs = actor_kwargs - self.actor_scheduling = actor_scheduling - num_actors = pool_size_from_resources(client_resources) - - args = actor_kwargs if actor_kwargs is not None else {} + self.create_actor_fn = create_actor_fn if actor_list is None: + # Figure out how many actors can be created given the cluster resources + # and the resources the user indicates each VirtualClient will need. + num_actors = pool_size_from_resources(client_resources) + actors = [create_actor_fn() for _ in range(num_actors)] + else: # When __reduce__ is executed, we don't want to created # a new list of actors again. - actors = [ - actor_type.options( # type: ignore - **client_resources, - scheduling_strategy=actor_scheduling, - ).remote(**args) - for _ in range(num_actors) - ] - else: actors = actor_list super().__init__(actors) @@ -195,13 +170,21 @@ def __init__( def __reduce__(self): # type: ignore """Make this class serializable (needed due to lock).""" return VirtualClientEngineActorPool, ( + self.create_actor_fn, self.client_resources, - self.actor_type, - self.actor_kwargs, - self.actor_scheduling, self._idle_actors, # Pass existing actors to avoid killing/re-creating ) + def add_actors_to_pool(self, num_actors: int) -> None: + """Add actors to the pool. + + This expands the pool after it has been created iif new resources are added to + your Ray cluster (e.g. you add a new node). + """ + with self.lock: + new_actors = [self.create_actor_fn() for _ in range(num_actors)] + self._idle_actors.extend(new_actors) + def submit(self, fn: Any, value: Tuple[Callable[[], ClientRes], str]) -> None: """Take idle actor and assign it a client workload. From f524fff614b4e90ae0e2a088ca8aed8ed22d32a4 Mon Sep 17 00:00:00 2001 From: javier Date: Thu, 17 Aug 2023 09:01:02 +0000 Subject: [PATCH 035/133] w/ previous --- src/py/flwr/simulation/app.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 4c860035c5b2..9c5341b52ece 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -251,6 +251,9 @@ def create_actor_fn() -> Type[VirtualClientEngineActor]: # node has been added). When this happens, we likely want to enlarge # the actor pool by adding more Actors to it. def update_resources(f_stop: threading.Event) -> None: + """Periodically check if more actors can be added to the pool. + + If so, extend the pool.""" if not f_stop.is_set(): num_max_actors = pool_size_from_resources(client_resources) if num_max_actors > pool.num_actors: From de7e252c96026f70a79eeed9fdd9dc976943a5d3 Mon Sep 17 00:00:00 2001 From: javier Date: Thu, 17 Aug 2023 09:29:14 +0000 Subject: [PATCH 036/133] better assesment of number of actors that fit in cluster; fix --- src/py/flwr/simulation/app.py | 5 +- .../simulation/ray_transport/ray_actor.py | 50 +++++++++++++------ 2 files changed, 38 insertions(+), 17 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 9c5341b52ece..79b79f24f564 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -252,8 +252,9 @@ def create_actor_fn() -> Type[VirtualClientEngineActor]: # the actor pool by adding more Actors to it. def update_resources(f_stop: threading.Event) -> None: """Periodically check if more actors can be added to the pool. - - If so, extend the pool.""" + + If so, extend the pool. + """ if not f_stop.is_set(): num_max_actors = pool_size_from_resources(client_resources) if num_max_actors > pool.num_actors: diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 1ca32bfb2313..311d224ba0a6 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -80,23 +80,42 @@ class DefaultActor(VirtualClientEngineActor): def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> int: - """Calculate number of Actors that fit in pool given the resources in the. + """Calculate number of Actors that fit in the cluster. - cluster and those required per client. + For this we consider the resources available on each node and those required per + client. """ - cluster_resources = ray.cluster_resources() - num_cpus = cluster_resources["CPU"] - num_gpus = cluster_resources.get("GPU", 0) # There might not be GPU - num_actors = int(num_cpus / client_resources["num_cpus"]) - # If a GPU is present and client resources do require one - if "num_gpus" in client_resources.keys() and client_resources["num_gpus"] > 0.0: - if num_gpus: - # If there are gpus in the cluster - num_actors = min(num_actors, int(num_gpus / client_resources["num_gpus"])) - else: - num_actors = 0 + total_num_actors = 0 + # We calculate the number of actors that fit in a node per node basis. This is + # the right way of doing it otherwise situations like the following arise: imagine + # each client needs 3 CPUs and Ray has w nodes (one with 2 CPUs and another with 4) + # if we don't follow a per-node estimation of actors, we'll be creating an actor + # pool with 2 Actors. This, however, doesn't fit in the cluster since only one of + # the nodes can fit one Actor. + nodes = ray.nodes() + for node in nodes: + node_resources = node["Resources"] + + # If a node has detached, it is still in the list of nodes + # however, its resources will be empty. + if not node_resources: + continue + + num_cpus = node_resources["CPU"] + num_gpus = node_resources.get("GPU", 0) # There might not be GPU + num_actors = int(num_cpus / client_resources["num_cpus"]) + # If a GPU is present and client resources do require one + if "num_gpus" in client_resources.keys() and client_resources["num_gpus"] > 0.0: + if num_gpus: + # If there are gpus in the cluster + num_actors = min( + num_actors, int(num_gpus / client_resources["num_gpus"]) + ) + else: + num_actors = 0 + total_num_actors += num_actors - if num_actors == 0: + if total_num_actors == 0: log( WARNING, "Your ActorPool is empty. Your system (%s, %s) " @@ -110,7 +129,7 @@ def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> "ActorPool is empty. Stopping Simulation. Check 'client_resources'" ) - return num_actors + return total_num_actors class VirtualClientEngineActorPool(ActorPool): @@ -184,6 +203,7 @@ def add_actors_to_pool(self, num_actors: int) -> None: with self.lock: new_actors = [self.create_actor_fn() for _ in range(num_actors)] self._idle_actors.extend(new_actors) + self.num_actors += num_actors def submit(self, fn: Any, value: Tuple[Callable[[], ClientRes], str]) -> None: """Take idle actor and assign it a client workload. From 935a4ca7894dce487f9f3b03e4bdeff5890ca17e Mon Sep 17 00:00:00 2001 From: javier Date: Thu, 17 Aug 2023 10:30:22 +0000 Subject: [PATCH 037/133] minor tweaks --- src/py/flwr/simulation/ray_transport/ray_actor.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 311d224ba0a6..4eec06d0c9e8 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -118,7 +118,7 @@ def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> if total_num_actors == 0: log( WARNING, - "Your ActorPool is empty. Your system (%s, %s) " + "Your ActorPool is empty. Your system (CPUs=%s, GPUs=%s) " "does not meet the criteria to host at least one client with resources:" " %s. Consider lowering your `client_resources`", num_cpus, @@ -126,7 +126,8 @@ def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> client_resources, ) raise ValueError( - "ActorPool is empty. Stopping Simulation. Check 'client_resources'" + "ActorPool is empty. Stopping Simulation. " + "Check 'client_resources' passed to `start_simulation`" ) return total_num_actors From 9a41ead9206005a28f895d966d349d3f6d4a865e Mon Sep 17 00:00:00 2001 From: jafermarq Date: Fri, 18 Aug 2023 09:57:22 +0100 Subject: [PATCH 038/133] init simulation-pytorch test --- e2e/simulation-pytorch/README.md | 7 ++ e2e/simulation-pytorch/sim.py | 160 +++++++++++++++++++++++++++++++ 2 files changed, 167 insertions(+) create mode 100644 e2e/simulation-pytorch/README.md create mode 100644 e2e/simulation-pytorch/sim.py diff --git a/e2e/simulation-pytorch/README.md b/e2e/simulation-pytorch/README.md new file mode 100644 index 000000000000..435072ffe979 --- /dev/null +++ b/e2e/simulation-pytorch/README.md @@ -0,0 +1,7 @@ +# Flower's VirtualClientEngine with PyTorch testing + +This directory is used for testing Flower's VirtualClientEngine to simulate FL workloads with PyTorch by using the CIFAR10 dataset and a CNN. This test heavily borrows from that in [e2d/pytorch](https://github.com/adap/flower/tree/main/e2e/pytorch). + +It uses the `FedAvg` strategy with a pool of 100 clients. The VCE allocates 1 cpu core per actor. + +It uses a subset of size 1000 for the training data and 10 data points for the testing. \ No newline at end of file diff --git a/e2e/simulation-pytorch/sim.py b/e2e/simulation-pytorch/sim.py new file mode 100644 index 000000000000..177735e225ee --- /dev/null +++ b/e2e/simulation-pytorch/sim.py @@ -0,0 +1,160 @@ +import warnings +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader, Subset +from torchvision.datasets import CIFAR10 +from torchvision.transforms import Compose, Normalize, ToTensor +from tqdm import tqdm + +import flwr as fl + +# ############################################################################# +# 1. Regular PyTorch pipeline: nn.Module, train, test, and DataLoader +# ############################################################################# + +warnings.filterwarnings("ignore", category=UserWarning) +SUBSET_SIZE = 1000 +POOL_SIZE = 100 # number of total clients in the experiment +CLIENT_RESOURCES = {'num_cpus': 1} + + +class Net(nn.Module): + """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz').""" + + def __init__(self) -> None: + super().__init__() + self.conv1 = nn.Conv2d(3, 6, 5) + self.pool = nn.MaxPool2d(2, 2) + self.conv2 = nn.Conv2d(6, 16, 5) + self.fc1 = nn.Linear(16 * 5 * 5, 120) + self.fc2 = nn.Linear(120, 84) + self.fc3 = nn.Linear(84, 10) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = x.view(-1, 16 * 5 * 5) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + return self.fc3(x) + + +def train(net, trainloader, epochs, device): + """Train the model on the training set.""" + criterion = torch.nn.CrossEntropyLoss() + optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9) + for _ in range(epochs): + for images, labels in trainloader: + optimizer.zero_grad() + criterion(net(images.to(device)), labels.to(device)).backward() + optimizer.step() + + +def test(net, testloader, device): + """Validate the model on the test set.""" + criterion = torch.nn.CrossEntropyLoss() + correct, loss = 0, 0.0 + with torch.no_grad(): + for images, labels in testloader: + outputs = net(images.to(device)) + labels = labels.to(device) + loss += criterion(outputs, labels).item() + correct += (torch.max(outputs.data, 1)[1] == labels).sum().item() + accuracy = correct / len(testloader.dataset) + return loss, accuracy + + +def load_data(): + """Load CIFAR-10 (training and test set).""" + trf = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) + trainset = CIFAR10("./data", train=True, download=True, transform=trf) + testset = CIFAR10("./data", train=False, download=True, transform=trf) + trainset = Subset(trainset, range(SUBSET_SIZE)) + testset = Subset(testset, range(10)) + return DataLoader(trainset, batch_size=32, shuffle=True), DataLoader(testset) + + +# ############################################################################# +# 2. Federation of the pipeline with Flower +# ############################################################################# + + +# Define Flower client +class FlowerClient(fl.client.NumPyClient): + def __init__(self): + super().__init__() + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + self.net = Net() + + def get_parameters(self, config): + return [val.cpu().numpy() for _, val in self.net.state_dict().items()] + + def fit(self, parameters, config): + set_parameters(self.net, parameters) + train(self.net, trainloader, epochs=1, device=self.device) + return self.get_parameters(config={}), len(trainloader.dataset), {} + + def evaluate(self, parameters, config): + set_parameters(self.net, parameters) + loss, accuracy = test(self.net, testloader, device=self.device) + return loss, len(testloader.dataset), {"accuracy": accuracy} + + +def set_parameters(model, parameters): + params_dict = zip(model.state_dict().keys(), parameters) + state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) + model.load_state_dict(state_dict, strict=True) + return + + +def client_fn(cid): + return FlowerClient() + + +def get_evaluate_fn(test_loader): + """Return an evaluation function for centralized evaluation.""" + + def evaluate(server_round, parameters, config): + """Use the entire CIFAR-10 test set for evaluation.""" + # determine device + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + model = Net() + set_parameters(model, parameters) + model.to(device) + loss, accuracy = test(model, test_loader, device=device) + + # return statistics + return loss, {"accuracy": accuracy} + + return evaluate + + +if __name__ == "__main__": + # Load model and data (simple CNN, CIFAR-10) + trainloader, testloader = load_data() + + strategy = fl.server.strategy.FedAvg( + fraction_fit=0.1, + fraction_evaluate=0.1, + min_fit_clients=10, + min_evaluate_clients=10, + min_available_clients=POOL_SIZE, # All clients should be available + evaluate_fn=get_evaluate_fn(testloader), # centralised evaluation of global model + ) + + # (optional) specify Ray config + ray_init_args = {"include_dashboard": False} + + # start simulation + fl.simulation.start_simulation( + client_fn=client_fn, + num_clients=POOL_SIZE, + client_resources=CLIENT_RESOURCES, + config=fl.server.ServerConfig(num_rounds=3), + strategy=strategy, + ray_init_args=ray_init_args, + ) From 1539c9e0a724a45d93890298e8f343d2437de8a4 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Fri, 18 Aug 2023 11:03:23 +0100 Subject: [PATCH 039/133] added pyproject.toml --- e2e/simulation-pytorch/pyproject.toml | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 e2e/simulation-pytorch/pyproject.toml diff --git a/e2e/simulation-pytorch/pyproject.toml b/e2e/simulation-pytorch/pyproject.toml new file mode 100644 index 000000000000..272d976062b0 --- /dev/null +++ b/e2e/simulation-pytorch/pyproject.toml @@ -0,0 +1,15 @@ +[build-system] +requires = ["poetry-core>=1.4.0"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry] +name = "simulation_pytorch" +version = "0.1.0" +description = "Federated Learning Simulation with Flower and PyTorch" +authors = ["The Flower Authors "] + +[tool.poetry.dependencies] +python = ">=3.8,<3.11" +flwr = {version = ">=1.0,<2.0", extras = ["simulation"]} +torch = "1.13.1" +torchvision = "0.14.1" From 3873f07e917d1ec363a27191577eb61037162995 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Fri, 18 Aug 2023 11:15:43 +0100 Subject: [PATCH 040/133] added CI stuff correctly? --- .github/dependabot.yml | 6 ++++++ .github/workflows/e2e.yml | 5 +++++ 2 files changed, 11 insertions(+) diff --git a/.github/dependabot.yml b/.github/dependabot.yml index fed26fa6eb24..2e8756db5b4a 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -76,3 +76,9 @@ updates: schedule: interval: "daily" open-pull-requests-limit: 2 + + - package-ecosystem: "pip" + directory: "/e2e/simulation-pytorch" + schedule: + interval: "daily" + open-pull-requests-limit: 2 \ No newline at end of file diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml index ded3aa611195..c59ce5e4adee 100644 --- a/.github/workflows/e2e.yml +++ b/.github/workflows/e2e.yml @@ -70,6 +70,11 @@ jobs: from sklearn.datasets import load_iris Path('data').mkdir(exist_ok=True) load_iris(as_frame=True)['data'].to_csv('./data/client.csv') + + - directory: simulation-pytorch + dataset: | + from torchvision.datasets import CIFAR10 + CIFAR10('./data', download=True) name: Framework / ${{matrix.directory}} From 4e7e5dd7516ab2d14f92f1849d1c725df61ee45e Mon Sep 17 00:00:00 2001 From: jafermarq Date: Fri, 18 Aug 2023 11:46:49 +0100 Subject: [PATCH 041/133] conditional ECE & driver tests --- .github/workflows/e2e.yml | 4 ++++ e2e/simulation-pytorch/{sim.py => simulation.py} | 0 2 files changed, 4 insertions(+) rename e2e/simulation-pytorch/{sim.py => simulation.py} (100%) diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml index c59ce5e4adee..5a798bfbf97b 100644 --- a/.github/workflows/e2e.yml +++ b/.github/workflows/e2e.yml @@ -75,6 +75,8 @@ jobs: dataset: | from torchvision.datasets import CIFAR10 CIFAR10('./data', download=True) + skip-ece: True + skip-driver: True name: Framework / ${{matrix.directory}} @@ -94,10 +96,12 @@ jobs: if: ${{ matrix.dataset }} run: python -c "${{ matrix.dataset }}" - name: Run edge client test + if: ${{!matrix.skip-ece}} run: ./../test.sh "${{ matrix.directory }}" - name: Run virtual client test run: python simulation.py - name: Run driver test + if: ${{!matrix.skip-driver}} run: ./../test_driver.sh strategies: diff --git a/e2e/simulation-pytorch/sim.py b/e2e/simulation-pytorch/simulation.py similarity index 100% rename from e2e/simulation-pytorch/sim.py rename to e2e/simulation-pytorch/simulation.py From a848a3497fdac916d691c6c34f881c6042be1085 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Fri, 18 Aug 2023 11:50:09 +0100 Subject: [PATCH 042/133] fix --- e2e/simulation-pytorch/simulation.py | 1 - 1 file changed, 1 deletion(-) diff --git a/e2e/simulation-pytorch/simulation.py b/e2e/simulation-pytorch/simulation.py index 177735e225ee..c061218fb615 100644 --- a/e2e/simulation-pytorch/simulation.py +++ b/e2e/simulation-pytorch/simulation.py @@ -7,7 +7,6 @@ from torch.utils.data import DataLoader, Subset from torchvision.datasets import CIFAR10 from torchvision.transforms import Compose, Normalize, ToTensor -from tqdm import tqdm import flwr as fl From ee85152145c6dbdd9c6f1834d7d9446df2aa1e7a Mon Sep 17 00:00:00 2001 From: jafermarq Date: Mon, 21 Aug 2023 09:16:23 +0100 Subject: [PATCH 043/133] reverted adding tests --- .github/dependabot.yml | 6 - .github/workflows/e2e.yml | 9 - e2e/simulation-pytorch/README.md | 7 - e2e/simulation-pytorch/pyproject.toml | 15 -- e2e/simulation-pytorch/simulation.py | 159 ------------------ .../simulation/ray_transport/ray_actor.py | 5 +- 6 files changed, 2 insertions(+), 199 deletions(-) delete mode 100644 e2e/simulation-pytorch/README.md delete mode 100644 e2e/simulation-pytorch/pyproject.toml delete mode 100644 e2e/simulation-pytorch/simulation.py diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 2e8756db5b4a..fed26fa6eb24 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -76,9 +76,3 @@ updates: schedule: interval: "daily" open-pull-requests-limit: 2 - - - package-ecosystem: "pip" - directory: "/e2e/simulation-pytorch" - schedule: - interval: "daily" - open-pull-requests-limit: 2 \ No newline at end of file diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml index 5a798bfbf97b..ded3aa611195 100644 --- a/.github/workflows/e2e.yml +++ b/.github/workflows/e2e.yml @@ -70,13 +70,6 @@ jobs: from sklearn.datasets import load_iris Path('data').mkdir(exist_ok=True) load_iris(as_frame=True)['data'].to_csv('./data/client.csv') - - - directory: simulation-pytorch - dataset: | - from torchvision.datasets import CIFAR10 - CIFAR10('./data', download=True) - skip-ece: True - skip-driver: True name: Framework / ${{matrix.directory}} @@ -96,12 +89,10 @@ jobs: if: ${{ matrix.dataset }} run: python -c "${{ matrix.dataset }}" - name: Run edge client test - if: ${{!matrix.skip-ece}} run: ./../test.sh "${{ matrix.directory }}" - name: Run virtual client test run: python simulation.py - name: Run driver test - if: ${{!matrix.skip-driver}} run: ./../test_driver.sh strategies: diff --git a/e2e/simulation-pytorch/README.md b/e2e/simulation-pytorch/README.md deleted file mode 100644 index 435072ffe979..000000000000 --- a/e2e/simulation-pytorch/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Flower's VirtualClientEngine with PyTorch testing - -This directory is used for testing Flower's VirtualClientEngine to simulate FL workloads with PyTorch by using the CIFAR10 dataset and a CNN. This test heavily borrows from that in [e2d/pytorch](https://github.com/adap/flower/tree/main/e2e/pytorch). - -It uses the `FedAvg` strategy with a pool of 100 clients. The VCE allocates 1 cpu core per actor. - -It uses a subset of size 1000 for the training data and 10 data points for the testing. \ No newline at end of file diff --git a/e2e/simulation-pytorch/pyproject.toml b/e2e/simulation-pytorch/pyproject.toml deleted file mode 100644 index 272d976062b0..000000000000 --- a/e2e/simulation-pytorch/pyproject.toml +++ /dev/null @@ -1,15 +0,0 @@ -[build-system] -requires = ["poetry-core>=1.4.0"] -build-backend = "poetry.core.masonry.api" - -[tool.poetry] -name = "simulation_pytorch" -version = "0.1.0" -description = "Federated Learning Simulation with Flower and PyTorch" -authors = ["The Flower Authors "] - -[tool.poetry.dependencies] -python = ">=3.8,<3.11" -flwr = {version = ">=1.0,<2.0", extras = ["simulation"]} -torch = "1.13.1" -torchvision = "0.14.1" diff --git a/e2e/simulation-pytorch/simulation.py b/e2e/simulation-pytorch/simulation.py deleted file mode 100644 index c061218fb615..000000000000 --- a/e2e/simulation-pytorch/simulation.py +++ /dev/null @@ -1,159 +0,0 @@ -import warnings -from collections import OrderedDict - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import DataLoader, Subset -from torchvision.datasets import CIFAR10 -from torchvision.transforms import Compose, Normalize, ToTensor - -import flwr as fl - -# ############################################################################# -# 1. Regular PyTorch pipeline: nn.Module, train, test, and DataLoader -# ############################################################################# - -warnings.filterwarnings("ignore", category=UserWarning) -SUBSET_SIZE = 1000 -POOL_SIZE = 100 # number of total clients in the experiment -CLIENT_RESOURCES = {'num_cpus': 1} - - -class Net(nn.Module): - """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz').""" - - def __init__(self) -> None: - super().__init__() - self.conv1 = nn.Conv2d(3, 6, 5) - self.pool = nn.MaxPool2d(2, 2) - self.conv2 = nn.Conv2d(6, 16, 5) - self.fc1 = nn.Linear(16 * 5 * 5, 120) - self.fc2 = nn.Linear(120, 84) - self.fc3 = nn.Linear(84, 10) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.pool(F.relu(self.conv1(x))) - x = self.pool(F.relu(self.conv2(x))) - x = x.view(-1, 16 * 5 * 5) - x = F.relu(self.fc1(x)) - x = F.relu(self.fc2(x)) - return self.fc3(x) - - -def train(net, trainloader, epochs, device): - """Train the model on the training set.""" - criterion = torch.nn.CrossEntropyLoss() - optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9) - for _ in range(epochs): - for images, labels in trainloader: - optimizer.zero_grad() - criterion(net(images.to(device)), labels.to(device)).backward() - optimizer.step() - - -def test(net, testloader, device): - """Validate the model on the test set.""" - criterion = torch.nn.CrossEntropyLoss() - correct, loss = 0, 0.0 - with torch.no_grad(): - for images, labels in testloader: - outputs = net(images.to(device)) - labels = labels.to(device) - loss += criterion(outputs, labels).item() - correct += (torch.max(outputs.data, 1)[1] == labels).sum().item() - accuracy = correct / len(testloader.dataset) - return loss, accuracy - - -def load_data(): - """Load CIFAR-10 (training and test set).""" - trf = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) - trainset = CIFAR10("./data", train=True, download=True, transform=trf) - testset = CIFAR10("./data", train=False, download=True, transform=trf) - trainset = Subset(trainset, range(SUBSET_SIZE)) - testset = Subset(testset, range(10)) - return DataLoader(trainset, batch_size=32, shuffle=True), DataLoader(testset) - - -# ############################################################################# -# 2. Federation of the pipeline with Flower -# ############################################################################# - - -# Define Flower client -class FlowerClient(fl.client.NumPyClient): - def __init__(self): - super().__init__() - self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - self.net = Net() - - def get_parameters(self, config): - return [val.cpu().numpy() for _, val in self.net.state_dict().items()] - - def fit(self, parameters, config): - set_parameters(self.net, parameters) - train(self.net, trainloader, epochs=1, device=self.device) - return self.get_parameters(config={}), len(trainloader.dataset), {} - - def evaluate(self, parameters, config): - set_parameters(self.net, parameters) - loss, accuracy = test(self.net, testloader, device=self.device) - return loss, len(testloader.dataset), {"accuracy": accuracy} - - -def set_parameters(model, parameters): - params_dict = zip(model.state_dict().keys(), parameters) - state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) - model.load_state_dict(state_dict, strict=True) - return - - -def client_fn(cid): - return FlowerClient() - - -def get_evaluate_fn(test_loader): - """Return an evaluation function for centralized evaluation.""" - - def evaluate(server_round, parameters, config): - """Use the entire CIFAR-10 test set for evaluation.""" - # determine device - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - model = Net() - set_parameters(model, parameters) - model.to(device) - loss, accuracy = test(model, test_loader, device=device) - - # return statistics - return loss, {"accuracy": accuracy} - - return evaluate - - -if __name__ == "__main__": - # Load model and data (simple CNN, CIFAR-10) - trainloader, testloader = load_data() - - strategy = fl.server.strategy.FedAvg( - fraction_fit=0.1, - fraction_evaluate=0.1, - min_fit_clients=10, - min_evaluate_clients=10, - min_available_clients=POOL_SIZE, # All clients should be available - evaluate_fn=get_evaluate_fn(testloader), # centralised evaluation of global model - ) - - # (optional) specify Ray config - ray_init_args = {"include_dashboard": False} - - # start simulation - fl.simulation.start_simulation( - client_fn=client_fn, - num_clients=POOL_SIZE, - client_resources=CLIENT_RESOURCES, - config=fl.server.ServerConfig(num_rounds=3), - strategy=strategy, - ray_init_args=ray_init_args, - ) diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 4eec06d0c9e8..311d224ba0a6 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -118,7 +118,7 @@ def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> if total_num_actors == 0: log( WARNING, - "Your ActorPool is empty. Your system (CPUs=%s, GPUs=%s) " + "Your ActorPool is empty. Your system (%s, %s) " "does not meet the criteria to host at least one client with resources:" " %s. Consider lowering your `client_resources`", num_cpus, @@ -126,8 +126,7 @@ def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> client_resources, ) raise ValueError( - "ActorPool is empty. Stopping Simulation. " - "Check 'client_resources' passed to `start_simulation`" + "ActorPool is empty. Stopping Simulation. Check 'client_resources'" ) return total_num_actors From b6a51c426351456e4024d3fba26dc4695f1bc951 Mon Sep 17 00:00:00 2001 From: Javier Date: Tue, 22 Aug 2023 20:15:29 +0100 Subject: [PATCH 044/133] Apply suggestions from code review Co-authored-by: Daniel J. Beutel --- src/py/flwr/simulation/app.py | 17 +++++++------ .../simulation/ray_transport/ray_actor.py | 24 +++++++++++-------- 2 files changed, 22 insertions(+), 19 deletions(-) diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py index 79b79f24f564..18ba34cc94de 100644 --- a/src/py/flwr/simulation/app.py +++ b/src/py/flwr/simulation/app.py @@ -136,10 +136,9 @@ def start_simulation( # pylint: disable=too-many-arguments Set to True to prevent `ray.shutdown()` in case `ray.is_initialized()=True`. actor_type: VirtualClientEngineActor (default: DefaultActor) - Optionally specify the type of actor to use. The actor object, which - persist throughout the simulation will be the process in charged of - running the clients' jobs (i.e. their fit() method). + persists throughout the simulation, will be the process in charge of + running the clients' jobs (i.e. their `fit()` method). actor_kwargs: Optional[Dict[str, Any]] (default: None) If you want to create your own Actor classes, you might need to pass @@ -230,7 +229,7 @@ def start_simulation( # pylint: disable=too-many-arguments actor_args = {} if actor_kwargs is None else actor_kwargs - # An actor generator. This is called N times to add N actors + # An actor factory. This is called N times to add N actors # to the pool. If at some point the pool can accommodate more actors # this will be called again. def create_actor_fn() -> Type[VirtualClientEngineActor]: @@ -247,8 +246,8 @@ def create_actor_fn() -> Type[VirtualClientEngineActor]: f_stop = threading.Event() - # Periodically, we want to check if the cluster has grown (i.e. a new - # node has been added). When this happens, we likely want to enlarge + # Periodically, check if the cluster has grown (i.e. a new + # node has been added). If this happens, we likely want to grow # the actor pool by adding more Actors to it. def update_resources(f_stop: threading.Event) -> None: """Periodically check if more actors can be added to the pool. @@ -258,9 +257,9 @@ def update_resources(f_stop: threading.Event) -> None: if not f_stop.is_set(): num_max_actors = pool_size_from_resources(client_resources) if num_max_actors > pool.num_actors: - num = num_max_actors - pool.num_actors - log(INFO, "The cluster expanded. Adding %s actors to the pool.", num) - pool.add_actors_to_pool(num_actors=num) + num_new = num_max_actors - pool.num_actors + log(INFO, "The cluster expanded. Adding %s actors to the pool.", num_new) + pool.add_actors_to_pool(num_actors=num_new) threading.Timer(10, update_resources, [f_stop]).start() diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 311d224ba0a6..39274d016b5c 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -86,6 +86,7 @@ def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> client. """ total_num_actors = 0 + # We calculate the number of actors that fit in a node per node basis. This is # the right way of doing it otherwise situations like the following arise: imagine # each client needs 3 CPUs and Ray has w nodes (one with 2 CPUs and another with 4) @@ -104,6 +105,7 @@ def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> num_cpus = node_resources["CPU"] num_gpus = node_resources.get("GPU", 0) # There might not be GPU num_actors = int(num_cpus / client_resources["num_cpus"]) + # If a GPU is present and client resources do require one if "num_gpus" in client_resources.keys() and client_resources["num_gpus"] > 0.0: if num_gpus: @@ -118,9 +120,9 @@ def pool_size_from_resources(client_resources: Dict[str, Union[int, float]]) -> if total_num_actors == 0: log( WARNING, - "Your ActorPool is empty. Your system (%s, %s) " + "The ActorPool is empty. The system (%s, %s) " "does not meet the criteria to host at least one client with resources:" - " %s. Consider lowering your `client_resources`", + " %s. Lowering the `client_resources` could help.", num_cpus, num_gpus, client_resources, @@ -166,7 +168,7 @@ def __init__( if actor_list is None: # Figure out how many actors can be created given the cluster resources - # and the resources the user indicates each VirtualClient will need. + # and the resources the user indicates each VirtualClient will need num_actors = pool_size_from_resources(client_resources) actors = [create_actor_fn() for _ in range(num_actors)] else: @@ -219,21 +221,21 @@ def submit(self, fn: Any, value: Tuple[Callable[[], ClientRes], str]) -> None: self._future_to_actor[future_key] = (self._next_task_index, actor, cid) self._next_task_index += 1 - # update with future + # Update with future self._cid_to_future[cid]["future"] = future_key def submit_client_job( self, actor_fn: Any, job: Tuple[Callable[[], ClientRes], str] ) -> None: """Submit a job while tracking client ids.""" + _, cid = job + # We need to put this behind a lock since .submit() involves # removing and adding elements from a dictionary. Which creates # issues in multi-threaded settings - - _, cid = job with self.lock: # TODO: w/ timestamp check, call ray.resources() # pylint: disable=fixme - # Creating cid to future mapping + # Create cid to future mapping self._reset_cid_to_future_dict(cid) if self._idle_actors: # Submit job since there is an Actor that's available @@ -336,9 +338,9 @@ def _check_actor_fits_in_pool(self) -> bool: self.num_actors, num_actors_updated, ) - # We are preventing one actor to be added back in the queue, so we just + # We are preventing one actor from being added back to the queue, so we just # decrease the number of actors by one. Eventually `self.num_actors` - # should be equal what pool_size_from_resources(self.resources) returns + # should be equal to what `pool_size_from_resources(self.resources)` returns self.num_actors -= 1 return False @@ -348,6 +350,7 @@ def process_unordered_future(self, timeout: Optional[float] = None) -> None: """Similar to parent's get_next_unordered() but without final ray.get().""" if not self.has_next(): # type: ignore raise StopIteration("No more results to get") + # Block until one result is ready res, _ = ray.wait(list(self._future_to_actor), num_returns=1, timeout=timeout) @@ -360,7 +363,7 @@ def process_unordered_future(self, timeout: Optional[float] = None) -> None: # Get actor that completed a job _, actor, cid = self._future_to_actor.pop(future, (None, None, -1)) if actor is not None: - # Still space in queue ? (no if a node in the cluster died) + # Still space in queue? (no if a node in the cluster died) if self._check_actor_fits_in_pool(): if self._check_and_remove_actor_from_pool(actor): self._return_actor(actor) # type: ignore @@ -375,6 +378,7 @@ def process_unordered_future(self, timeout: Optional[float] = None) -> None: def get_client_result(self, cid: str, timeout: Optional[float]) -> ClientRes: """Get result from VirtualClient with specific cid.""" + # Loop until all jobs submitted to the pool are completed. Break early # if the result for the ClientProxy calling this method is ready while self.has_next() and not self._is_future_ready(cid): # type: ignore From 9decadb69ea35fd2bed4aeae9afd5fc5c9a1fcb8 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 23 Aug 2023 13:53:38 +0900 Subject: [PATCH 045/133] create Fedmeta --- baselines/FedMeta/EXTENDED_README.md | 123 +++++++++++ baselines/FedMeta/FedMeta/__init__.py | 1 + baselines/FedMeta/FedMeta/client.py | 5 + baselines/FedMeta/FedMeta/conf/base.yaml | 17 ++ baselines/FedMeta/FedMeta/dataset.py | 10 + .../FedMeta/FedMeta/dataset_preparation.py | 34 +++ baselines/FedMeta/FedMeta/main.py | 53 +++++ baselines/FedMeta/FedMeta/models.py | 7 + baselines/FedMeta/FedMeta/server.py | 5 + baselines/FedMeta/FedMeta/strategy.py | 5 + baselines/FedMeta/FedMeta/utils.py | 6 + baselines/FedMeta/LICENSE | 202 ++++++++++++++++++ baselines/FedMeta/README.md | 79 +++++++ baselines/FedMeta/pyproject.toml | 135 ++++++++++++ 14 files changed, 682 insertions(+) create mode 100644 baselines/FedMeta/EXTENDED_README.md create mode 100644 baselines/FedMeta/FedMeta/__init__.py create mode 100644 baselines/FedMeta/FedMeta/client.py create mode 100644 baselines/FedMeta/FedMeta/conf/base.yaml create mode 100644 baselines/FedMeta/FedMeta/dataset.py create mode 100644 baselines/FedMeta/FedMeta/dataset_preparation.py create mode 100644 baselines/FedMeta/FedMeta/main.py create mode 100644 baselines/FedMeta/FedMeta/models.py create mode 100644 baselines/FedMeta/FedMeta/server.py create mode 100644 baselines/FedMeta/FedMeta/strategy.py create mode 100644 baselines/FedMeta/FedMeta/utils.py create mode 100644 baselines/FedMeta/LICENSE create mode 100644 baselines/FedMeta/README.md create mode 100644 baselines/FedMeta/pyproject.toml diff --git a/baselines/FedMeta/EXTENDED_README.md b/baselines/FedMeta/EXTENDED_README.md new file mode 100644 index 000000000000..9c8f5bc72fa9 --- /dev/null +++ b/baselines/FedMeta/EXTENDED_README.md @@ -0,0 +1,123 @@ + +# Extended Readme + +> The baselines are expected to run in a machine running Ubuntu 22.04 + +While `README.md` should include information about the baseline you implement and how to run it, this _extended_ readme provides info on what's the expected directory structure for a new baseline and more generally the instructions to follow before your baseline can be merged into the Flower repository. Please follow closely these instructions. It is likely that you have already completed steps 1-2. + +1. Fork the Flower repository and clone it. +2. Navigate to the `baselines/` directory and from there run: + ```bash + # This will create a new directory with the same structure as this `baseline_template` directory. + ./dev/create-baseline.sh + ``` +3. All your code and configs should go into a sub-directory with the same name as the name of your baseline. + * The sub-directory contains a series of Python scripts that you can edit. Please stick to these files and consult with us if you need additional ones. + * There is also a basic config structure in `/conf` ready be parsed by [Hydra](https://hydra.cc/) when executing your `main.py`. +4. Therefore, the directory structure in your baseline should look like: + ```bash + baselines/ + ├── README.md # describes your baseline and everything needed to use it + ├── EXTENDED_README.md # to remove before creating your PR + ├── pyproject.toml # details your Python environment + └── + ├── *.py # several .py files including main.py and __init__.py + └── conf + └── *.yaml # one or more Hydra config files + + ``` +> :warning: Make sure the variable `name` in `pyproject.toml` is set to the name of the sub-directory containing all your code. + +5. Add your dependencies to the `pyproject.toml` (see below a few examples on how to do it). Read more about Poetry below in this `EXTENDED_README.md`. +6. Regularly check that your coding style and the documentation you add follow good coding practices. To test whether your code meets the requirements, please run the following: + ```bash + # After activating your environment and from your baseline's directory + cd .. # to go to the top-level directory of all baselines + ./dev/test-baseline.sh + ./dev/test-baseline-structure.sh + ``` + Both `test-baseline.sh` and `test-baseline-structure.sh` will also be automatically run when you create a PR, and both tests need to pass for the baseline to be merged. + To automatically solve some formatting issues and apply easy fixes, please run the formatting script: + ```bash + # After activating your environment and from your baseline's directory + cd .. # to go to the top-level directory of all baselines + ./dev/format-baseline.sh + ``` +7. Ensure that the Python environment for your baseline can be created without errors by simply running `poetry install` and that this is properly described later when you complete the `Environment Setup` section in `README.md`. This is specially important if your environment requires additional steps after doing `poetry install`. +8. Ensure that your baseline runs with default arguments by running `poetry run python -m .main`. Then, describe this and other forms of running your code in the `Running the Experiments` section in `README.md`. +9. Once your code is ready and you have checked: + * that following the instructions in your `README.md` the Python environment can be created correctly + + * that running the code following your instructions can reproduce the experiments in the paper + + , then you just need to create a Pull Request (PR) to kickstart the process of merging your baseline into the Flower repository. + +> Once you are happy to merge your baseline contribution, please delete this `EXTENDED_README.md` file. + + +## About Poetry + +We use Poetry to manage the Python environment for each individual baseline. You can follow the instructions [here](https://python-poetry.org/docs/) to install Poetry in your machine. + + +### Specifying a Python Version (optional) +By default, Poetry will use the Python version in your system. In some settings, you might want to specify a particular version of Python to use inside your Poetry environment. You can do so with [`pyenv`](https://github.com/pyenv/pyenv). Check the documentation for the different ways of installing `pyenv`, but one easy way is using the [automatic installer](https://github.com/pyenv/pyenv-installer): +```bash +curl https://pyenv.run | bash # then, don't forget links to your .bashrc/.zshrc +``` + +You can then install any Python version with `pyenv install ` (e.g. `pyenv install 3.9.17`). Then, in order to use that version for your baseline, you'd do the following: + +```bash +# cd to your baseline directory (i.e. where the `pyproject.toml` is) +pyenv local + +# set that version for poetry +poetry env use + +# then you can install your Poetry environment (see the next setp) +``` + +### Installing Your Environment +With the Poetry tool already installed, you can create an environment for this baseline with commands: +```bash +# run this from the same directory as the `pyproject.toml` file is +poetry install +``` + +This will create a basic Python environment with just Flower and additional packages, including those needed for simulation. Next, you should add the dependencies for your code. It is **critical** that you fix the version of the packages you use using a `=` not a `=^`. You can do so via [`poetry add`](https://python-poetry.org/docs/cli/#add). Below are some examples: + +```bash +# For instance, if you want to install tqdm +poetry add tqdm==4.65.0 + +# If you already have a requirements.txt, you can add all those packages (but ensure you have fixed the version) in one go as follows: +poetry add $( cat requirements.txt ) +``` +With each `poetry add` command, the `pyproject.toml` gets automatically updated so you don't need to keep that `requirements.txt` as part of this baseline. + + +More critically however, is adding your ML framework of choice to the list of dependencies. For some frameworks you might be able to do so with the `poetry add` command. Check [the Poetry documentation](https://python-poetry.org/docs/cli/#add) for how to add packages in various ways. For instance, let's say you want to use PyTorch: + +```bash +# with plain `pip` you'd run a command such as: +pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117 + +# to add the same 3 dependencies to your Poetry environment you'd need to add the URL to the wheel that the above pip command auto-resolves for you. +# You can find those wheels in `https://download.pytorch.org/whl/cu117`. Copy the link and paste it after the `poetry add` command. +# For instance to add `torch==1.13.1+cu117` and a x86 Linux system with Python3.8 you'd: +poetry add https://download.pytorch.org/whl/cu117/torch-1.13.1%2Bcu117-cp38-cp38-linux_x86_64.whl +# you'll need to repeat this for both `torchvision` and `torchaudio` +``` +The above is just an example of how you can add these dependencies. Please refer to the Poetry documentation to extra reference. + +If all attempts fail, you can still install packages via standard `pip`. You'd first need to source/activate your Poetry environment. +```bash +# first ensure you have created your environment +# and installed the base packages provided in the template +poetry install + +# then activate it +poetry shell +``` +Now you are inside your environment (pretty much as when you use `virtualenv` or `conda`) so you can install further packages with `pip`. Please note that, unlike with `poetry add`, these extra requirements won't be captured by `pyproject.toml`. Therefore, please ensure that you provide all instructions needed to: (1) create the base environment with Poetry and (2) install any additional dependencies via `pip` when you complete your `README.md`. \ No newline at end of file diff --git a/baselines/FedMeta/FedMeta/__init__.py b/baselines/FedMeta/FedMeta/__init__.py new file mode 100644 index 000000000000..a5e567b59135 --- /dev/null +++ b/baselines/FedMeta/FedMeta/__init__.py @@ -0,0 +1 @@ +"""Template baseline package.""" diff --git a/baselines/FedMeta/FedMeta/client.py b/baselines/FedMeta/FedMeta/client.py new file mode 100644 index 000000000000..d2e2206111f3 --- /dev/null +++ b/baselines/FedMeta/FedMeta/client.py @@ -0,0 +1,5 @@ +"""Define your client class and a function to construct such clients. + +Please overwrite `flwr.client.NumPyClient` or `flwr.client.Client` and create a function +to instantiate your client. +""" diff --git a/baselines/FedMeta/FedMeta/conf/base.yaml b/baselines/FedMeta/FedMeta/conf/base.yaml new file mode 100644 index 000000000000..2d65b3b989b2 --- /dev/null +++ b/baselines/FedMeta/FedMeta/conf/base.yaml @@ -0,0 +1,17 @@ +--- +# this is the config that will be loaded as default by main.py +# Please follow the provided structure (this will ensuring all baseline follow +# a similar configuration structure and hence be easy to customise) + +dataset: + # dataset config + +model: + # model config + +strategy: + _target_: # points to your strategy (either custom or exiting in Flower) + # rest of strategy config + +client: + # client config diff --git a/baselines/FedMeta/FedMeta/dataset.py b/baselines/FedMeta/FedMeta/dataset.py new file mode 100644 index 000000000000..5e436abe12fb --- /dev/null +++ b/baselines/FedMeta/FedMeta/dataset.py @@ -0,0 +1,10 @@ +"""Handle basic dataset creation. + +In case of PyTorch it should return dataloaders for your dataset (for both the clients +and the server). If you are using a custom dataset class, this module is the place to +define it. If your dataset requires to be downloaded (and this is not done +automatically -- e.g. as it is the case for many dataset in TorchVision) and +partitioned, please include all those functions and logic in the +`dataset_preparation.py` module. You can use all those functions from functions/methods +defined here of course. +""" diff --git a/baselines/FedMeta/FedMeta/dataset_preparation.py b/baselines/FedMeta/FedMeta/dataset_preparation.py new file mode 100644 index 000000000000..bd3440b9276b --- /dev/null +++ b/baselines/FedMeta/FedMeta/dataset_preparation.py @@ -0,0 +1,34 @@ +"""Handle the dataset partitioning and (optionally) complex downloads. + +Please add here all the necessary logic to either download, uncompress, pre/post-process +your dataset (or all of the above). If the desired way of running your baseline is to +first download the dataset and partition it and then run the experiments, please +uncomment the lines below and tell us in the README.md (see the "Running the Experiment" +block) that this file should be executed first. +""" +# import hydra +# from hydra.core.hydra_config import HydraConfig +# from hydra.utils import call, instantiate +# from omegaconf import DictConfig, OmegaConf + + +# @hydra.main(config_path="conf", config_name="base", version_base=None) +# def download_and_preprocess(cfg: DictConfig) -> None: +# """Does everything needed to get the dataset. + +# Parameters +# ---------- +# cfg : DictConfig +# An omegaconf object that stores the hydra config. +# """ + +# ## 1. print parsed config +# print(OmegaConf.to_yaml(cfg)) + +# # Please include here all the logic +# # Please use the Hydra config style as much as possible specially +# # for parts that can be customised (e.g. how data is partitioned) + +# if __name__ == "__main__": + +# download_and_preprocess() diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py new file mode 100644 index 000000000000..795800c64e21 --- /dev/null +++ b/baselines/FedMeta/FedMeta/main.py @@ -0,0 +1,53 @@ +"""Create and connect the building blocks for your experiments; start the simulation. + +It includes processioning the dataset, instantiate strategy, specify how the global +model is going to be evaluated, etc. At the end, this script saves the results. +""" +# these are the basic packages you'll need here +# feel free to remove some if aren't needed +import hydra +from omegaconf import DictConfig, OmegaConf + + +@hydra.main(config_path="conf", config_name="base", version_base=None) +def main(cfg: DictConfig) -> None: + """Run the baseline. + + Parameters + ---------- + cfg : DictConfig + An omegaconf object that stores the hydra config. + """ + # 1. Print parsed config + print(OmegaConf.to_yaml(cfg)) + + # 2. Prepare your dataset + # here you should call a function in datasets.py that returns whatever is needed to: + # (1) ensure the server can access the dataset used to evaluate your model after + # aggregation + # (2) tell each client what dataset partitions they should use (e.g. a this could + # be a location in the file system, a list of dataloader, a list of ids to extract + # from a dataset, it's up to you) + + # 3. Define your clients + # Define a function that returns another function that will be used during + # simulation to instantiate each individual client + # client_fn = client.() + + # 4. Define your strategy + # pass all relevant argument (including the global dataset used after aggregation, + # if needed by your method.) + # strategy = instantiate(cfg.strategy, ) + + # 5. Start Simulation + # history = fl.simulation.start_simulation() + + # 6. Save your results + # Here you can save the `history` returned by the simulation and include + # also other buffers, statistics, info needed to be saved in order to later + # on generate the plots you provide in the README.md. You can for instance + # access elements that belong to the strategy for example: + # data = strategy.get_my_custom_data() -- assuming you have such method defined. + # Hydra will generate for you a directory each time you run the code. You + # can retrieve the path to that directory with this: + # save_path = HydraConfig.get().runtime.output_dir diff --git a/baselines/FedMeta/FedMeta/models.py b/baselines/FedMeta/FedMeta/models.py new file mode 100644 index 000000000000..71fa553d1f59 --- /dev/null +++ b/baselines/FedMeta/FedMeta/models.py @@ -0,0 +1,7 @@ +"""Define our models, and training and eval functions. + +If your model is 100% off-the-shelf (e.g. directly from torchvision without requiring +modifications) you might be better off instantiating your model directly from the Hydra +config. In this way, swapping your model for another one can be done without changing +the python code at all +""" diff --git a/baselines/FedMeta/FedMeta/server.py b/baselines/FedMeta/FedMeta/server.py new file mode 100644 index 000000000000..2fd7d42cde5a --- /dev/null +++ b/baselines/FedMeta/FedMeta/server.py @@ -0,0 +1,5 @@ +"""Create global evaluation function. + +Optionally, also define a new Server class (please note this is not needed in most +settings). +""" diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py new file mode 100644 index 000000000000..17436c401c30 --- /dev/null +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -0,0 +1,5 @@ +"""Optionally define a custom strategy. + +Needed only when the strategy is not yet implemented in Flower or because you want to +extend or modify the functionality of an existing strategy. +""" diff --git a/baselines/FedMeta/FedMeta/utils.py b/baselines/FedMeta/FedMeta/utils.py new file mode 100644 index 000000000000..9a831719d623 --- /dev/null +++ b/baselines/FedMeta/FedMeta/utils.py @@ -0,0 +1,6 @@ +"""Define any utility function. + +They are not directly relevant to the other (more FL specific) python modules. For +example, you may define here things like: loading a model from a checkpoint, saving +results, plotting. +""" diff --git a/baselines/FedMeta/LICENSE b/baselines/FedMeta/LICENSE new file mode 100644 index 000000000000..d64569567334 --- /dev/null +++ b/baselines/FedMeta/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md new file mode 100644 index 000000000000..0f9ecb972652 --- /dev/null +++ b/baselines/FedMeta/README.md @@ -0,0 +1,79 @@ +--- +title: Federated Meta-Learning with Fast Convergence and Efficient Communication +url: https://arxiv.org/abs/1802.07876 +labels: [meta learning, maml, meta-sgd, personalization] # please add between 4 and 10 single-word (maybe two-words) labels (e.g. "system heterogeneity", "image classification", "asynchronous", "weight sharing", "cross-silo") +dataset: [Femnist, Shakespeare] # list of datasets you include in your baseline +--- + +# FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication + +****Paper:**** : https://arxiv.org/abs/1802.07876 + +****Authors:**** :Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, Xiuqiang He + +****Abstract:**** :Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta-learning is a natural choice to handle these issues, and propose a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches. We conduct an extensive empirical evaluation on LEAF datasets and a real-world production dataset, and demonstrate that FedMeta achieves a reduction in required communication cost by 2.82-4.33 times with faster convergence, and an increase in accuracy by 3.23%-14.84% as compared to Federated Averaging (FedAvg) which is a leading optimization algorithm in federated learning. Moreover, FedMeta preserves user privacy since only the parameterized algorithm is transmitted between mobile devices and central servers, and no raw data is collected onto the servers. + + +## About this baseline + +****What’s implemented:**** :warning: *_Concisely describe what experiment(s) in the publication can be replicated by running the code. Please only use a few sentences. Start with: “The code in this directory …”_* + +****Datasets:**** :warning: *_List the datasets you used (if you used a medium to large dataset, >10GB please also include the sizes of the dataset)._* + +****Hardware Setup:**** :warning: *_Give some details about the hardware (e.g. a server with 8x V100 32GB and 256GB of RAM) you used to run the experiments for this baseline. Someone out there might not have access to the same resources you have so, could list the absolute minimum hardware needed to run the experiment in a reasonable amount of time ? (e.g. minimum is 1x 16GB GPU otherwise a client model can’t be trained with a sufficiently large batch size). Could you test this works too?_* + +****Contributors:**** :warning: *_let the world know who contributed to this baseline. This could be either your name, your name and affiliation at the time, or your GitHub profile name if you prefer. If multiple contributors signed up for this baseline, please list yourself and your colleagues_* + + +## Experimental Setup + +****Task:**** :warning: *_what’s the primary task that is being federated? (e.g. image classification, next-word prediction). If you have experiments for several, please list them_* + +****Model:**** :warning: *_provide details about the model you used in your experiments (if more than use a list). If your model is small, describing it as a table would be :100:. Some FL methods do not use an off-the-shelve model (e.g. ResNet18) instead they create your own. If this is your case, please provide a summary here and give pointers to where in the paper (e.g. Appendix B.4) is detailed._* + +****Dataset:**** :warning: *_Earlier you listed already the datasets that your baseline uses. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table._* + +****Training Hyperparameters:**** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._* + + +## Environment Setup + +:warning: _The Python environment for all baselines should follow these guidelines in the `EXTENDED_README`. Specify the steps to create and activate your environment. If there are any external system-wide requirements, please include instructions for them too. These instructions should be comprehensive enough so anyone can run them (if non standard, describe them step-by-step)._ + + +## Running the Experiments + +:warning: _Provide instructions on the steps to follow to run all the experiments._ +```bash +# The main experiment implemented in your baseline using default hyperparameters (that should be setup in the Hydra configs) should run (including dataset download and necessary partitioning) by executing the command: + +poetry run -m .main # where is the name of this directory and that of the only sub-directory in this directory (i.e. where all your source code is) + +# If you are using a dataset that requires a complicated download (i.e. not using one natively supported by TF/PyTorch) + preprocessing logic, you might want to tell people to run one script first that will do all that. Please ensure the download + preprocessing can be configured to suit (at least!) a different download directory (and use as default the current directory). The expected command to run to do this is: + +poetry run -m .dataset_preparation + +# It is expected that you baseline supports more than one dataset and different FL settings (e.g. different number of clients, dataset partitioning methods, etc). Please provide a list of commands showing how these experiments are run. Include also a short explanation of what each one does. Here it is expected you'll be using the Hydra syntax to override the default config. + +poetry run -m .main +. +. +. +poetry run -m .main +``` + + +## Expected Results + +:warning: _Your baseline implementation should replicate several of the experiments in the original paper. Please include here the exact command(s) needed to run each of those experiments followed by a figure (e.g. a line plot) or table showing the results you obtained when you ran the code. Below is an example of how you can present this. Please add command followed by results for all your experiments._ + +```bash +# it is likely that for one experiment you need to sweep over different hyperparameters. You are encouraged to use Hydra's multirun functionality for this. This is an example of how you could achieve this for some typical FL hyperparameteres + +poetry run -m .main --multirun num_client_per_round=5,10,50 dataset=femnist,cifar10 +# the above command will run a total of 6 individual experiments (because 3client_configs x 2datasets = 6 -- you can think of it as a grid). + +[Now show a figure/table displaying the results of the above command] + +# add more commands + plots for additional experiments. +``` diff --git a/baselines/FedMeta/pyproject.toml b/baselines/FedMeta/pyproject.toml new file mode 100644 index 000000000000..013db24e8871 --- /dev/null +++ b/baselines/FedMeta/pyproject.toml @@ -0,0 +1,135 @@ +[build-system] +requires = ["poetry-core>=1.4.0"] +build-backend = "poetry.masonry.api" + +[tool.poetry] +name = "FedMeta" # <----- Ensure it matches the name of your baseline directory containing all the source code +version = "1.0.0" +description = "Flower Baselines" +license = "Apache-2.0" +authors = ["The Flower Authors "] +readme = "README.md" +homepage = "https://flower.dev" +repository = "https://github.com/adap/flower" +documentation = "https://flower.dev" +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: Apache Software License", + "Operating System :: MacOS :: MacOS X", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: Implementation :: CPython", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Scientific/Engineering :: Mathematics", + "Topic :: Software Development", + "Topic :: Software Development :: Libraries", + "Topic :: Software Development :: Libraries :: Python Modules", + "Typing :: Typed", +] + +[tool.poetry.dependencies] +python = ">=3.8.15, <3.12.0" # don't change this +flwr = "1.3.0" # don't change this +ray = "1.11.1" # don't change this +hydra-core = "1.3.2" # don't change this + +[tool.poetry.dev-dependencies] +isort = "==5.11.5" +black = "==23.1.0" +docformatter = "==1.5.1" +mypy = "==0.961" +pylint = "==2.8.2" +flake8 = "==3.9.2" +pytest = "==6.2.4" +pytest-watch = "==4.2.0" +ruff = "==0.0.272" +types-requests = "==2.27.7" + +[tool.isort] +line_length = 88 +indent = " " +multi_line_output = 3 +include_trailing_comma = true +force_grid_wrap = 0 +use_parentheses = true + +[tool.black] +line-length = 88 +target-version = ["py38", "py39", "py310", "py311"] + +[tool.pytest.ini_options] +minversion = "6.2" +addopts = "-qq" +testpaths = [ + "flwr_baselines", +] + +[tool.mypy] +ignore_missing_imports = true +strict = false +plugins = "numpy.typing.mypy_plugin" + +[tool.pylint."MESSAGES CONTROL"] +disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias" +good-names = "i,j,k,_,x,y,X,Y" +signature-mutators="hydra.main.main" + +[[tool.mypy.overrides]] +module = [ + "importlib.metadata.*", + "importlib_metadata.*", +] +follow_imports = "skip" +follow_imports_for_stubs = true +disallow_untyped_calls = false + +[[tool.mypy.overrides]] +module = "torch.*" +follow_imports = "skip" +follow_imports_for_stubs = true + +[tool.docformatter] +wrap-summaries = 88 +wrap-descriptions = 88 + +[tool.ruff] +target-version = "py38" +line-length = 88 +select = ["D", "E", "F", "W", "B", "ISC", "C4"] +fixable = ["D", "E", "F", "W", "B", "ISC", "C4"] +ignore = ["B024", "B027"] +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".hg", + ".mypy_cache", + ".nox", + ".pants.d", + ".pytype", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "venv", + "proto", +] + +[tool.ruff.pydocstyle] +convention = "numpy" From 4561d980b78a076b92fc9dcf5c9e772b688a5c58 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 23 Aug 2023 14:01:07 +0900 Subject: [PATCH 046/133] Test Source Tree --- baselines/FedMeta/README.md | 2 +- doc/source/tutorial-get-started-with-flower-pytorch.ipynb | 0 doc/source/tutorial-what-is-federated-learning.ipynb | 0 3 files changed, 1 insertion(+), 1 deletion(-) mode change 100755 => 100644 doc/source/tutorial-get-started-with-flower-pytorch.ipynb mode change 100755 => 100644 doc/source/tutorial-what-is-federated-learning.ipynb diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 0f9ecb972652..4cca63a1c006 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -16,7 +16,7 @@ dataset: [Femnist, Shakespeare] # list of datasets you include in your baseline ## About this baseline -****What’s implemented:**** :warning: *_Concisely describe what experiment(s) in the publication can be replicated by running the code. Please only use a few sentences. Start with: “The code in this directory …”_* +****What’s implemented:**** : I implement ~~~~~ ****Datasets:**** :warning: *_List the datasets you used (if you used a medium to large dataset, >10GB please also include the sizes of the dataset)._* diff --git a/doc/source/tutorial-get-started-with-flower-pytorch.ipynb b/doc/source/tutorial-get-started-with-flower-pytorch.ipynb old mode 100755 new mode 100644 diff --git a/doc/source/tutorial-what-is-federated-learning.ipynb b/doc/source/tutorial-what-is-federated-learning.ipynb old mode 100755 new mode 100644 From 60e51acab82f14e402fa84e98a54a6166fa49b08 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 23 Aug 2023 14:13:34 +0900 Subject: [PATCH 047/133] Section "About this baseline" --- baselines/FedMeta/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 4cca63a1c006..2edaf283df33 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -16,13 +16,13 @@ dataset: [Femnist, Shakespeare] # list of datasets you include in your baseline ## About this baseline -****What’s implemented:**** : I implement ~~~~~ +****What’s implemented:**** : I implement ~~ -****Datasets:**** :warning: *_List the datasets you used (if you used a medium to large dataset, >10GB please also include the sizes of the dataset)._* +****Datasets:**** : -****Hardware Setup:**** :warning: *_Give some details about the hardware (e.g. a server with 8x V100 32GB and 256GB of RAM) you used to run the experiments for this baseline. Someone out there might not have access to the same resources you have so, could list the absolute minimum hardware needed to run the experiment in a reasonable amount of time ? (e.g. minimum is 1x 16GB GPU otherwise a client model can’t be trained with a sufficiently large batch size). Could you test this works too?_* +****Hardware Setup:**** : -****Contributors:**** :warning: *_let the world know who contributed to this baseline. This could be either your name, your name and affiliation at the time, or your GitHub profile name if you prefer. If multiple contributors signed up for this baseline, please list yourself and your colleagues_* +****Contributors:**** : ## Experimental Setup From 4b4d63c9871cf32adf30965275d5773d8b35f0b5 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 23 Aug 2023 14:18:00 +0900 Subject: [PATCH 048/133] Test branch --- baselines/FedMeta/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 2edaf283df33..390d23715b96 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -18,9 +18,9 @@ dataset: [Femnist, Shakespeare] # list of datasets you include in your baseline ****What’s implemented:**** : I implement ~~ -****Datasets:**** : +****Datasets:**** : Dataset~ -****Hardware Setup:**** : +****Hardware Setup:**** : Hardware Setup ~ ****Contributors:**** : From 4862a25b726cbb9b491ee8515d202271b7c4ba12 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 23 Aug 2023 14:19:28 +0900 Subject: [PATCH 049/133] test branch --- baselines/FedMeta/FedMeta/Test_Source_Tree.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 baselines/FedMeta/FedMeta/Test_Source_Tree.py diff --git a/baselines/FedMeta/FedMeta/Test_Source_Tree.py b/baselines/FedMeta/FedMeta/Test_Source_Tree.py new file mode 100644 index 000000000000..e69de29bb2d1 From 3d9fde60490f91812c4cf87bf1e0753f8aedb292 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 23 Aug 2023 14:27:37 +0900 Subject: [PATCH 050/133] Delete Test file --- baselines/FedMeta/FedMeta/Test_Source_Tree.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 baselines/FedMeta/FedMeta/Test_Source_Tree.py diff --git a/baselines/FedMeta/FedMeta/Test_Source_Tree.py b/baselines/FedMeta/FedMeta/Test_Source_Tree.py deleted file mode 100644 index e69de29bb2d1..000000000000 From e75b0dce49a325bd7890d3a5935c78aaff3c6b1d Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 24 Aug 2023 09:27:27 +0900 Subject: [PATCH 051/133] update Fedmeta strategy base --- baselines/FedMeta/FedMeta/main.py | 25 ++++++++++++-- baselines/FedMeta/FedMeta/strategy.py | 47 +++++++++++++++++++++++++++ baselines/FedMeta/README.md | 2 +- 3 files changed, 70 insertions(+), 4 deletions(-) diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index 795800c64e21..b3a7c8b00657 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -7,6 +7,11 @@ # feel free to remove some if aren't needed import hydra from omegaconf import DictConfig, OmegaConf +from hydra.utils import instantiate +from strategy import FedMeta + +import flwr as fl + @hydra.main(config_path="conf", config_name="base", version_base=None) @@ -20,7 +25,6 @@ def main(cfg: DictConfig) -> None: """ # 1. Print parsed config print(OmegaConf.to_yaml(cfg)) - # 2. Prepare your dataset # here you should call a function in datasets.py that returns whatever is needed to: # (1) ensure the server can access the dataset used to evaluate your model after @@ -37,10 +41,22 @@ def main(cfg: DictConfig) -> None: # 4. Define your strategy # pass all relevant argument (including the global dataset used after aggregation, # if needed by your method.) - # strategy = instantiate(cfg.strategy, ) + strategy = instantiate( + cfg.strategy, + evaluate_fn=evaluate_fn, + on_fit_config_fn=get_on_fit_config(), + ) # 5. Start Simulation - # history = fl.simulation.start_simulation() + history = fl.simulation.start_simulation( + client_fn = "test", + num_clients = "test", + config = fl.server.ServerConfig(num_rounds=), + client_resources = { + + }, + strategy = strategy + ) # 6. Save your results # Here you can save the `history` returned by the simulation and include @@ -51,3 +67,6 @@ def main(cfg: DictConfig) -> None: # Hydra will generate for you a directory each time you run the code. You # can retrieve the path to that directory with this: # save_path = HydraConfig.get().runtime.output_dir + +if __name__ == "__main__": + main() diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index 17436c401c30..decd722dc543 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -3,3 +3,50 @@ Needed only when the strategy is not yet implemented in Flower or because you want to extend or modify the functionality of an existing strategy. """ +from typing import Dict, List, Optional, Tuple, Union +from logging import WARNING + +from flwr.server.client_proxy import ClientProxy +from flwr.server.strategy import FedAvg +from flwr.server.strategy.aggregate import aggregate + +from flwr.common.logger import log +from flwr.common import ( + FitRes, + Parameters, + Scalar, + ndarrays_to_parameters, + parameters_to_ndarrays, +) + + +class FedMeta(FedAvg): + def aggregate_fit( + self, + server_round: int, + results: List[Tuple[ClientProxy, FitRes]], + failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]], + ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]: + """Aggregate fit results using weighted average.""" + if not results: + return None, {} + # Do not aggregate if there are failures and failures are not accepted + if not self.accept_failures and failures: + return None, {} + + # Convert results + weights_results = [ + (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples) + for _, fit_res in results + ] + parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results)) + + # Aggregate custom metrics if aggregation fn was provided + metrics_aggregated = {} + if self.fit_metrics_aggregation_fn: + fit_metrics = [(res.num_examples, res.metrics) for _, res in results] + metrics_aggregated = self.fit_metrics_aggregation_fn(fit_metrics) + elif server_round == 1: # Only log this warning once + log(WARNING, "No fit_metrics_aggregation_fn provided") + + return parameters_aggregated, metrics_aggregated diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 390d23715b96..0ddf72e01b90 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -22,7 +22,7 @@ dataset: [Femnist, Shakespeare] # list of datasets you include in your baseline ****Hardware Setup:**** : Hardware Setup ~ -****Contributors:**** : +****Contributors:**** : Jinsoo Kim and Kangyoon Lee ## Experimental Setup From 1a361c2e262c539e3b34c719902d1ac5979779e7 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Mon, 28 Aug 2023 15:04:22 +0900 Subject: [PATCH 052/133] based fedmeta test --- baselines/FedMeta/FedMeta/client.py | 146 +++++++++++++++++++- baselines/FedMeta/FedMeta/conf/base.yaml | 17 --- baselines/FedMeta/FedMeta/conf/config.yaml | 35 +++++ baselines/FedMeta/FedMeta/dataset.py | 21 +++ baselines/FedMeta/FedMeta/main.py | 26 ++-- baselines/FedMeta/FedMeta/models.py | 151 +++++++++++++++++++++ baselines/FedMeta/FedMeta/strategy.py | 56 +++++++- 7 files changed, 421 insertions(+), 31 deletions(-) delete mode 100644 baselines/FedMeta/FedMeta/conf/base.yaml create mode 100644 baselines/FedMeta/FedMeta/conf/config.yaml diff --git a/baselines/FedMeta/FedMeta/client.py b/baselines/FedMeta/FedMeta/client.py index d2e2206111f3..49f3e60367fa 100644 --- a/baselines/FedMeta/FedMeta/client.py +++ b/baselines/FedMeta/FedMeta/client.py @@ -1,5 +1,145 @@ """Define your client class and a function to construct such clients. - -Please overwrite `flwr.client.NumPyClient` or `flwr.client.Client` and create a function -to instantiate your client. """ + +from collections import OrderedDict +from typing import Callable, Dict, List, Tuple +from omegaconf import DictConfig +from hydra.utils import instantiate + +import flwr as fl +from flwr.common.typing import NDArrays, Scalar + +import torch.nn +from torch.utils.data import DataLoader + +from models import train, test + +import json +import random +from dataset import FemnistDataset +import torchvision.transforms as transforms + + +class FlowerClient( + fl.client.NumPyClient +): + def __init__( + self, + net: torch.nn.Module, + trainloader: DataLoader, + valloader: DataLoader, + device: torch.device, + num_epochs: int, + learning_rate: float + ) -> object: + self.net = net + self.trainloader = trainloader + self.valloader = valloader + self.device = device + self.num_epochs = num_epochs + self.learning_rate = learning_rate + + def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays: + """Returns the parameters of the current net.""" + return [val.cpu().numpy() for _, val in self.net.state_dict().items()] + + def set_parameters(self, parameters: NDArrays) -> None: + """Changes the parameters of the model using the given ones.""" + params_dict = zip(self.net.state_dict().keys(), parameters) + state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict}) + self.net.load_state_dict(state_dict, strict=True) + + def fit( + self, parameters: NDArrays, config: Dict[str, Scalar] + ) -> Tuple[NDArrays, int, Dict]: + """Implements distributed fit function for a given client.""" + self.set_parameters(parameters) + train( + self.net, + self.trainloader, + self.device, + epochs=self.num_epochs, + learning_rate=self.learning_rate, + ) + + return self.get_parameters({}), len(self.trainloader), {} + + def evaluate( + self, parameters: NDArrays, config: Dict[str, Scalar] + ) -> Tuple[float, int, Dict]: + """Implements distributed evaluation for a given client.""" + self.set_parameters(parameters) + loss, accuracy = test(self.net, self.valloader, self.device) + print("test now") + return float(loss), len(self.valloader), {"accuracy": float(accuracy)} + + +def gen_client_fn( + num_epochs: int, + # trainloaders: List[DataLoader], + # valloaders: List[DataLoader], + learning_rate: float, + model: DictConfig, +) -> Callable[[str], FlowerClient]: # pylint: disable=too-many-arguments + """Generates the client function that creates the Flower Clients. + + Parameters + ---------- + num_epochs : int + The number of local epochs each client should run the training for before + sending it to the server. + trainloaders: List[DataLoader] + A list of DataLoaders, each pointing to the dataset training partition + belonging to a particular client. + valloaders: List[DataLoader] + A list of DataLoaders, each pointing to the dataset validation partition + belonging to a particular client. + learning_rate : float + The learning rate for the SGD optimizer of clients. + + Returns + ------- + Tuple[Callable[[str], FlowerClient], DataLoader] + A tuple containing the client function that creates Flower Clients and + the DataLoader that will be used for testing + """ + + + def client_fn(cid: str) -> FlowerClient: + """Create a Flower client representing a single organization.""" + + print(f'cid : {cid}') + + # Load model + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + net = instantiate(model).to(device) + ''' + Test Dataset + ''' + transform = transforms.Compose([transforms.ToTensor()]) + with open("/Users/jinsookim/PycharmProjects/fedmeta/leaf_data/train/all_data_" + str(cid) + "_niid_1_keep_0_train_9.json", "r") as f: + train_json = json.load(f) + with open("/Users/jinsookim/PycharmProjects/fedmeta/leaf_data/test/all_data_" + str(cid) + "_niid_1_keep_0_test_9.json", "r") as f: + test_json = json.load(f) + round_user = random.choice(train_json['users']) + trainset = FemnistDataset(train_json['user_data'][round_user], transform) + testset = FemnistDataset(test_json['user_data'][round_user],transform) + + # Note: each client gets a different trainloader/valloader, so each client + # will train and evaluate on their own unique data + # trainloader = trainloaders[int(cid)] + # valloader = valloaders[int(cid)] + + trainloader = DataLoader(trainset, batch_size=10, shuffle=True) + valloader = DataLoader(testset) + + return FlowerClient( + net, + trainloader, + valloader, + device, + num_epochs, + learning_rate, + ) + + return client_fn diff --git a/baselines/FedMeta/FedMeta/conf/base.yaml b/baselines/FedMeta/FedMeta/conf/base.yaml deleted file mode 100644 index 2d65b3b989b2..000000000000 --- a/baselines/FedMeta/FedMeta/conf/base.yaml +++ /dev/null @@ -1,17 +0,0 @@ ---- -# this is the config that will be loaded as default by main.py -# Please follow the provided structure (this will ensuring all baseline follow -# a similar configuration structure and hence be easy to customise) - -dataset: - # dataset config - -model: - # model config - -strategy: - _target_: # points to your strategy (either custom or exiting in Flower) - # rest of strategy config - -client: - # client config diff --git a/baselines/FedMeta/FedMeta/conf/config.yaml b/baselines/FedMeta/FedMeta/conf/config.yaml new file mode 100644 index 000000000000..4f726d38bb7d --- /dev/null +++ b/baselines/FedMeta/FedMeta/conf/config.yaml @@ -0,0 +1,35 @@ +--- +# this is the config that will be loaded as default by main.py +# Please follow the provided structure (this will ensuring all baseline follow +# a similar configuration structure and hence be easy to customise) + +num_clients: 10 # total number of clients +num_epochs: 1 # number of local epochs +batch_size: 10 +clients_per_round: 4 +num_rounds: 10 +learning_rate: 0.03 + +client_resources: + num_cpus: 2 + num_gpus: 0.0 + +server_device: cpu + +dataset: + # dataset config + +model: + _target_: baselines.fedmeta.fedmeta.models.Femnist_network # model config + +strategy: + _target_: baselines.fedmeta.fedmeta.strategy.FedMeta + # points to your strategy (either custom or exiting in Flower) + fraction_fit : 0.3 + min_fit_clients: ${clients_per_round} + min_available_clients: ${clients_per_round} + + # rest of strategy config + +client: + # client config diff --git a/baselines/FedMeta/FedMeta/dataset.py b/baselines/FedMeta/FedMeta/dataset.py index 5e436abe12fb..3454dea227bf 100644 --- a/baselines/FedMeta/FedMeta/dataset.py +++ b/baselines/FedMeta/FedMeta/dataset.py @@ -8,3 +8,24 @@ `dataset_preparation.py` module. You can use all those functions from functions/methods defined here of course. """ +import json +import numpy as np +from torch.utils.data import DataLoader, Dataset +import torchvision.transforms as transforms + + +class FemnistDataset(Dataset): + def __init__(self, dataset, transform): + self.x = dataset['x'] + self.y = dataset['y'] + self.transform = transform + + def __getitem__(self, index): + input_data = np.array(self.x[index]).reshape(28, 28, 1) + if self.transform: + input_data = self.transform(input_data) + target_data = self.y[index] + return input_data, target_data + + def __len__(self): + return len(self.y) diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index b3a7c8b00657..cbcd10510c04 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -8,13 +8,13 @@ import hydra from omegaconf import DictConfig, OmegaConf from hydra.utils import instantiate -from strategy import FedMeta +from strategy import weighted_average import flwr as fl +import client - -@hydra.main(config_path="conf", config_name="base", version_base=None) +@hydra.main(config_path="conf", config_name="config", version_base=None) def main(cfg: DictConfig) -> None: """Run the baseline. @@ -38,22 +38,28 @@ def main(cfg: DictConfig) -> None: # simulation to instantiate each individual client # client_fn = client.() + client_fn = client.gen_client_fn( + num_epochs=cfg.num_epochs, + learning_rate=cfg.learning_rate, + model=cfg.model + ) # 4. Define your strategy # pass all relevant argument (including the global dataset used after aggregation, # if needed by your method.) + strategy = instantiate( cfg.strategy, - evaluate_fn=evaluate_fn, - on_fit_config_fn=get_on_fit_config(), + evaluate_metrics_aggregation_fn = weighted_average ) # 5. Start Simulation history = fl.simulation.start_simulation( - client_fn = "test", - num_clients = "test", - config = fl.server.ServerConfig(num_rounds=), - client_resources = { - + client_fn = client_fn, + num_clients=cfg.num_clients, + config = fl.server.ServerConfig(num_rounds=cfg.num_rounds), + client_resources={ + "num_cpus": cfg.client_resources.num_cpus, + "num_gpus": cfg.client_resources.num_gpus, }, strategy = strategy ) diff --git a/baselines/FedMeta/FedMeta/models.py b/baselines/FedMeta/FedMeta/models.py index 71fa553d1f59..3187035af1eb 100644 --- a/baselines/FedMeta/FedMeta/models.py +++ b/baselines/FedMeta/FedMeta/models.py @@ -5,3 +5,154 @@ config. In this way, swapping your model for another one can be done without changing the python code at all """ + +from typing import Tuple + +import torch +import torch.nn as nn +from torch.utils.data import DataLoader + + +class Femnist_network(nn.Module): + """Convolutional Neural Network architecture. + + As described in McMahan 2017 paper : + + [Communication-Efficient Learning of Deep Networks from + Decentralized Data] (https://arxiv.org/pdf/1602.05629.pdf) + """ + + def __init__(self) -> None: + super(Femnist_network, self).__init__() + self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5, padding=2) + self.maxpool1 = nn.MaxPool2d(kernel_size=(2, 2)) + self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2) + self.maxpool2 = nn.MaxPool2d(kernel_size=(2, 2)) + self.linear1 = nn.Linear(7 * 7 * 64, 2048) + self.linear2 = nn.Linear(2048, 62) + + def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: + """Forward pass of the CNN. + + Parameters + ---------- + input_tensor : torch.Tensor + Input Tensor that will pass through the network + + Returns + ------- + torch.Tensor + The resulting Tensor after it has passed through the network + """ + output_tensor = torch.relu(self.conv1(input_tensor)) + output_tensor = self.maxpool1(output_tensor) + output_tensor = torch.relu(self.conv2(output_tensor)) + output_tensor = self.maxpool2(output_tensor) + output_tensor = torch.flatten(output_tensor, start_dim=1) + output_tensor = torch.relu((self.linear1(output_tensor))) + output_tensor = self.linear2(output_tensor) + return output_tensor + + +def train( # pylint: disable=too-many-arguments + net: nn.Module, + trainloader: DataLoader, + device: torch.device, + epochs: int, + learning_rate: float, +) -> None: + """Train the network on the training set. + + Parameters + ---------- + net : nn.Module + The neural network to train. + trainloader : DataLoader + The DataLoader containing the data to train the network on. + device : torch.device + The device on which the model should be trained, either 'cpu' or 'cuda'. + epochs : int + The number of epochs the model should be trained for. + learning_rate : float + The learning rate for the SGD optimizer. + """ + criterion = torch.nn.CrossEntropyLoss() + optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate, weight_decay=0.001) + net.train() + for _ in range(epochs): + net = _train_one_epoch( + net, trainloader, device, criterion, optimizer + ) + + +def _train_one_epoch( # pylint: disable=too-many-arguments + net: nn.Module, + trainloader: DataLoader, + device: torch.device, + criterion: torch.nn.CrossEntropyLoss, + optimizer: torch.optim.Adam, +) -> nn.Module: + """Train for one epoch. + + Parameters + ---------- + net : nn.Module + The neural network to train. + trainloader : DataLoader + The DataLoader containing the data to train the network on. + device : torch.device + The device on which the model should be trained, either 'cpu' or 'cuda'. + criterion : torch.nn.CrossEntropyLoss + The loss function to use for training + optimizer : torch.optim.Adam + The optimizer to use for training + + Returns + ------- + nn.Module + The model that has been trained for one epoch. + """ + for images, labels in trainloader: + images, labels = images.to(device), labels.to(device) + optimizer.zero_grad() + loss = criterion(net(images.to(torch.float32)), labels) + loss.backward() + optimizer.step() + return net + + +def test( + net: nn.Module, testloader: DataLoader, device: torch.device +) -> Tuple[float, float]: + """Evaluate the network on the entire test set. + + Parameters + ---------- + net : nn.Module + The neural network to test. + testloader : DataLoader + The DataLoader containing the data to test the network on. + device : torch.device + The device on which the model should be tested, either 'cpu' or 'cuda'. + + Returns + ------- + Tuple[float, float] + The loss and the accuracy of the input model on the given data. + """ + criterion = torch.nn.CrossEntropyLoss() + correct, total, loss = 0, 0, 0.0 + net.eval() + with torch.no_grad(): + for images, labels in testloader: + images, labels = images.to(device), labels.to(device) + outputs = net(images.to(torch.float32)) + loss += criterion(outputs, labels).item() + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + if len(testloader.dataset) == 0: + raise ValueError("Testloader can't be 0, exiting...") + loss /= len(testloader.dataset) + accuracy = correct / total + return loss, accuracy diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index decd722dc543..d4e150c38fb0 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -8,7 +8,7 @@ from flwr.server.client_proxy import ClientProxy from flwr.server.strategy import FedAvg -from flwr.server.strategy.aggregate import aggregate +from flwr.server.strategy.aggregate import aggregate, weighted_loss_avg from flwr.common.logger import log from flwr.common import ( @@ -17,9 +17,32 @@ Scalar, ndarrays_to_parameters, parameters_to_ndarrays, + EvaluateRes, + Metrics ) +def weighted_average(metrics: List[Tuple[int, Metrics]]) -> dict: + """Aggregation function for weighted average during evaluation. + + Parameters + ---------- + metrics : List[Tuple[int, Metrics]] + The list of metrics to aggregate. + + Returns + ------- + Metrics + The weighted average metric. + """ + # Multiply accuracy of each client by number of examples used + accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics] + examples = [num_examples for num_examples, _ in metrics] + + # Aggregate and return custom metric (weighted average) + return {"accuracy": int(sum(accuracies)) / int(sum(examples))} + + class FedMeta(FedAvg): def aggregate_fit( self, @@ -50,3 +73,34 @@ def aggregate_fit( log(WARNING, "No fit_metrics_aggregation_fn provided") return parameters_aggregated, metrics_aggregated + + def aggregate_evaluate( + self, + server_round: int, + results: List[Tuple[ClientProxy, EvaluateRes]], + failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]], + ) -> Tuple[Optional[float], Dict[str, Scalar]]: + """Aggregate evaluation losses using weighted average.""" + if not results: + return None, {} + # Do not aggregate if there are failures and failures are not accepted + if not self.accept_failures and failures: + return None, {} + + # Aggregate loss + loss_aggregated = weighted_loss_avg( + [ + (evaluate_res.num_examples, evaluate_res.loss) + for _, evaluate_res in results + ] + ) + + # Aggregate custom metrics if aggregation fn was provided + metrics_aggregated = {} + if self.evaluate_metrics_aggregation_fn: + eval_metrics = [(res.num_examples, res.metrics) for _, res in results] + metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics) + elif server_round == 1: # Only log this warning once + log(WARNING, "No evaluate_metrics_aggregation_fn provided") + + return loss_aggregated, metrics_aggregated From f075201b5020641322fa0e3309c7e73801e06230 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 31 Aug 2023 09:23:20 +0900 Subject: [PATCH 053/133] add Fedmeta data preprocessing & client list add evaluate client selection --- .../FedMeta/FedMeta/Fedmeta_client_manager.py | 45 ++++++++ baselines/FedMeta/FedMeta/client.py | 5 +- baselines/FedMeta/FedMeta/conf/config.yaml | 17 ++- baselines/FedMeta/FedMeta/dataset.py | 28 ++++- .../FedMeta/FedMeta/dataset_preparation.py | 109 +++++++++++++++--- baselines/FedMeta/FedMeta/main.py | 44 ++++--- baselines/FedMeta/FedMeta/strategy.py | 59 +++++++++- 7 files changed, 247 insertions(+), 60 deletions(-) create mode 100644 baselines/FedMeta/FedMeta/Fedmeta_client_manager.py diff --git a/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py new file mode 100644 index 000000000000..d7a2644b7920 --- /dev/null +++ b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py @@ -0,0 +1,45 @@ +from flwr.server.client_manager import SimpleClientManager +from typing import List, Optional +from logging import INFO +from flwr.common.logger import log +from flwr.server.criterion import Criterion +from flwr.server.client_proxy import ClientProxy +import random + + +class evaluate_client_Criterion(Criterion): + """Criterion to select evaluate clients.""" + def select(self, evaluate_clients: int) -> bool: + return [str(result) for result in range(0, evaluate_clients)] + + +class Fedmeta_client_manager(SimpleClientManager): + def sample( + self, + num_clients: int, + min_num_clients: Optional[int] = None, + min_evaluate_clients: Optional[int] = None, + criterion: Optional[Criterion] = None, + ) -> List[ClientProxy]: + """Sample a number of Flower ClientProxy instances.""" + # Block until at least num_clients are connected. + if min_num_clients is None: + min_num_clients = num_clients + self.wait_for(min_num_clients) + # Sample clients which meet the criterion + available_cids = list(self.clients) + if criterion is not None: + available_cids = criterion.select(min_evaluate_clients) + + if num_clients > len(available_cids): + log( + INFO, + "Sampling failed: number of available clients" + " (%s) is less than number of requested clients (%s).", + len(available_cids), + num_clients, + ) + return [] + + sampled_cids = random.sample(available_cids, num_clients) + return [self.clients[cid] for cid in sampled_cids] diff --git a/baselines/FedMeta/FedMeta/client.py b/baselines/FedMeta/FedMeta/client.py index 49f3e60367fa..932f98e21a33 100644 --- a/baselines/FedMeta/FedMeta/client.py +++ b/baselines/FedMeta/FedMeta/client.py @@ -70,14 +70,13 @@ def evaluate( """Implements distributed evaluation for a given client.""" self.set_parameters(parameters) loss, accuracy = test(self.net, self.valloader, self.device) - print("test now") return float(loss), len(self.valloader), {"accuracy": float(accuracy)} def gen_client_fn( num_epochs: int, - # trainloaders: List[DataLoader], - # valloaders: List[DataLoader], + # dataset: Tuple[Dict], + # client_list: List[str], learning_rate: float, model: DictConfig, ) -> Callable[[str], FlowerClient]: # pylint: disable=too-many-arguments diff --git a/baselines/FedMeta/FedMeta/conf/config.yaml b/baselines/FedMeta/FedMeta/conf/config.yaml index 4f726d38bb7d..279e93a041a0 100644 --- a/baselines/FedMeta/FedMeta/conf/config.yaml +++ b/baselines/FedMeta/FedMeta/conf/config.yaml @@ -3,20 +3,20 @@ # Please follow the provided structure (this will ensuring all baseline follow # a similar configuration structure and hence be easy to customise) -num_clients: 10 # total number of clients +num_clients: 35 # total number of clients num_epochs: 1 # number of local epochs -batch_size: 10 -clients_per_round: 4 +clients_per_round: 10 num_rounds: 10 learning_rate: 0.03 client_resources: - num_cpus: 2 - num_gpus: 0.0 server_device: cpu dataset: + path: /Users/jinsookim/PycharmProjects/fedmeta/leaf_data # Leaf Dataset path (Femnist or Shakespeare) + support_ratio : 0.2 + batch_size : 10 # dataset config model: @@ -25,10 +25,9 @@ model: strategy: _target_: baselines.fedmeta.fedmeta.strategy.FedMeta # points to your strategy (either custom or exiting in Flower) - fraction_fit : 0.3 - min_fit_clients: ${clients_per_round} - min_available_clients: ${clients_per_round} - + fraction_fit : 0.1 + fraction_evaluate : 0.1 + min_available_clients : ${clients_per_round} # rest of strategy config client: diff --git a/baselines/FedMeta/FedMeta/dataset.py b/baselines/FedMeta/FedMeta/dataset.py index 3454dea227bf..8cb7d9507c31 100644 --- a/baselines/FedMeta/FedMeta/dataset.py +++ b/baselines/FedMeta/FedMeta/dataset.py @@ -8,11 +8,12 @@ `dataset_preparation.py` module. You can use all those functions from functions/methods defined here of course. """ -import json + import numpy as np from torch.utils.data import DataLoader, Dataset -import torchvision.transforms as transforms - +from omegaconf import DictConfig +from typing import Optional, Tuple +from dataset_preparation import _partition_data, split_train_validation_test_clients class FemnistDataset(Dataset): def __init__(self, dataset, transform): @@ -29,3 +30,24 @@ def __getitem__(self, index): def __len__(self): return len(self.y) + + +def load_datasets( # pylint: disable=too-many-arguments + config: DictConfig, + seed: Optional[int] = 42, +) -> Tuple[DataLoader, DataLoader, DataLoader]: + print(f"Dataset partitioning config: {config}") + + dataset = _partition_data( + dir_path= config.path, + support_ratio= config.support_ratio + ) + + clients_list = split_train_validation_test_clients( + dataset[0]['users'] + ) + + return dataset, clients_list + + + diff --git a/baselines/FedMeta/FedMeta/dataset_preparation.py b/baselines/FedMeta/FedMeta/dataset_preparation.py index bd3440b9276b..05557c1dbaff 100644 --- a/baselines/FedMeta/FedMeta/dataset_preparation.py +++ b/baselines/FedMeta/FedMeta/dataset_preparation.py @@ -6,29 +6,100 @@ uncomment the lines below and tell us in the README.md (see the "Running the Experiment" block) that this file should be executed first. """ -# import hydra -# from hydra.core.hydra_config import HydraConfig -# from hydra.utils import call, instantiate -# from omegaconf import DictConfig, OmegaConf +import json +import os +from typing import List, Optional, Tuple, Dict, DefaultDict +from collections import defaultdict +import numpy as np -# @hydra.main(config_path="conf", config_name="base", version_base=None) -# def download_and_preprocess(cfg: DictConfig) -> None: -# """Does everything needed to get the dataset. +# def _read_dataset() -> Dict[List, Dict, List]: +def _read_dataset( + path: str +) -> Tuple[List, DefaultDict]: + """Read (if necessary) and returns the leaf dataset. -# Parameters -# ---------- -# cfg : DictConfig -# An omegaconf object that stores the hydra config. -# """ + Returns + ------- + Tuple[user, data[x,y]] + The dataset for training and the dataset for testing Femnist. + """ + users = [] + data = defaultdict(lambda: None) -# ## 1. print parsed config -# print(OmegaConf.to_yaml(cfg)) + files = [f for f in os.listdir(path) if f.endswith('.json')] -# # Please include here all the logic -# # Please use the Hydra config style as much as possible specially -# # for parts that can be customised (e.g. how data is partitioned) + for file_name in files: + with open(f'{path}/{file_name}') as f: + dataset = json.load(f) + users.extend(dataset['users']) + data.update(dataset['user_data']) -# if __name__ == "__main__": + return users, data -# download_and_preprocess() + +def support_query_split( + data: DefaultDict, + label: List, + support_ratio: int, + seed: Optional[int] = 42, +): + np.random.seed(seed) + random_index = np.random.permutation(len(label)) + slice_index = int(len(label) * support_ratio) + train_index = random_index[:slice_index] + test_index = random_index[slice_index:] + return data[train_index].tolist(), data[test_index].tolist(), label[train_index].tolist(), label[test_index].tolist() + + +def split_train_validation_test_clients( + clients: List, + train_rate: Optional[float] = 0.8, + val_rate: Optional[float] = 0.1, + seed: Optional[int] = 42 +) -> Tuple[List[str], List[str], List[str]]: + + np.random.seed(seed) + train_rate = int(train_rate * len(clients)) + val_rate = int(val_rate * len(clients)) + test_rate = len(clients) - train_rate - val_rate + + index = np.random.permutation(len(clients)) + trans_numpy = np.asarray(clients) + train_clients = trans_numpy[index[:train_rate]].tolist() + val_clients = trans_numpy[index[train_rate:train_rate + val_rate]].tolist() + test_clients = trans_numpy[index[train_rate + val_rate:]].tolist() + + return train_clients, val_clients, test_clients + + +def _partition_data( + dir_path: str, + support_ratio: float, + seed: Optional[int] = 42, +) -> Tuple[Dict, Dict]: + + train_path = f'{dir_path}/train' + test_path = f'{dir_path}/test' + + train_users, train_data = _read_dataset(train_path) + test_users, test_data = _read_dataset(test_path) + + support_dataset = {'users': [], 'user_data': {}, 'num_samples': []} + query_dataset = {'users': [], 'user_data': {}, 'num_samples': []} + + for user in train_users: + print(f'now preprocessing user : {user}') + all_x = np.asarray(train_data[user]['x'] + test_data[user]['x']) + all_y = np.asarray(train_data[user]['y'] + test_data[user]['y']) + sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio, seed) + + support_dataset['users'].append(user) + support_dataset['user_data'][user] = {'x': sup_x, 'y': sup_y} + support_dataset['num_samples'].append(len(sup_y)) + + query_dataset['users'].append(user) + query_dataset['user_data'][user] = {'x': qry_x, 'y': qry_y} + query_dataset['num_samples'].append(len(qry_y)) + + return support_dataset, query_dataset diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index cbcd10510c04..a10dc9efc3ea 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -9,6 +9,8 @@ from omegaconf import DictConfig, OmegaConf from hydra.utils import instantiate from strategy import weighted_average +from dataset import load_datasets +from Fedmeta_client_manager import Fedmeta_client_manager import flwr as fl import client @@ -23,45 +25,39 @@ def main(cfg: DictConfig) -> None: cfg : DictConfig An omegaconf object that stores the hydra config. """ - # 1. Print parsed config + # print config structured as YAML print(OmegaConf.to_yaml(cfg)) - # 2. Prepare your dataset - # here you should call a function in datasets.py that returns whatever is needed to: - # (1) ensure the server can access the dataset used to evaluate your model after - # aggregation - # (2) tell each client what dataset partitions they should use (e.g. a this could - # be a location in the file system, a list of dataloader, a list of ids to extract - # from a dataset, it's up to you) - # 3. Define your clients - # Define a function that returns another function that will be used during - # simulation to instantiate each individual client - # client_fn = client.() + # partition dataset and get dataloaders + # dataset, client_list = load_datasets(config=cfg.dataset) + # train_clients, val_clients, test_clients = client_list + # prepare function that will be used to spawn each client client_fn = client.gen_client_fn( num_epochs=cfg.num_epochs, + # dataset=dataset, + # client_list=client_list, learning_rate=cfg.learning_rate, - model=cfg.model + model=cfg.model, ) - # 4. Define your strategy - # pass all relevant argument (including the global dataset used after aggregation, - # if needed by your method.) strategy = instantiate( cfg.strategy, - evaluate_metrics_aggregation_fn = weighted_average + evaluate_metrics_aggregation_fn=weighted_average, + # min_evaluate_clients=len(val_clients), + min_fit_clients=7, + min_evaluate_clients=3, + # min_fit_client=len(train_clients) ) # 5. Start Simulation history = fl.simulation.start_simulation( - client_fn = client_fn, + client_fn=client_fn, + # num_clients=len(train_clients), num_clients=cfg.num_clients, - config = fl.server.ServerConfig(num_rounds=cfg.num_rounds), - client_resources={ - "num_cpus": cfg.client_resources.num_cpus, - "num_gpus": cfg.client_resources.num_gpus, - }, - strategy = strategy + config=fl.server.ServerConfig(num_rounds=cfg.num_rounds), + strategy=strategy, + client_manager=Fedmeta_client_manager() ) # 6. Save your results diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index d4e150c38fb0..2b894609f1a6 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -9,6 +9,8 @@ from flwr.server.client_proxy import ClientProxy from flwr.server.strategy import FedAvg from flwr.server.strategy.aggregate import aggregate, weighted_loss_avg +from flwr.server.client_manager import ClientManager +from Fedmeta_client_manager import evaluate_client_Criterion from flwr.common.logger import log from flwr.common import ( @@ -18,11 +20,13 @@ ndarrays_to_parameters, parameters_to_ndarrays, EvaluateRes, - Metrics + Metrics, + FitIns, + EvaluateIns, ) -def weighted_average(metrics: List[Tuple[int, Metrics]]) -> dict: +def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: """Aggregation function for weighted average during evaluation. Parameters @@ -44,6 +48,57 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> dict: class FedMeta(FedAvg): + def configure_fit( + self, server_round: int, parameters: Parameters, client_manager: ClientManager + ) -> List[Tuple[ClientProxy, FitIns]]: + """Configure the next round of training.""" + config = {} + if self.on_fit_config_fn is not None: + # Custom fit config function provided + config = self.on_fit_config_fn(server_round) + fit_ins = FitIns(parameters, config) + + # Sample clients + sample_size, min_num_clients = self.num_fit_clients( + client_manager.num_available() + ) + clients = client_manager.sample( + num_clients=sample_size, min_num_clients=min_num_clients + ) + + # Return client/config pairs + return [(client, fit_ins) for client in clients] + + def configure_evaluate( + self, server_round: int, parameters: Parameters, client_manager: ClientManager + ) -> List[Tuple[ClientProxy, EvaluateIns]]: + """Configure the next round of evaluation.""" + # Do not configure federated evaluation if fraction eval is 0. + if self.fraction_evaluate == 0.0: + return [] + + # Parameters and config + config = {} + if self.on_evaluate_config_fn is not None: + # Custom evaluation config function provided + config = self.on_evaluate_config_fn(server_round) + evaluate_ins = EvaluateIns(parameters, config) + + # Sample clients + sample_size, min_num_clients = self.num_evaluation_clients( + client_manager.num_available() + ) + clients = client_manager.sample( + num_clients=sample_size, + min_num_clients=min_num_clients, + min_evaluate_clients=self.min_evaluate_clients, + criterion=evaluate_client_Criterion() + ) + + # Return client/config pairs + return [(client, evaluate_ins) for client in clients] + + def aggregate_fit( self, server_round: int, From 5312b1363a619c36295c370cc30c8c048d8fd58f Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 31 Aug 2023 13:55:30 +0900 Subject: [PATCH 054/133] fix Fedmeta base code --- .../FedMeta/FedMeta/Fedmeta_client_manager.py | 10 ++-- baselines/FedMeta/FedMeta/client.py | 29 ++++------ baselines/FedMeta/FedMeta/conf/config.yaml | 10 ++-- baselines/FedMeta/FedMeta/main.py | 37 +++++++++---- baselines/FedMeta/FedMeta/models.py | 2 +- baselines/FedMeta/FedMeta/server.py | 54 +++++++++++++++++-- baselines/FedMeta/FedMeta/strategy.py | 25 +-------- 7 files changed, 101 insertions(+), 66 deletions(-) diff --git a/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py index d7a2644b7920..b5708b6dd75c 100644 --- a/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py +++ b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py @@ -8,9 +8,12 @@ class evaluate_client_Criterion(Criterion): + def __init__(self, min_evaluate_clients): + self.min_evaluate_clients = min_evaluate_clients + """Criterion to select evaluate clients.""" - def select(self, evaluate_clients: int) -> bool: - return [str(result) for result in range(0, evaluate_clients)] + def select(self, clients_num: int) -> bool: + return [str(result) for result in range(0, min(self.min_evaluate_clients, clients_num))] class Fedmeta_client_manager(SimpleClientManager): @@ -18,7 +21,6 @@ def sample( self, num_clients: int, min_num_clients: Optional[int] = None, - min_evaluate_clients: Optional[int] = None, criterion: Optional[Criterion] = None, ) -> List[ClientProxy]: """Sample a number of Flower ClientProxy instances.""" @@ -29,7 +31,7 @@ def sample( # Sample clients which meet the criterion available_cids = list(self.clients) if criterion is not None: - available_cids = criterion.select(min_evaluate_clients) + available_cids = criterion.select(len(self.clients)) if num_clients > len(available_cids): log( diff --git a/baselines/FedMeta/FedMeta/client.py b/baselines/FedMeta/FedMeta/client.py index 932f98e21a33..6f30cf6943c5 100644 --- a/baselines/FedMeta/FedMeta/client.py +++ b/baselines/FedMeta/FedMeta/client.py @@ -9,13 +9,12 @@ import flwr as fl from flwr.common.typing import NDArrays, Scalar +import torch import torch.nn from torch.utils.data import DataLoader from models import train, test -import json -import random from dataset import FemnistDataset import torchvision.transforms as transforms @@ -75,8 +74,8 @@ def evaluate( def gen_client_fn( num_epochs: int, - # dataset: Tuple[Dict], - # client_list: List[str], + dataset: Tuple[Dict], + client_list: List[str], learning_rate: float, model: DictConfig, ) -> Callable[[str], FlowerClient]: # pylint: disable=too-many-arguments @@ -103,7 +102,6 @@ def gen_client_fn( the DataLoader that will be used for testing """ - def client_fn(cid: str) -> FlowerClient: """Create a Flower client representing a single organization.""" @@ -112,25 +110,18 @@ def client_fn(cid: str) -> FlowerClient: # Load model device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") net = instantiate(model).to(device) - ''' - Test Dataset - ''' transform = transforms.Compose([transforms.ToTensor()]) - with open("/Users/jinsookim/PycharmProjects/fedmeta/leaf_data/train/all_data_" + str(cid) + "_niid_1_keep_0_train_9.json", "r") as f: - train_json = json.load(f) - with open("/Users/jinsookim/PycharmProjects/fedmeta/leaf_data/test/all_data_" + str(cid) + "_niid_1_keep_0_test_9.json", "r") as f: - test_json = json.load(f) - round_user = random.choice(train_json['users']) - trainset = FemnistDataset(train_json['user_data'][round_user], transform) - testset = FemnistDataset(test_json['user_data'][round_user],transform) # Note: each client gets a different trainloader/valloader, so each client # will train and evaluate on their own unique data - # trainloader = trainloaders[int(cid)] - # valloader = valloaders[int(cid)] + sup_set, qry_set = dataset + train_clients, val_client, _ = client_list + + train_set = sup_set['user_data'][train_clients[int(cid)]] + valid_set = sup_set['user_data'][val_client[int(cid)]] - trainloader = DataLoader(trainset, batch_size=10, shuffle=True) - valloader = DataLoader(testset) + trainloader = DataLoader(FemnistDataset(train_set, transform), batch_size=10, shuffle=True) + valloader = DataLoader(FemnistDataset(valid_set, transform)) return FlowerClient( net, diff --git a/baselines/FedMeta/FedMeta/conf/config.yaml b/baselines/FedMeta/FedMeta/conf/config.yaml index 279e93a041a0..36e9013ffabd 100644 --- a/baselines/FedMeta/FedMeta/conf/config.yaml +++ b/baselines/FedMeta/FedMeta/conf/config.yaml @@ -3,9 +3,9 @@ # Please follow the provided structure (this will ensuring all baseline follow # a similar configuration structure and hence be easy to customise) -num_clients: 35 # total number of clients +num_clients: 15 # total number of clients num_epochs: 1 # number of local epochs -clients_per_round: 10 +clients_per_round: 4 num_rounds: 10 learning_rate: 0.03 @@ -25,8 +25,10 @@ model: strategy: _target_: baselines.fedmeta.fedmeta.strategy.FedMeta # points to your strategy (either custom or exiting in Flower) - fraction_fit : 0.1 - fraction_evaluate : 0.1 + fraction_fit: 0.00001 + fraction_evaluate: 0.00001 + min_fit_clients : ${clients_per_round} + min_evaluate_clients : ${clients_per_round} min_available_clients : ${clients_per_round} # rest of strategy config diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index a10dc9efc3ea..c979dd96b815 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -11,6 +11,9 @@ from strategy import weighted_average from dataset import load_datasets from Fedmeta_client_manager import Fedmeta_client_manager +from flwr.common.logger import log +from logging import WARNING +import server import flwr as fl import client @@ -29,35 +32,48 @@ def main(cfg: DictConfig) -> None: print(OmegaConf.to_yaml(cfg)) # partition dataset and get dataloaders - # dataset, client_list = load_datasets(config=cfg.dataset) - # train_clients, val_clients, test_clients = client_list + dataset, client_list = load_datasets(config=cfg.dataset) + train_clients, val_clients, test_clients = client_list + + # Check config Clients value + if cfg.num_clients > len(train_clients): + raise ImportError(f"Total Clients num is {len(train_clients)}") + + if cfg.min_evaluate_clients > len(val_clients): + min_evaluate_clients = min(len(val_clients), cfg.min_evaluate_clients) + log(WARNING, "min_evaluate_clients iis smaller than Validation Clients") # prepare function that will be used to spawn each client client_fn = client.gen_client_fn( num_epochs=cfg.num_epochs, - # dataset=dataset, - # client_list=client_list, + dataset=dataset, + client_list=client_list, learning_rate=cfg.learning_rate, model=cfg.model, ) + # device = cfg.server_device + # evaluate_fn = server.gen_evaluate_fn( + # dataset=dataset, + # device=device, + # model=cfg.model + # ) + + strategy = instantiate( cfg.strategy, + # evaluate_fn=evaluate_fn, evaluate_metrics_aggregation_fn=weighted_average, - # min_evaluate_clients=len(val_clients), - min_fit_clients=7, - min_evaluate_clients=3, - # min_fit_client=len(train_clients) + min_evaluate_clients=int(min_evaluate_clients), ) # 5. Start Simulation history = fl.simulation.start_simulation( client_fn=client_fn, - # num_clients=len(train_clients), num_clients=cfg.num_clients, config=fl.server.ServerConfig(num_rounds=cfg.num_rounds), + client_manager=Fedmeta_client_manager(), strategy=strategy, - client_manager=Fedmeta_client_manager() ) # 6. Save your results @@ -70,5 +86,6 @@ def main(cfg: DictConfig) -> None: # can retrieve the path to that directory with this: # save_path = HydraConfig.get().runtime.output_dir + if __name__ == "__main__": main() diff --git a/baselines/FedMeta/FedMeta/models.py b/baselines/FedMeta/FedMeta/models.py index 3187035af1eb..9de15b834ec7 100644 --- a/baselines/FedMeta/FedMeta/models.py +++ b/baselines/FedMeta/FedMeta/models.py @@ -77,7 +77,7 @@ def train( # pylint: disable=too-many-arguments The learning rate for the SGD optimizer. """ criterion = torch.nn.CrossEntropyLoss() - optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate, weight_decay=0.001) + optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) net.train() for _ in range(epochs): net = _train_one_epoch( diff --git a/baselines/FedMeta/FedMeta/server.py b/baselines/FedMeta/FedMeta/server.py index 2fd7d42cde5a..8bd063ad2891 100644 --- a/baselines/FedMeta/FedMeta/server.py +++ b/baselines/FedMeta/FedMeta/server.py @@ -1,5 +1,51 @@ -"""Create global evaluation function. +from collections import OrderedDict +from typing import Callable, Dict, Optional, Tuple -Optionally, also define a new Server class (please note this is not needed in most -settings). -""" +import torch +from flwr.common.typing import NDArrays, Scalar +from hydra.utils import instantiate +from omegaconf import DictConfig +from torch.utils.data import DataLoader + +from fedprox.models import test + + +def gen_evaluate_fn( + testloader: DataLoader, + device: torch.device, + model: DictConfig, +) -> Callable[ + [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]] +]: + """Generates the function for centralized evaluation. + + Parameters + ---------- + testloader : DataLoader + The dataloader to test the model with. + device : torch.device + The device to test the model on. + + Returns + ------- + Callable[ [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]] ] + The centralized evaluation function. + """ + + def evaluate( + server_round: int, parameters_ndarrays: NDArrays, config: Dict[str, Scalar] + ) -> Optional[Tuple[float, Dict[str, Scalar]]]: + # pylint: disable=unused-argument + """Use the entire CIFAR-10 test set for evaluation.""" + + net = instantiate(model) + params_dict = zip(net.state_dict().keys(), parameters_ndarrays) + state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict}) + net.load_state_dict(state_dict, strict=True) + net.to(device) + + loss, accuracy = test(net, testloader, device=device) + # return statistics + return loss, {"accuracy": accuracy} + + return evaluate diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index 2b894609f1a6..97e142e847c5 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -48,27 +48,6 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: class FedMeta(FedAvg): - def configure_fit( - self, server_round: int, parameters: Parameters, client_manager: ClientManager - ) -> List[Tuple[ClientProxy, FitIns]]: - """Configure the next round of training.""" - config = {} - if self.on_fit_config_fn is not None: - # Custom fit config function provided - config = self.on_fit_config_fn(server_round) - fit_ins = FitIns(parameters, config) - - # Sample clients - sample_size, min_num_clients = self.num_fit_clients( - client_manager.num_available() - ) - clients = client_manager.sample( - num_clients=sample_size, min_num_clients=min_num_clients - ) - - # Return client/config pairs - return [(client, fit_ins) for client in clients] - def configure_evaluate( self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, EvaluateIns]]: @@ -91,14 +70,12 @@ def configure_evaluate( clients = client_manager.sample( num_clients=sample_size, min_num_clients=min_num_clients, - min_evaluate_clients=self.min_evaluate_clients, - criterion=evaluate_client_Criterion() + criterion=evaluate_client_Criterion(self.min_evaluate_clients) ) # Return client/config pairs return [(client, evaluate_ins) for client in clients] - def aggregate_fit( self, server_round: int, From ba0f908a0ec37e2ee0a18fe6eedc79f3f44638c8 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 31 Aug 2023 16:04:25 +0900 Subject: [PATCH 055/133] add Fedavg dataset preprocessing --- baselines/FedMeta/FedMeta/conf/config.yaml | 1 + baselines/FedMeta/FedMeta/dataset.py | 14 ++++-- .../FedMeta/FedMeta/dataset_preparation.py | 50 ++++++++++++------- 3 files changed, 44 insertions(+), 21 deletions(-) diff --git a/baselines/FedMeta/FedMeta/conf/config.yaml b/baselines/FedMeta/FedMeta/conf/config.yaml index 36e9013ffabd..52f809c883a6 100644 --- a/baselines/FedMeta/FedMeta/conf/config.yaml +++ b/baselines/FedMeta/FedMeta/conf/config.yaml @@ -14,6 +14,7 @@ client_resources: server_device: cpu dataset: + algo: fedavg #fedavg, fedavg(meta), fedmeta(maml), fedmeta(meta-sgd) path: /Users/jinsookim/PycharmProjects/fedmeta/leaf_data # Leaf Dataset path (Femnist or Shakespeare) support_ratio : 0.2 batch_size : 10 diff --git a/baselines/FedMeta/FedMeta/dataset.py b/baselines/FedMeta/FedMeta/dataset.py index 8cb7d9507c31..fd3b4a705cab 100644 --- a/baselines/FedMeta/FedMeta/dataset.py +++ b/baselines/FedMeta/FedMeta/dataset.py @@ -38,10 +38,16 @@ def load_datasets( # pylint: disable=too-many-arguments ) -> Tuple[DataLoader, DataLoader, DataLoader]: print(f"Dataset partitioning config: {config}") - dataset = _partition_data( - dir_path= config.path, - support_ratio= config.support_ratio - ) + if config.algo == 'fedavg': + dataset = _partition_data( + dir_path= config.path + ) + + elif config.algo == 'fedmeta(maml)': + dataset = _partition_data( + dir_path= config.path, + support_ratio= config.support_ratio + ) clients_list = split_train_validation_test_clients( dataset[0]['users'] diff --git a/baselines/FedMeta/FedMeta/dataset_preparation.py b/baselines/FedMeta/FedMeta/dataset_preparation.py index 05557c1dbaff..03b4e6234793 100644 --- a/baselines/FedMeta/FedMeta/dataset_preparation.py +++ b/baselines/FedMeta/FedMeta/dataset_preparation.py @@ -49,7 +49,8 @@ def support_query_split( slice_index = int(len(label) * support_ratio) train_index = random_index[:slice_index] test_index = random_index[slice_index:] - return data[train_index].tolist(), data[test_index].tolist(), label[train_index].tolist(), label[test_index].tolist() + return data[train_index].tolist(), data[test_index].tolist(), label[train_index].tolist(), label[ + test_index].tolist() def split_train_validation_test_clients( @@ -58,7 +59,6 @@ def split_train_validation_test_clients( val_rate: Optional[float] = 0.1, seed: Optional[int] = 42 ) -> Tuple[List[str], List[str], List[str]]: - np.random.seed(seed) train_rate = int(train_rate * len(clients)) val_rate = int(val_rate * len(clients)) @@ -75,7 +75,7 @@ def split_train_validation_test_clients( def _partition_data( dir_path: str, - support_ratio: float, + support_ratio: Optional[float] = None, seed: Optional[int] = 42, ) -> Tuple[Dict, Dict]: @@ -85,21 +85,37 @@ def _partition_data( train_users, train_data = _read_dataset(train_path) test_users, test_data = _read_dataset(test_path) - support_dataset = {'users': [], 'user_data': {}, 'num_samples': []} - query_dataset = {'users': [], 'user_data': {}, 'num_samples': []} + if support_ratio is None: + train_dataset = {'users': [], 'user_data': {}, 'num_samples': []} + test_dataset = {'users': [], 'user_data': {}, 'num_samples': []} + + for user in train_users: + train_dataset['users'].append(user) + train_dataset['user_data'][user] = {'x': train_data[user]['x'], 'y': train_data[user]['y']} + train_dataset['num_samples'].append(len(train_data[user]['y'])) + + test_dataset['users'].append(user) + test_dataset['user_data'][user] = {'x': test_data[user]['x'], 'y': test_data[user]['y']} + test_dataset['num_samples'].append(len(test_data[user]['y'])) + + return train_dataset, test_dataset + + else: + support_dataset = {'users': [], 'user_data': {}, 'num_samples': []} + query_dataset = {'users': [], 'user_data': {}, 'num_samples': []} - for user in train_users: - print(f'now preprocessing user : {user}') - all_x = np.asarray(train_data[user]['x'] + test_data[user]['x']) - all_y = np.asarray(train_data[user]['y'] + test_data[user]['y']) - sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio, seed) + for user in train_users: + print(f'now preprocessing user : {user}') + all_x = np.asarray(train_data[user]['x'] + test_data[user]['x']) + all_y = np.asarray(train_data[user]['y'] + test_data[user]['y']) + sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio, seed) - support_dataset['users'].append(user) - support_dataset['user_data'][user] = {'x': sup_x, 'y': sup_y} - support_dataset['num_samples'].append(len(sup_y)) + support_dataset['users'].append(user) + support_dataset['user_data'][user] = {'x': sup_x, 'y': sup_y} + support_dataset['num_samples'].append(len(sup_y)) - query_dataset['users'].append(user) - query_dataset['user_data'][user] = {'x': qry_x, 'y': qry_y} - query_dataset['num_samples'].append(len(qry_y)) + query_dataset['users'].append(user) + query_dataset['user_data'][user] = {'x': qry_x, 'y': qry_y} + query_dataset['num_samples'].append(len(qry_y)) - return support_dataset, query_dataset + return support_dataset, query_dataset From 652ac7cfd9ef26ac43da4cab3366ce1e41a83396 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Fri, 1 Sep 2023 10:03:09 +0900 Subject: [PATCH 056/133] setting local --> server interpreter --- baselines/FedMeta/FedMeta/conf/config.yaml | 5 +++-- baselines/FedMeta/FedMeta/dataset.py | 6 +++--- baselines/FedMeta/FedMeta/main.py | 1 - baselines/FedMeta/FedMeta/server.py | 2 -- 4 files changed, 6 insertions(+), 8 deletions(-) diff --git a/baselines/FedMeta/FedMeta/conf/config.yaml b/baselines/FedMeta/FedMeta/conf/config.yaml index 52f809c883a6..6255dcae7757 100644 --- a/baselines/FedMeta/FedMeta/conf/config.yaml +++ b/baselines/FedMeta/FedMeta/conf/config.yaml @@ -14,8 +14,9 @@ client_resources: server_device: cpu dataset: - algo: fedavg #fedavg, fedavg(meta), fedmeta(maml), fedmeta(meta-sgd) - path: /Users/jinsookim/PycharmProjects/fedmeta/leaf_data # Leaf Dataset path (Femnist or Shakespeare) +# algo: fedavg #fedavg, fedavg(meta), fedmeta(maml), fedmeta(meta-sgd) + algo: fedmeta(maml) #fedavg, fedavg(meta), fedmeta(maml), fedmeta(meta-sgd) + path: # Leaf Dataset path (Femnist or Shakespeare) support_ratio : 0.2 batch_size : 10 # dataset config diff --git a/baselines/FedMeta/FedMeta/dataset.py b/baselines/FedMeta/FedMeta/dataset.py index fd3b4a705cab..31d21b32661a 100644 --- a/baselines/FedMeta/FedMeta/dataset.py +++ b/baselines/FedMeta/FedMeta/dataset.py @@ -40,13 +40,13 @@ def load_datasets( # pylint: disable=too-many-arguments if config.algo == 'fedavg': dataset = _partition_data( - dir_path= config.path + dir_path=config.path ) elif config.algo == 'fedmeta(maml)': dataset = _partition_data( - dir_path= config.path, - support_ratio= config.support_ratio + dir_path=config.path, + support_ratio=config.support_ratio ) clients_list = split_train_validation_test_clients( diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index c979dd96b815..a9a6a7394427 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -13,7 +13,6 @@ from Fedmeta_client_manager import Fedmeta_client_manager from flwr.common.logger import log from logging import WARNING -import server import flwr as fl import client diff --git a/baselines/FedMeta/FedMeta/server.py b/baselines/FedMeta/FedMeta/server.py index 8bd063ad2891..915e9944cffc 100644 --- a/baselines/FedMeta/FedMeta/server.py +++ b/baselines/FedMeta/FedMeta/server.py @@ -7,8 +7,6 @@ from omegaconf import DictConfig from torch.utils.data import DataLoader -from fedprox.models import test - def gen_evaluate_fn( testloader: DataLoader, From 4c9f435f693c63d4ed1fb758938f345a321af1f1 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Fri, 1 Sep 2023 18:04:14 +0900 Subject: [PATCH 057/133] add fedavg learning(ray & gpu) --- .../FedMeta/FedMeta/Fedmeta_client_manager.py | 10 +++-- baselines/FedMeta/FedMeta/client.py | 44 +++++++++++++------ baselines/FedMeta/FedMeta/main.py | 25 ++++------- baselines/FedMeta/FedMeta/models.py | 2 +- baselines/FedMeta/FedMeta/strategy.py | 6 ++- 5 files changed, 50 insertions(+), 37 deletions(-) diff --git a/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py index b5708b6dd75c..4d04e4ca141a 100644 --- a/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py +++ b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py @@ -12,11 +12,15 @@ def __init__(self, min_evaluate_clients): self.min_evaluate_clients = min_evaluate_clients """Criterion to select evaluate clients.""" - def select(self, clients_num: int) -> bool: - return [str(result) for result in range(0, min(self.min_evaluate_clients, clients_num))] + def select(self, valid_client: int) -> bool: + return [str(result) for result in range(0, valid_client)] class Fedmeta_client_manager(SimpleClientManager): + def __init__(self, valid_client, **kwargs): + super().__init__(**kwargs) + self.valid_client = valid_client + def sample( self, num_clients: int, @@ -31,7 +35,7 @@ def sample( # Sample clients which meet the criterion available_cids = list(self.clients) if criterion is not None: - available_cids = criterion.select(len(self.clients)) + available_cids = criterion.select(self.valid_client) if num_clients > len(available_cids): log( diff --git a/baselines/FedMeta/FedMeta/client.py b/baselines/FedMeta/FedMeta/client.py index 6f30cf6943c5..4bebfc50762d 100644 --- a/baselines/FedMeta/FedMeta/client.py +++ b/baselines/FedMeta/FedMeta/client.py @@ -25,18 +25,28 @@ class FlowerClient( def __init__( self, net: torch.nn.Module, - trainloader: DataLoader, - valloader: DataLoader, + # trainloader: DataLoader, + # valloader: DataLoader, + cid: int, + dataset: Tuple[Dict], + client_list: List[str], device: torch.device, num_epochs: int, learning_rate: float ) -> object: self.net = net - self.trainloader = trainloader - self.valloader = valloader + self.trainloader = None + self.valloader = None + self.cid = cid self.device = device self.num_epochs = num_epochs self.learning_rate = learning_rate + self.train_set, self.valid_set = dataset + self.train_client, self.valid_client, _ = client_list + + + self.transform = transforms.Compose([transforms.ToTensor()]) + def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays: """Returns the parameters of the current net.""" @@ -53,6 +63,8 @@ def fit( ) -> Tuple[NDArrays, int, Dict]: """Implements distributed fit function for a given client.""" self.set_parameters(parameters) + train_set = self.train_set['user_data'][self.train_client[int(self.cid)]] + self.trainloader = DataLoader(FemnistDataset(train_set, self.transform), batch_size=10, shuffle=True) train( self.net, self.trainloader, @@ -68,8 +80,10 @@ def evaluate( ) -> Tuple[float, int, Dict]: """Implements distributed evaluation for a given client.""" self.set_parameters(parameters) + valid_set = self.valid_set['user_data'][self.valid_client[int(self.cid)]] + self.valloader = DataLoader(FemnistDataset(valid_set, self.transform)) loss, accuracy = test(self.net, self.valloader, self.device) - return float(loss), len(self.valloader), {"accuracy": float(accuracy)} + return float(loss), len(self.valloader), {"correct": accuracy} def gen_client_fn( @@ -79,6 +93,7 @@ def gen_client_fn( learning_rate: float, model: DictConfig, ) -> Callable[[str], FlowerClient]: # pylint: disable=too-many-arguments + """Generates the client function that creates the Flower Clients. Parameters @@ -109,24 +124,25 @@ def client_fn(cid: str) -> FlowerClient: # Load model device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + # device = 'cpu' net = instantiate(model).to(device) - transform = transforms.Compose([transforms.ToTensor()]) + # transform = transforms.Compose([transforms.ToTensor()]) # Note: each client gets a different trainloader/valloader, so each client # will train and evaluate on their own unique data - sup_set, qry_set = dataset - train_clients, val_client, _ = client_list + # train_clients, val_client, _ = client_list - train_set = sup_set['user_data'][train_clients[int(cid)]] - valid_set = sup_set['user_data'][val_client[int(cid)]] + # train_set = sup_set['user_data'][train_clients[int(cid)]] + # valid_set = sup_set['user_data'][val_client[int(cid)]] - trainloader = DataLoader(FemnistDataset(train_set, transform), batch_size=10, shuffle=True) - valloader = DataLoader(FemnistDataset(valid_set, transform)) + # trainloader = DataLoader(FemnistDataset(train_set, transform), batch_size=10, shuffle=True) + # valloader = DataLoader(FemnistDataset(valid_set, transform), batch_size=10) return FlowerClient( net, - trainloader, - valloader, + cid, + dataset, + client_list, device, num_epochs, learning_rate, diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index a9a6a7394427..ea8d5c6202a2 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -11,8 +11,7 @@ from strategy import weighted_average from dataset import load_datasets from Fedmeta_client_manager import Fedmeta_client_manager -from flwr.common.logger import log -from logging import WARNING + import flwr as fl import client @@ -33,15 +32,10 @@ def main(cfg: DictConfig) -> None: # partition dataset and get dataloaders dataset, client_list = load_datasets(config=cfg.dataset) train_clients, val_clients, test_clients = client_list - # Check config Clients value if cfg.num_clients > len(train_clients): raise ImportError(f"Total Clients num is {len(train_clients)}") - if cfg.min_evaluate_clients > len(val_clients): - min_evaluate_clients = min(len(val_clients), cfg.min_evaluate_clients) - log(WARNING, "min_evaluate_clients iis smaller than Validation Clients") - # prepare function that will be used to spawn each client client_fn = client.gen_client_fn( num_epochs=cfg.num_epochs, @@ -49,21 +43,14 @@ def main(cfg: DictConfig) -> None: client_list=client_list, learning_rate=cfg.learning_rate, model=cfg.model, - ) - - # device = cfg.server_device - # evaluate_fn = server.gen_evaluate_fn( - # dataset=dataset, - # device=device, - # model=cfg.model - # ) + ) strategy = instantiate( cfg.strategy, # evaluate_fn=evaluate_fn, evaluate_metrics_aggregation_fn=weighted_average, - min_evaluate_clients=int(min_evaluate_clients), + min_evaluate_clients=len(val_clients) ) # 5. Start Simulation @@ -71,7 +58,11 @@ def main(cfg: DictConfig) -> None: client_fn=client_fn, num_clients=cfg.num_clients, config=fl.server.ServerConfig(num_rounds=cfg.num_rounds), - client_manager=Fedmeta_client_manager(), + client_resources={ + "num_cpus": cfg.client_resources.num_cpus, + "num_gpus": cfg.client_resources.num_gpus, + }, + client_manager=Fedmeta_client_manager(valid_client=len(val_clients)), strategy=strategy, ) diff --git a/baselines/FedMeta/FedMeta/models.py b/baselines/FedMeta/FedMeta/models.py index 9de15b834ec7..17d6a26b1292 100644 --- a/baselines/FedMeta/FedMeta/models.py +++ b/baselines/FedMeta/FedMeta/models.py @@ -154,5 +154,5 @@ def test( if len(testloader.dataset) == 0: raise ValueError("Testloader can't be 0, exiting...") loss /= len(testloader.dataset) - accuracy = correct / total + accuracy = correct return loss, accuracy diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index 97e142e847c5..37f73d42a5c6 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -40,11 +40,11 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: The weighted average metric. """ # Multiply accuracy of each client by number of examples used - accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics] + correct = [num_examples * m["correct"] for num_examples, m in metrics] examples = [num_examples for num_examples, _ in metrics] # Aggregate and return custom metric (weighted average) - return {"accuracy": int(sum(accuracies)) / int(sum(examples))} + return {"accuracy": sum(correct) / sum(examples)} class FedMeta(FedAvg): @@ -132,6 +132,8 @@ def aggregate_evaluate( if self.evaluate_metrics_aggregation_fn: eval_metrics = [(res.num_examples, res.metrics) for _, res in results] metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics) + log(WARNING, f"Test Accuracy : {metrics_aggregated['accuracy']}") + elif server_round == 1: # Only log this warning once log(WARNING, "No evaluate_metrics_aggregation_fn provided") From e461bc141865fac207e7a3531f03c653bd0194d5 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Mon, 4 Sep 2023 14:37:44 +0900 Subject: [PATCH 058/133] completed FedMeta(fedavg) --- .../FedMeta/FedMeta/Fedmeta_client_manager.py | 4 +- baselines/FedMeta/FedMeta/client.py | 54 +- baselines/FedMeta/FedMeta/dataset.py | 23 +- .../FedMeta/FedMeta/dataset_preparation.py | 12 +- baselines/FedMeta/FedMeta/main.py | 16 +- baselines/FedMeta/FedMeta/models.py | 4 +- baselines/FedMeta/FedMeta/server.py | 49 - baselines/FedMeta/FedMeta/strategy.py | 32 +- examples/flower-in-30-minutes/tutorial.ipynb | 2390 ++++++++--------- 9 files changed, 1277 insertions(+), 1307 deletions(-) diff --git a/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py index 4d04e4ca141a..e39b9a64a01a 100644 --- a/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py +++ b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py @@ -24,6 +24,7 @@ def __init__(self, valid_client, **kwargs): def sample( self, num_clients: int, + server_round: Optional[int] = None, min_num_clients: Optional[int] = None, criterion: Optional[Criterion] = None, ) -> List[ClientProxy]: @@ -46,6 +47,7 @@ def sample( num_clients, ) return [] - + if server_round is not None: + random.seed(server_round-1) sampled_cids = random.sample(available_cids, num_clients) return [self.clients[cid] for cid in sampled_cids] diff --git a/baselines/FedMeta/FedMeta/client.py b/baselines/FedMeta/FedMeta/client.py index 4bebfc50762d..30b9f6309046 100644 --- a/baselines/FedMeta/FedMeta/client.py +++ b/baselines/FedMeta/FedMeta/client.py @@ -15,38 +15,26 @@ from models import train, test -from dataset import FemnistDataset -import torchvision.transforms as transforms - - class FlowerClient( fl.client.NumPyClient ): def __init__( self, net: torch.nn.Module, - # trainloader: DataLoader, - # valloader: DataLoader, - cid: int, - dataset: Tuple[Dict], - client_list: List[str], + trainloaders: DataLoader, + valloaders: DataLoader, + cid: str, device: torch.device, num_epochs: int, learning_rate: float ) -> object: self.net = net - self.trainloader = None - self.valloader = None - self.cid = cid + self.trainloaders = trainloaders + self.valloaders = valloaders + self.cid = int(cid) self.device = device self.num_epochs = num_epochs self.learning_rate = learning_rate - self.train_set, self.valid_set = dataset - self.train_client, self.valid_client, _ = client_list - - - self.transform = transforms.Compose([transforms.ToTensor()]) - def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays: """Returns the parameters of the current net.""" @@ -63,33 +51,29 @@ def fit( ) -> Tuple[NDArrays, int, Dict]: """Implements distributed fit function for a given client.""" self.set_parameters(parameters) - train_set = self.train_set['user_data'][self.train_client[int(self.cid)]] - self.trainloader = DataLoader(FemnistDataset(train_set, self.transform), batch_size=10, shuffle=True) train( self.net, - self.trainloader, + self.trainloaders['train'][self.cid], self.device, epochs=self.num_epochs, learning_rate=self.learning_rate, ) - return self.get_parameters({}), len(self.trainloader), {} + return self.get_parameters({}), len(self.trainloaders['train'][self.cid]), {} def evaluate( self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[float, int, Dict]: """Implements distributed evaluation for a given client.""" self.set_parameters(parameters) - valid_set = self.valid_set['user_data'][self.valid_client[int(self.cid)]] - self.valloader = DataLoader(FemnistDataset(valid_set, self.transform)) - loss, accuracy = test(self.net, self.valloader, self.device) - return float(loss), len(self.valloader), {"correct": accuracy} + loss, accuracy = test(self.net, self.valloaders['test'][self.cid], self.device) + return float(loss), len(self.valloaders['test'][self.cid]), {"correct": accuracy} def gen_client_fn( num_epochs: int, - dataset: Tuple[Dict], - client_list: List[str], + trainloaders: List[DataLoader], + valloaders: List[DataLoader], learning_rate: float, model: DictConfig, ) -> Callable[[str], FlowerClient]: # pylint: disable=too-many-arguments @@ -123,26 +107,18 @@ def client_fn(cid: str) -> FlowerClient: print(f'cid : {cid}') # Load model + torch.manual_seed(123) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - # device = 'cpu' net = instantiate(model).to(device) - # transform = transforms.Compose([transforms.ToTensor()]) # Note: each client gets a different trainloader/valloader, so each client # will train and evaluate on their own unique data - # train_clients, val_client, _ = client_list - - # train_set = sup_set['user_data'][train_clients[int(cid)]] - # valid_set = sup_set['user_data'][val_client[int(cid)]] - - # trainloader = DataLoader(FemnistDataset(train_set, transform), batch_size=10, shuffle=True) - # valloader = DataLoader(FemnistDataset(valid_set, transform), batch_size=10) return FlowerClient( net, + trainloaders, + valloaders, cid, - dataset, - client_list, device, num_epochs, learning_rate, diff --git a/baselines/FedMeta/FedMeta/dataset.py b/baselines/FedMeta/FedMeta/dataset.py index 31d21b32661a..9e046a9b4d8b 100644 --- a/baselines/FedMeta/FedMeta/dataset.py +++ b/baselines/FedMeta/FedMeta/dataset.py @@ -14,6 +14,11 @@ from omegaconf import DictConfig from typing import Optional, Tuple from dataset_preparation import _partition_data, split_train_validation_test_clients +import numpy as np +from torch.utils.data import DataLoader +import torchvision.transforms as transforms + + class FemnistDataset(Dataset): def __init__(self, dataset, transform): @@ -34,7 +39,6 @@ def __len__(self): def load_datasets( # pylint: disable=too-many-arguments config: DictConfig, - seed: Optional[int] = 42, ) -> Tuple[DataLoader, DataLoader, DataLoader]: print(f"Dataset partitioning config: {config}") @@ -53,7 +57,22 @@ def load_datasets( # pylint: disable=too-many-arguments dataset[0]['users'] ) - return dataset, clients_list + trainloaders = {'train': [], 'test': []} + valloaders = {'train': [], 'test': []} + testloaders = {'train': [], 'test': []} + + transform = transforms.Compose([transforms.ToTensor()]) + for user in clients_list[0]: + trainloaders['train'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) + trainloaders['test'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform))) + for user in clients_list[2]: + valloaders['train'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) + valloaders['test'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform))) + for user in clients_list[1]: + testloaders['train'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) + testloaders['test'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform))) + + return trainloaders, valloaders, testloaders diff --git a/baselines/FedMeta/FedMeta/dataset_preparation.py b/baselines/FedMeta/FedMeta/dataset_preparation.py index 03b4e6234793..0234519281c1 100644 --- a/baselines/FedMeta/FedMeta/dataset_preparation.py +++ b/baselines/FedMeta/FedMeta/dataset_preparation.py @@ -11,6 +11,8 @@ from typing import List, Optional, Tuple, Dict, DefaultDict from collections import defaultdict import numpy as np +import torch +import random # def _read_dataset() -> Dict[List, Dict, List]: @@ -35,6 +37,7 @@ def _read_dataset( users.extend(dataset['users']) data.update(dataset['user_data']) + users = list(sorted(data.keys())) return users, data @@ -42,9 +45,8 @@ def support_query_split( data: DefaultDict, label: List, support_ratio: int, - seed: Optional[int] = 42, ): - np.random.seed(seed) + np.random.seed(42) random_index = np.random.permutation(len(label)) slice_index = int(len(label) * support_ratio) train_index = random_index[:slice_index] @@ -57,9 +59,8 @@ def split_train_validation_test_clients( clients: List, train_rate: Optional[float] = 0.8, val_rate: Optional[float] = 0.1, - seed: Optional[int] = 42 ) -> Tuple[List[str], List[str], List[str]]: - np.random.seed(seed) + np.random.seed(42) train_rate = int(train_rate * len(clients)) val_rate = int(val_rate * len(clients)) test_rate = len(clients) - train_rate - val_rate @@ -76,7 +77,6 @@ def split_train_validation_test_clients( def _partition_data( dir_path: str, support_ratio: Optional[float] = None, - seed: Optional[int] = 42, ) -> Tuple[Dict, Dict]: train_path = f'{dir_path}/train' @@ -108,7 +108,7 @@ def _partition_data( print(f'now preprocessing user : {user}') all_x = np.asarray(train_data[user]['x'] + test_data[user]['x']) all_y = np.asarray(train_data[user]['y'] + test_data[user]['y']) - sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio, seed) + sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio) support_dataset['users'].append(user) support_dataset['user_data'][user] = {'x': sup_x, 'y': sup_y} diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index ea8d5c6202a2..18703c5eab2a 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -30,27 +30,25 @@ def main(cfg: DictConfig) -> None: print(OmegaConf.to_yaml(cfg)) # partition dataset and get dataloaders - dataset, client_list = load_datasets(config=cfg.dataset) - train_clients, val_clients, test_clients = client_list + trainloaders, valloaders, testloaders= load_datasets(config=cfg.dataset) # Check config Clients value - if cfg.num_clients > len(train_clients): - raise ImportError(f"Total Clients num is {len(train_clients)}") + if cfg.num_clients > len(trainloaders['train']): + raise ImportError(f"Total Clients num is {len(trainloaders['train'])}") # prepare function that will be used to spawn each client client_fn = client.gen_client_fn( num_epochs=cfg.num_epochs, - dataset=dataset, - client_list=client_list, + trainloaders=trainloaders, + valloaders=valloaders, learning_rate=cfg.learning_rate, model=cfg.model, - ) strategy = instantiate( cfg.strategy, # evaluate_fn=evaluate_fn, evaluate_metrics_aggregation_fn=weighted_average, - min_evaluate_clients=len(val_clients) + min_evaluate_clients=len(valloaders['train']) ) # 5. Start Simulation @@ -62,7 +60,7 @@ def main(cfg: DictConfig) -> None: "num_cpus": cfg.client_resources.num_cpus, "num_gpus": cfg.client_resources.num_gpus, }, - client_manager=Fedmeta_client_manager(valid_client=len(val_clients)), + client_manager=Fedmeta_client_manager(valid_client=len(valloaders['train'])), strategy=strategy, ) diff --git a/baselines/FedMeta/FedMeta/models.py b/baselines/FedMeta/FedMeta/models.py index 17d6a26b1292..5ae02b9a005a 100644 --- a/baselines/FedMeta/FedMeta/models.py +++ b/baselines/FedMeta/FedMeta/models.py @@ -28,8 +28,8 @@ def __init__(self) -> None: self.maxpool1 = nn.MaxPool2d(kernel_size=(2, 2)) self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2) self.maxpool2 = nn.MaxPool2d(kernel_size=(2, 2)) - self.linear1 = nn.Linear(7 * 7 * 64, 2048) - self.linear2 = nn.Linear(2048, 62) + self.linear1 = nn.Linear(7 * 7 * 64, 1024) + self.linear2 = nn.Linear(1024, 62) def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: """Forward pass of the CNN. diff --git a/baselines/FedMeta/FedMeta/server.py b/baselines/FedMeta/FedMeta/server.py index 915e9944cffc..e69de29bb2d1 100644 --- a/baselines/FedMeta/FedMeta/server.py +++ b/baselines/FedMeta/FedMeta/server.py @@ -1,49 +0,0 @@ -from collections import OrderedDict -from typing import Callable, Dict, Optional, Tuple - -import torch -from flwr.common.typing import NDArrays, Scalar -from hydra.utils import instantiate -from omegaconf import DictConfig -from torch.utils.data import DataLoader - - -def gen_evaluate_fn( - testloader: DataLoader, - device: torch.device, - model: DictConfig, -) -> Callable[ - [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]] -]: - """Generates the function for centralized evaluation. - - Parameters - ---------- - testloader : DataLoader - The dataloader to test the model with. - device : torch.device - The device to test the model on. - - Returns - ------- - Callable[ [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]] ] - The centralized evaluation function. - """ - - def evaluate( - server_round: int, parameters_ndarrays: NDArrays, config: Dict[str, Scalar] - ) -> Optional[Tuple[float, Dict[str, Scalar]]]: - # pylint: disable=unused-argument - """Use the entire CIFAR-10 test set for evaluation.""" - - net = instantiate(model) - params_dict = zip(net.state_dict().keys(), parameters_ndarrays) - state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict}) - net.load_state_dict(state_dict, strict=True) - net.to(device) - - loss, accuracy = test(net, testloader, device=device) - # return statistics - return loss, {"accuracy": accuracy} - - return evaluate diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index 37f73d42a5c6..bf5883231945 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -4,7 +4,7 @@ extend or modify the functionality of an existing strategy. """ from typing import Dict, List, Optional, Tuple, Union -from logging import WARNING +from logging import WARNING, INFO from flwr.server.client_proxy import ClientProxy from flwr.server.strategy import FedAvg @@ -40,7 +40,8 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: The weighted average metric. """ # Multiply accuracy of each client by number of examples used - correct = [num_examples * m["correct"] for num_examples, m in metrics] + # correct = [num_examples * m["correct"] for num_examples, m in metrics] + correct = [ m["correct"] for _, m in metrics] examples = [num_examples for num_examples, _ in metrics] # Aggregate and return custom metric (weighted average) @@ -48,6 +49,29 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: class FedMeta(FedAvg): + def configure_fit( + self, server_round: int, parameters: Parameters, client_manager: ClientManager + ) -> List[Tuple[ClientProxy, FitIns]]: + """Configure the next round of training.""" + config = {} + if self.on_fit_config_fn is not None: + # Custom fit config function provided + config = self.on_fit_config_fn(server_round) + fit_ins = FitIns(parameters, config) + + # Sample clients + sample_size, min_num_clients = self.num_fit_clients( + client_manager.num_available() + ) + clients = client_manager.sample( + num_clients=sample_size, + min_num_clients=min_num_clients, + server_round=server_round + ) + + # Return client/config pairs + return [(client, fit_ins) for client in clients] + def configure_evaluate( self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, EvaluateIns]]: @@ -70,7 +94,7 @@ def configure_evaluate( clients = client_manager.sample( num_clients=sample_size, min_num_clients=min_num_clients, - criterion=evaluate_client_Criterion(self.min_evaluate_clients) + criterion=evaluate_client_Criterion(self.min_evaluate_clients), ) # Return client/config pairs @@ -132,7 +156,7 @@ def aggregate_evaluate( if self.evaluate_metrics_aggregation_fn: eval_metrics = [(res.num_examples, res.metrics) for _, res in results] metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics) - log(WARNING, f"Test Accuracy : {metrics_aggregated['accuracy']}") + log(INFO, f'Test Accuracy : {metrics_aggregated["accuracy"]:.3%}') elif server_round == 1: # Only log this warning once log(WARNING, "No evaluate_metrics_aggregation_fn provided") diff --git a/examples/flower-in-30-minutes/tutorial.ipynb b/examples/flower-in-30-minutes/tutorial.ipynb index b6864dcc696c..dd370833ff45 100644 --- a/examples/flower-in-30-minutes/tutorial.ipynb +++ b/examples/flower-in-30-minutes/tutorial.ipynb @@ -1,1240 +1,1240 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "P-iD0bgbXDdC" - }, - "source": [ - "Welcome to the 30 minutes Flower federated learning tutorial!\n", - "\n", - "In this tutorial you will implement your first Federated Learning project using [Flower](https://flower.dev/).\n", - "\n", - "🧑‍🏫 This tutorial starts at zero and expects no familiarity with federated learning. Only a basic understanding of data science and Python programming is assumed. A minimal understanding of ML is not required but if you already know about it, nothing is stopping your from modifying this code as you see fit!\n", - "\n", - "> Star Flower on [GitHub ⭐️](https://github.com/adap/flower) and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack 🌼](https://flower.dev/join-slack/). We'd love to hear from you in the #introductions channel! And if anything is unclear, head over to the #questions channel.\n", - "\n", - "Let's get stated!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Complementary Content\n", - "\n", - "But before do so, let me point you to a few video tutorials in the [Flower Youtube channel](https://www.youtube.com/@flowerlabs) that you might want to check out after this tutorial. We post new videos fairly regularly with new content:\n", - "* **[VIDEO]** quickstart-tensorflow: [15-min video on how to start with Flower + Tensorflow/Keras](https://www.youtube.com/watch?v=jOmmuzMIQ4c)\n", - "* **[VIDEO]** quickstart-pytorch: [20-min video on how to start with Flower + PyTorch](https://www.youtube.com/watch?v=jOmmuzMIQ4c)\n", - "* **[VIDEO]** Flower simulation mini-series: [9 line-by-line video tutorials](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "jfy1EuX7Xzfg" - }, - "source": [ - "# Environment Setup\n", - "\n", - "Now let's really begin with this tutorial!\n", - "\n", - "To start working with Flower, very little is required once you have activated your Python environment (e.g. via `conda`, `virtualenv`, `pyenv`, etc). If you are running this code on Colab, there is really nothing to do except to install Flower and other dependencies. The steps below have been verified to run in Colab. Let's first, install Flower, then the ML framework of your choice and extra dependencies you might want to use.\n", - "\n", - "## Installing Flower\n", - "\n", - "You can install flower very conveniently from `pip`:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "Gc_GOyNXXB35" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/adap/flower.git@main\n", - " Cloning https://github.com/adap/flower.git (to revision main) to /private/var/folders/yr/0jrp7k711jzcxcdxgh6y1jh80000gn/T/pip-req-build-9uwmcdwe\n", - " Running command git clone --filter=blob:none --quiet https://github.com/adap/flower.git /private/var/folders/yr/0jrp7k711jzcxcdxgh6y1jh80000gn/T/pip-req-build-9uwmcdwe\n", - " Resolved https://github.com/adap/flower.git to commit 351dae247c2d69680acdca6d449a4d62dfb5baf8\n", - " Installing build dependencies ... \u001b[?25ldone\n", - "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", - "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: cryptography<42.0.0,>=41.0.2 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (41.0.3)\n", - "Requirement already satisfied: grpcio!=1.52.0,<2.0.0,>=1.48.2 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (1.57.0)\n", - "Requirement already satisfied: iterators<0.0.3,>=0.0.2 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (0.0.2)\n", - "Requirement already satisfied: numpy<2.0.0,>=1.21.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (1.24.3)\n", - "Requirement already satisfied: protobuf<4.0.0,>=3.19.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (3.20.3)\n", - "Requirement already satisfied: pycryptodome<4.0.0,>=3.18.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (3.18.0)\n", - "Requirement already satisfied: cffi>=1.12 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from cryptography<42.0.0,>=41.0.2->flwr==1.5.0) (1.15.1)\n", - "Requirement already satisfied: pycparser in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from cffi>=1.12->cryptography<42.0.0,>=41.0.2->flwr==1.5.0) (2.21)\n" - ] - } - ], - "source": [ - "# depending on your shell, you might need to add `\\` before `[` and `]`.\n", - "!pip install -q flwr[simulation]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "y58HdudsYWQP" - }, - "source": [ - "We will be using the _simulation_ model in Flower, which allows you to run a large number of clients without the overheads of manually managing devices. This is achieved via the `Virtual Client Engine`, the core component that runs [FL Simulations](https://flower.dev/docs/framework/how-to-run-simulations.html) with Flower. With simulation, you can dynamically scale your experiments whether you run the code on your laptop, a machine with a single GPU, a server with multiple GPUs os even on a cluster with multiple servers. The `Virtual Client Engine` handles everything transparently and it allows you to specify how many resources (e.g. CPU cores, GPU VRAM) should be assigned to each virtual client." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "2rkzo1M9a0io" - }, - "source": [ - "## Install your ML framework\n", - "\n", - "Flower is agnostic to your choice of ML Framework. Flower works with `PyTorch`, `Tensorflow`, `NumPy`, `🤗 Transformers`, `MXNet`, `JAX`, `scikit-learn`, `fastai`, `Pandas`. Flower also supports all major platforms: `iOS`, `Android` and plain `C++`. You can find a _quickstart- example for each of the above in the [Flower Repository](https://github.com/adap/flower/tree/main/examples) inside the `examples/` directory. And check the [Flower Documentation](https://flower.dev/docs/) for even more learning materials.\n", - "\n", - "In this tutorial we are going to use PyTorch, so let's install a recent version. In this tutorial we'll use a small model so using CPU only training will suffice (this will also prevent Colab from abruptly terminating your experiment if resource limits are exceeded)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lqrJYPbZZ8aM", - "outputId": "7192138a-8c87-4d9a-f726-af1038ad264c" - }, - "outputs": [], - "source": [ - "# you might see a warning after running the command below, this can be ignored\n", - "# if you are running this outside Colab, you probably need to adjust the command below\n", - "!pip install torch==1.13.1+cpu torchvision==0.14.1+cpu torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cpu" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "4UTuRurVeLDF" - }, - "source": [ - "We are going to install some other dependencies you are likely familiar with. We'll use these to make plots." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ybSlTUVXeT3u", - "outputId": "58b7af77-609f-4118-bd5b-5629a4b5a296" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting matplotlib\n", - " Obtaining dependency information for matplotlib from https://files.pythonhosted.org/packages/8d/22/719f4fff33b13b0708711fb52ca3fc44617a26728e0e023358288d5197ae/matplotlib-3.7.2-cp310-cp310-macosx_11_0_arm64.whl.metadata\n", - " Downloading matplotlib-3.7.2-cp310-cp310-macosx_11_0_arm64.whl.metadata (5.6 kB)\n", - "Collecting contourpy>=1.0.1 (from matplotlib)\n", - " Obtaining dependency information for contourpy>=1.0.1 from https://files.pythonhosted.org/packages/15/c4/aae3954fce0e22362cc55430d1a395bf0be5a22b40fce63edda9eb6ea339/contourpy-1.1.0-cp310-cp310-macosx_11_0_arm64.whl.metadata\n", - " Downloading contourpy-1.1.0-cp310-cp310-macosx_11_0_arm64.whl.metadata (5.7 kB)\n", - "Collecting cycler>=0.10 (from matplotlib)\n", - " Using cached cycler-0.11.0-py3-none-any.whl (6.4 kB)\n", - "Collecting fonttools>=4.22.0 (from matplotlib)\n", - " Obtaining dependency information for fonttools>=4.22.0 from https://files.pythonhosted.org/packages/21/66/bddd878452ae1e2d5f5891daa6bcce594d6b19396d33b8798e722837b222/fonttools-4.42.1-cp310-cp310-macosx_10_9_universal2.whl.metadata\n", - " Downloading fonttools-4.42.1-cp310-cp310-macosx_10_9_universal2.whl.metadata (150 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m151.0/151.0 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting kiwisolver>=1.0.1 (from matplotlib)\n", - " Obtaining dependency information for kiwisolver>=1.0.1 from https://files.pythonhosted.org/packages/23/11/6fb190bae4b279d712a834e7b1da89f6dcff6791132f7399aa28a57c3565/kiwisolver-1.4.5-cp310-cp310-macosx_11_0_arm64.whl.metadata\n", - " Downloading kiwisolver-1.4.5-cp310-cp310-macosx_11_0_arm64.whl.metadata (6.4 kB)\n", - "Requirement already satisfied: numpy>=1.20 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from matplotlib) (1.24.3)\n", - "Requirement already satisfied: packaging>=20.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from matplotlib) (23.1)\n", - "Requirement already satisfied: pillow>=6.2.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from matplotlib) (10.0.0)\n", - "Collecting pyparsing<3.1,>=2.3.1 (from matplotlib)\n", - " Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n", - "Requirement already satisfied: six>=1.5 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", - "Downloading matplotlib-3.7.2-cp310-cp310-macosx_11_0_arm64.whl (7.3 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.3/7.3 MB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", - "\u001b[?25hDownloading contourpy-1.1.0-cp310-cp310-macosx_11_0_arm64.whl (229 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m229.4/229.4 kB\u001b[0m \u001b[31m30.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading fonttools-4.42.1-cp310-cp310-macosx_10_9_universal2.whl (2.7 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7 MB\u001b[0m \u001b[31m47.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", - "\u001b[?25hDownloading kiwisolver-1.4.5-cp310-cp310-macosx_11_0_arm64.whl (66 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.2/66.2 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: pyparsing, kiwisolver, fonttools, cycler, contourpy, matplotlib\n", - "Successfully installed contourpy-1.1.0 cycler-0.11.0 fonttools-4.42.1 kiwisolver-1.4.5 matplotlib-3.7.2 pyparsing-3.0.9\n" - ] - } - ], - "source": [ - "!pip install matplotlib" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "mpmcL_STdjIo" - }, - "source": [ - "# Centralised training: the old way of doing ML" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "gvw2TZjSec6C" - }, - "source": [ - "Let's begin by creating a simple (but complete) training loop as it is commonly done in centralised setups. Starting our tutorial in this way will allow us to very clearly identify which parts of a typical ML pipeline are common to both centralised and federated training and which ones are poles a part.\n", - "\n", - "For this tutorial we'll design a image classification pipeline for [MNIST digits](https://en.wikipedia.org/wiki/MNIST_database) and using a simple CNN model as the network to train. The MNIST dataset is comprised of `28x28` greyscale images with digits from 0 to 9 (i.e. 10 classes in total)\n", - "\n", - "\n", - "## A dataset\n", - "\n", - "Let's begin by constructing the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "p9aFBjd1cpHs" - }, - "outputs": [], - "source": [ - "# we naturally first need to import torch and torchvision\n", - "import torch\n", - "from torch.utils.data import DataLoader\n", - "from torchvision.transforms import ToTensor, Normalize, Compose\n", - "from torchvision.datasets import MNIST\n", - "\n", - "\n", - "def get_mnist(data_path: str = './data'):\n", - " '''This function downloads the MNIST dataset into the `data_path`\n", - " directory if it is not there already. WE construct the train/test\n", - " split by converting the images into tensors and normalising them'''\n", - "\n", - " # transformation to convert images to tensors and apply normalisation\n", - " tr = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])\n", - "\n", - " # prepare train and test set\n", - " trainset = MNIST(data_path, train=True, download=True, transform=tr)\n", - " testset = MNIST(data_path, train=False, download=True, transform=tr)\n", - "\n", - " return trainset, testset" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "vKoQfqgYgwg0" - }, - "source": [ - "Let's run the code above and do some visualisations to understand better the data we are working with !" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "pS8sL2hDgvZN" - }, - "outputs": [], - "source": [ - "trainset, testset = get_mnist()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "iNA-6AcYhYVM" - }, - "source": [ - "We can have a quick overview of our datasets by just typing the object on the command line. For instance, below you can see that the `trainset` has 60k training examples and will use the transformation rule we defined above in `get_mnist()`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pWsIHsq-g4nX", - "outputId": "f10b649f-3cee-4e86-c7ff-94bd1fd3e082" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Dataset MNIST\n", - " Number of datapoints: 60000\n", - " Root location: ./data\n", - " Split: Train\n", - " StandardTransform\n", - "Transform: Compose(\n", - " ToTensor()\n", - " Normalize(mean=(0.1307,), std=(0.3081,))\n", - " )" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "trainset" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "NY9bWlaMhweq" - }, - "source": [ - "Let's create a more insightful visualisation. First let's see the distribution over the labels by constructing a histogram. Then, let's visualise some training examples !" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 490 - }, - "id": "DCTjwpizikwy", - "outputId": "c8d0f4c0-60cd-4c58-bc91-3b061dae8046" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Class labels distribution for MNIST')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "# construct histogram\n", - "all_labels = trainset.targets\n", - "num_possible_labels = len(set(all_labels.numpy().tolist())) # this counts unique labels (so it should be = 10)\n", - "plt.hist(all_labels, bins=num_possible_labels)\n", - "\n", - "# plot formatting\n", - "plt.xticks(range(num_possible_labels))\n", - "plt.grid()\n", - "plt.xlabel('Label')\n", - "plt.ylabel('Number of images')\n", - "plt.title('Class labels distribution for MNIST')" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "K7-K0bKamho7" - }, - "source": [ - "Let's visualise 32 images from the dataset\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "ExGypiVsjMUv" - }, - "outputs": [], - "source": [ - "import random\n", - "import numpy as np\n", - "\n", - "def visualise_n_random_examples(trainset_, n: int, verbose: bool = True):\n", - " # take n examples at random\n", - " idx =list(range(len(trainset_.data)))\n", - " random.shuffle(idx)\n", - " idx = idx[:n]\n", - " if verbose:\n", - " print(f\"will display images with idx: {idx}\")\n", - "\n", - "\n", - " # construct canvas\n", - " num_cols = 8\n", - " num_rows = int(np.ceil(len(idx)/num_cols))\n", - " fig, axs = plt.subplots(figsize=(16, num_rows*2), nrows=num_rows, ncols=num_cols)\n", - "\n", - " # display images on canvas\n", - " for c_i, i in enumerate(idx):\n", - " axs.flat[c_i].imshow(trainset_.data[i], cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 715 - }, - "id": "xA2s8vqkmkga", - "outputId": "4e0988a8-388d-4acf-882b-089e4ea887bf" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "will display images with idx: [59717, 34422, 26054, 1199, 3182, 18665, 27924, 45921, 19494, 40038, 31891, 22197, 14705, 4590, 46747, 15779, 2575, 32582, 47065, 19149, 41838, 24098, 28738, 39203, 35935, 55347, 16343, 40626, 31743, 34183, 18890, 47438]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# it is likely that the plot this function will generate looks familiar to other plots you might have generated before\n", - "# or you might have encountered in other tutorials. So far, we aren't doing anything new, Federated Learning will start soon!\n", - "visualise_n_random_examples(trainset, n=32)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "PmGyjFEFhVwd" - }, - "source": [ - "# A CNN architecture\n", - "\n", - "This tutorial is not so much about novel architectural designs so we keep things simple and make use of a typical CNN that is adequate for the MNIST image classification task.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "Nr4jR6tspOh4" - }, - "outputs": [], - "source": [ - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "\n", - "class Net(nn.Module):\n", - " def __init__(self, num_classes: int) -> None:\n", - " super(Net, self).__init__()\n", - " self.conv1 = nn.Conv2d(1, 6, 5)\n", - " self.pool = nn.MaxPool2d(2, 2)\n", - " self.conv2 = nn.Conv2d(6, 16, 5)\n", - " self.fc1 = nn.Linear(16 * 4 * 4, 120)\n", - " self.fc2 = nn.Linear(120, 84)\n", - " self.fc3 = nn.Linear(84, num_classes)\n", - "\n", - " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", - " x = self.pool(F.relu(self.conv1(x)))\n", - " x = self.pool(F.relu(self.conv2(x)))\n", - " x = x.view(-1, 16 * 4 * 4)\n", - " x = F.relu(self.fc1(x))\n", - " x = F.relu(self.fc2(x))\n", - " x = self.fc3(x)\n", - " return x" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "5TJrrCBlpZOp" - }, - "source": [ - "Similarly to what we did with the dataset you could inspect the model in various ways. We can, for instance, count the number of model parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "zdVK9c4hpYaC", - "outputId": "67d01ab4-cdd9-4661-8f01-eaa9aabf786d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num_parameters = 44426\n" - ] - } - ], - "source": [ - "model = Net(num_classes=10)\n", - "num_parameters = sum(value.numel() for value in model.state_dict().values())\n", - "print(f\"{num_parameters = }\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "XAXzw1dlp_oO" - }, - "source": [ - "## The Training Loop\n", - "\n", - "A minimal training loop in PyTorch can be constructed with three functions:\n", - "* `train()` that will train the model given a dataloader.\n", - "* `test()` that will be used to evaluate the performance of the model on held-out data, e.g., a training set.\n", - "* `run_centralised()` which will define additional elements (e.g. the optimiser) and run the training loop over N epochs.\n", - "\n", - "Let's construct these functions!\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "DRhz5bcfpw06" - }, - "outputs": [], - "source": [ - "def train(net, trainloader, optimizer, epochs):\n", - " \"\"\"Train the network on the training set.\"\"\"\n", - " criterion = torch.nn.CrossEntropyLoss()\n", - " net.train()\n", - " for _ in range(epochs):\n", - " for images, labels in trainloader:\n", - " optimizer.zero_grad()\n", - " loss = criterion(net(images), labels)\n", - " loss.backward()\n", - " optimizer.step()\n", - " return net\n", - "\n", - "def test(net, testloader):\n", - " \"\"\"Validate the network on the entire test set.\"\"\"\n", - " criterion = torch.nn.CrossEntropyLoss()\n", - " correct, loss = 0, 0.0\n", - " net.eval()\n", - " with torch.no_grad():\n", - " for images, labels in testloader:\n", - " outputs = net(images)\n", - " loss += criterion(outputs, labels).item()\n", - " _, predicted = torch.max(outputs.data, 1)\n", - " correct += (predicted == labels).sum().item()\n", - " accuracy = correct / len(testloader.dataset)\n", - " return loss, accuracy\n", - "\n", - "\n", - "def run_centralised(epochs: int, lr: float, momentum: float=0.9):\n", - " \"\"\"A minimal (but complete) training loop\"\"\"\n", - "\n", - " # instantiate the model\n", - " model = Net(num_classes=10)\n", - "\n", - " # define optimiser with hyperparameters supplied\n", - " optim = torch.optim.SGD(model.parameters(), lr=lr, momentum=momentum)\n", - "\n", - " # get dataset and construct a dataloaders\n", - " trainset, testset = get_mnist()\n", - " trainloader = DataLoader(trainset, batch_size=64, shuffle=True, num_workers=2)\n", - " testloader = DataLoader(testset, batch_size=128)\n", - "\n", - " # train for the specified number of epochs\n", - " trained_model = train(model, trainloader, optim, epochs)\n", - "\n", - " # training is completed, then evaluate model on the test set\n", - " loss, accuracy = test(trained_model, testloader)\n", - " print(f\"{loss = }\")\n", - " print(f\"{accuracy = }\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "76Q0UnqiukYT" - }, - "source": [ - "Let's run this for 5 epochs (you'll see it reaching close to 99% accuracy -- as expected from a centralised setup with the MNIST dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xgJ6mdNSqzpI", - "outputId": "e8d9b429-178d-4924-e82f-4d4e52863788" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss = 2.4290689574118005\n", - "accuracy = 0.9894\n" - ] - } - ], - "source": [ - "run_centralised(epochs=5, lr=0.01)" - ] - }, + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "P-iD0bgbXDdC" + }, + "source": [ + "Welcome to the 30 minutes Flower federated learning tutorial!\n", + "\n", + "In this tutorial you will implement your first Federated Learning project using [Flower](https://flower.dev/).\n", + "\n", + "🧑‍🏫 This tutorial starts at zero and expects no familiarity with federated learning. Only a basic understanding of data science and Python programming is assumed. A minimal understanding of ML is not required but if you already know about it, nothing is stopping your from modifying this code as you see fit!\n", + "\n", + "> Star Flower on [GitHub ⭐️](https://github.com/adap/flower) and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack 🌼](https://flower.dev/join-slack/). We'd love to hear from you in the #introductions channel! And if anything is unclear, head over to the #questions channel.\n", + "\n", + "Let's get stated!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Complementary Content\n", + "\n", + "But before do so, let me point you to a few video tutorials in the [Flower Youtube channel](https://www.youtube.com/@flowerlabs) that you might want to check out after this tutorial. We post new videos fairly regularly with new content:\n", + "* **[VIDEO]** quickstart-tensorflow: [15-min video on how to start with Flower + Tensorflow/Keras](https://www.youtube.com/watch?v=jOmmuzMIQ4c)\n", + "* **[VIDEO]** quickstart-pytorch: [20-min video on how to start with Flower + PyTorch](https://www.youtube.com/watch?v=jOmmuzMIQ4c)\n", + "* **[VIDEO]** Flower simulation mini-series: [9 line-by-line video tutorials](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "jfy1EuX7Xzfg" + }, + "source": [ + "# Environment Setup\n", + "\n", + "Now let's really begin with this tutorial!\n", + "\n", + "To start working with Flower, very little is required once you have activated your Python environment (e.g. via `conda`, `virtualenv`, `pyenv`, etc). If you are running this code on Colab, there is really nothing to do except to install Flower and other dependencies. The steps below have been verified to run in Colab. Let's first, install Flower, then the ML framework of your choice and extra dependencies you might want to use.\n", + "\n", + "## Installing Flower\n", + "\n", + "You can install flower very conveniently from `pip`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "Gc_GOyNXXB35" + }, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "pyz2gQaluw-5" - }, - "source": [ - "The above centralised formulation has worked just fine for some applications and to showcase the potential of AI in a variety of scenarios. However, as was discussed earlier in the session, centralised training is unsuitable for a larger range of settings were information cannot be collected in order to build a unified (centralised) dataset.\n", - "\n", - "# The Future of AI is Federated\n", - "\n", - "What are the key differences between Federated Learning and Centralised Training? I you could only pick you, probably you'd say:\n", - "* Federated Learning is distributed -- the model is trained on-device by the participating clients.\n", - "* Data remains private and is owned by a specific _client_ -- the data is never sent to the central server.\n", - "\n", - "The are several more differences. But the above two are the main ones to always consider and that are common to all flavours of Federated Learning (e.g. _cross-device_ or _cross-silo_). The remaining of this tutorial is going to focus in transforming the code we have written so far for the centralised setting and construct a Federated Learning pipeline using Flower and PyTorch.\n", - "\n", - "Let's begin! 🚀" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting git+https://github.com/adap/flower.git@main\n", + " Cloning https://github.com/adap/flower.git (to revision main) to /private/var/folders/yr/0jrp7k711jzcxcdxgh6y1jh80000gn/T/pip-req-build-9uwmcdwe\n", + " Running command git clone --filter=blob:none --quiet https://github.com/adap/flower.git /private/var/folders/yr/0jrp7k711jzcxcdxgh6y1jh80000gn/T/pip-req-build-9uwmcdwe\n", + " Resolved https://github.com/adap/flower.git to commit 351dae247c2d69680acdca6d449a4d62dfb5baf8\n", + " Installing build dependencies ... \u001B[?25ldone\n", + "\u001B[?25h Getting requirements to build wheel ... \u001B[?25ldone\n", + "\u001B[?25h Preparing metadata (pyproject.toml) ... \u001B[?25ldone\n", + "\u001B[?25hRequirement already satisfied: cryptography<42.0.0,>=41.0.2 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (41.0.3)\n", + "Requirement already satisfied: grpcio!=1.52.0,<2.0.0,>=1.48.2 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (1.57.0)\n", + "Requirement already satisfied: iterators<0.0.3,>=0.0.2 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (0.0.2)\n", + "Requirement already satisfied: numpy<2.0.0,>=1.21.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (1.24.3)\n", + "Requirement already satisfied: protobuf<4.0.0,>=3.19.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (3.20.3)\n", + "Requirement already satisfied: pycryptodome<4.0.0,>=3.18.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (3.18.0)\n", + "Requirement already satisfied: cffi>=1.12 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from cryptography<42.0.0,>=41.0.2->flwr==1.5.0) (1.15.1)\n", + "Requirement already satisfied: pycparser in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from cffi>=1.12->cryptography<42.0.0,>=41.0.2->flwr==1.5.0) (2.21)\n" + ] + } + ], + "source": [ + "# depending on your shell, you might need to add `\\` before `[` and `]`.\n", + "!pip install -q flwr[simulation]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "y58HdudsYWQP" + }, + "source": [ + "We will be using the _simulation_ model in Flower, which allows you to run a large number of clients without the overheads of manually managing devices. This is achieved via the `Virtual Client Engine`, the core component that runs [FL Simulations](https://flower.dev/docs/framework/how-to-run-simulations.html) with Flower. With simulation, you can dynamically scale your experiments whether you run the code on your laptop, a machine with a single GPU, a server with multiple GPUs os even on a cluster with multiple servers. The `Virtual Client Engine` handles everything transparently and it allows you to specify how many resources (e.g. CPU cores, GPU VRAM) should be assigned to each virtual client." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "2rkzo1M9a0io" + }, + "source": [ + "## Install your ML framework\n", + "\n", + "Flower is agnostic to your choice of ML Framework. Flower works with `PyTorch`, `Tensorflow`, `NumPy`, `🤗 Transformers`, `MXNet`, `JAX`, `scikit-learn`, `fastai`, `Pandas`. Flower also supports all major platforms: `iOS`, `Android` and plain `C++`. You can find a _quickstart- example for each of the above in the [Flower Repository](https://github.com/adap/flower/tree/main/examples) inside the `examples/` directory. And check the [Flower Documentation](https://flower.dev/docs/) for even more learning materials.\n", + "\n", + "In this tutorial we are going to use PyTorch, so let's install a recent version. In this tutorial we'll use a small model so using CPU only training will suffice (this will also prevent Colab from abruptly terminating your experiment if resource limits are exceeded)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "-Jv-H2HBzbPA" - }, - "source": [ - "## One Client, One Data Partition\n", - "\n", - "To start designing a Federated Learning pipeline we need to meet one of the key properties in FL: each client has its own data partition. To accomplish this with the MNIST dataset, we are going to generate N random partitions, where N is the total number of clients in our FL system." - ] + "id": "lqrJYPbZZ8aM", + "outputId": "7192138a-8c87-4d9a-f726-af1038ad264c" + }, + "outputs": [], + "source": [ + "# you might see a warning after running the command below, this can be ignored\n", + "# if you are running this outside Colab, you probably need to adjust the command below\n", + "!pip install torch==1.13.1+cpu torchvision==0.14.1+cpu torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cpu" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "4UTuRurVeLDF" + }, + "source": [ + "We are going to install some other dependencies you are likely familiar with. We'll use these to make plots." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ybSlTUVXeT3u", + "outputId": "58b7af77-609f-4118-bd5b-5629a4b5a296" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "Lgc5C6yltJCv" - }, - "outputs": [], - "source": [ - "from torch.utils.data import random_split\n", - "\n", - "def prepare_dataset(num_partitions: int,\n", - " batch_size: int,\n", - " val_ratio: float = 0.1):\n", - "\n", - " \"\"\"This function partitions the training set into N disjoint\n", - " subsets, each will become the local dataset of a client. This\n", - " function also subsequently partitions each traininset partition\n", - " into train and validation. The test set is left intact and will\n", - " be used by the central server to asses the performance of the\n", - " global model. \"\"\"\n", - "\n", - " # get the MNIST dataset\n", - " trainset, testset = get_mnist()\n", - "\n", - " # split trainset into `num_partitions` trainsets\n", - " num_images = len(trainset) // num_partitions\n", - "\n", - " partition_len = [num_images] * num_partitions\n", - "\n", - " trainsets = random_split(trainset, partition_len, torch.Generator().manual_seed(2023))\n", - "\n", - " # create dataloaders with train+val support\n", - " trainloaders = []\n", - " valloaders = []\n", - " for trainset_ in trainsets:\n", - " num_total = len(trainset_)\n", - " num_val = int(val_ratio * num_total)\n", - " num_train = num_total - num_val\n", - "\n", - " for_train, for_val = random_split(trainset_, [num_train, num_val], torch.Generator().manual_seed(2023))\n", - "\n", - " trainloaders.append(DataLoader(for_train, batch_size=batch_size, shuffle=True, num_workers=2))\n", - " valloaders.append(DataLoader(for_val, batch_size=batch_size, shuffle=False, num_workers=2))\n", - "\n", - " # create dataloader for the test set\n", - " testloader = DataLoader(testset, batch_size=128)\n", - "\n", - " return trainloaders, valloaders, testloader" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting matplotlib\n", + " Obtaining dependency information for matplotlib from https://files.pythonhosted.org/packages/8d/22/719f4fff33b13b0708711fb52ca3fc44617a26728e0e023358288d5197ae/matplotlib-3.7.2-cp310-cp310-macosx_11_0_arm64.whl.metadata\n", + " Downloading matplotlib-3.7.2-cp310-cp310-macosx_11_0_arm64.whl.metadata (5.6 kB)\n", + "Collecting contourpy>=1.0.1 (from matplotlib)\n", + " Obtaining dependency information for contourpy>=1.0.1 from https://files.pythonhosted.org/packages/15/c4/aae3954fce0e22362cc55430d1a395bf0be5a22b40fce63edda9eb6ea339/contourpy-1.1.0-cp310-cp310-macosx_11_0_arm64.whl.metadata\n", + " Downloading contourpy-1.1.0-cp310-cp310-macosx_11_0_arm64.whl.metadata (5.7 kB)\n", + "Collecting cycler>=0.10 (from matplotlib)\n", + " Using cached cycler-0.11.0-py3-none-any.whl (6.4 kB)\n", + "Collecting fonttools>=4.22.0 (from matplotlib)\n", + " Obtaining dependency information for fonttools>=4.22.0 from https://files.pythonhosted.org/packages/21/66/bddd878452ae1e2d5f5891daa6bcce594d6b19396d33b8798e722837b222/fonttools-4.42.1-cp310-cp310-macosx_10_9_universal2.whl.metadata\n", + " Downloading fonttools-4.42.1-cp310-cp310-macosx_10_9_universal2.whl.metadata (150 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m151.0/151.0 kB\u001B[0m \u001B[31m6.2 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hCollecting kiwisolver>=1.0.1 (from matplotlib)\n", + " Obtaining dependency information for kiwisolver>=1.0.1 from https://files.pythonhosted.org/packages/23/11/6fb190bae4b279d712a834e7b1da89f6dcff6791132f7399aa28a57c3565/kiwisolver-1.4.5-cp310-cp310-macosx_11_0_arm64.whl.metadata\n", + " Downloading kiwisolver-1.4.5-cp310-cp310-macosx_11_0_arm64.whl.metadata (6.4 kB)\n", + "Requirement already satisfied: numpy>=1.20 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from matplotlib) (1.24.3)\n", + "Requirement already satisfied: packaging>=20.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from matplotlib) (10.0.0)\n", + "Collecting pyparsing<3.1,>=2.3.1 (from matplotlib)\n", + " Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "Downloading matplotlib-3.7.2-cp310-cp310-macosx_11_0_arm64.whl (7.3 MB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m7.3/7.3 MB\u001B[0m \u001B[31m30.3 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0ma \u001B[36m0:00:01\u001B[0m\n", + "\u001B[?25hDownloading contourpy-1.1.0-cp310-cp310-macosx_11_0_arm64.whl (229 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m229.4/229.4 kB\u001B[0m \u001B[31m30.6 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hDownloading fonttools-4.42.1-cp310-cp310-macosx_10_9_universal2.whl (2.7 MB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m2.7/2.7 MB\u001B[0m \u001B[31m47.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0ma \u001B[36m0:00:01\u001B[0m\n", + "\u001B[?25hDownloading kiwisolver-1.4.5-cp310-cp310-macosx_11_0_arm64.whl (66 kB)\n", + "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m66.2/66.2 kB\u001B[0m \u001B[31m10.5 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n", + "\u001B[?25hInstalling collected packages: pyparsing, kiwisolver, fonttools, cycler, contourpy, matplotlib\n", + "Successfully installed contourpy-1.1.0 cycler-0.11.0 fonttools-4.42.1 kiwisolver-1.4.5 matplotlib-3.7.2 pyparsing-3.0.9\n" + ] + } + ], + "source": [ + "!pip install matplotlib" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "mpmcL_STdjIo" + }, + "source": [ + "# Centralised training: the old way of doing ML" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "gvw2TZjSec6C" + }, + "source": [ + "Let's begin by creating a simple (but complete) training loop as it is commonly done in centralised setups. Starting our tutorial in this way will allow us to very clearly identify which parts of a typical ML pipeline are common to both centralised and federated training and which ones are poles a part.\n", + "\n", + "For this tutorial we'll design a image classification pipeline for [MNIST digits](https://en.wikipedia.org/wiki/MNIST_database) and using a simple CNN model as the network to train. The MNIST dataset is comprised of `28x28` greyscale images with digits from 0 to 9 (i.e. 10 classes in total)\n", + "\n", + "\n", + "## A dataset\n", + "\n", + "Let's begin by constructing the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "p9aFBjd1cpHs" + }, + "outputs": [], + "source": [ + "# we naturally first need to import torch and torchvision\n", + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from torchvision.transforms import ToTensor, Normalize, Compose\n", + "from torchvision.datasets import MNIST\n", + "\n", + "\n", + "def get_mnist(data_path: str = './data'):\n", + " '''This function downloads the MNIST dataset into the `data_path`\n", + " directory if it is not there already. WE construct the train/test\n", + " split by converting the images into tensors and normalising them'''\n", + "\n", + " # transformation to convert images to tensors and apply normalisation\n", + " tr = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])\n", + "\n", + " # prepare train and test set\n", + " trainset = MNIST(data_path, train=True, download=True, transform=tr)\n", + " testset = MNIST(data_path, train=False, download=True, transform=tr)\n", + "\n", + " return trainset, testset" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "vKoQfqgYgwg0" + }, + "source": [ + "Let's run the code above and do some visualisations to understand better the data we are working with !" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "pS8sL2hDgvZN" + }, + "outputs": [], + "source": [ + "trainset, testset = get_mnist()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "iNA-6AcYhYVM" + }, + "source": [ + "We can have a quick overview of our datasets by just typing the object on the command line. For instance, below you can see that the `trainset` has 60k training examples and will use the transformation rule we defined above in `get_mnist()`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "pWsIHsq-g4nX", + "outputId": "f10b649f-3cee-4e86-c7ff-94bd1fd3e082" + }, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "9sXWjalq-G39" - }, - "source": [ - "Let's create 100 partitions and extract some statistics from one partition\n" + "data": { + "text/plain": [ + "Dataset MNIST\n", + " Number of datapoints: 60000\n", + " Root location: ./data\n", + " Split: Train\n", + " StandardTransform\n", + "Transform: Compose(\n", + " ToTensor()\n", + " Normalize(mean=(0.1307,), std=(0.3081,))\n", + " )" ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainset" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "NY9bWlaMhweq" + }, + "source": [ + "Let's create a more insightful visualisation. First let's see the distribution over the labels by constructing a histogram. Then, let's visualise some training examples !" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 }, + "id": "DCTjwpizikwy", + "outputId": "c8d0f4c0-60cd-4c58-bc91-3b061dae8046" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 508 - }, - "id": "I0LbJhrC0evC", - "outputId": "0f53ca81-cb55-46ef-c8e0-4e19a4f060b2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "number of images: 540\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Class labels distribution for MNIST')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "trainloaders, valloaders, testloader = prepare_dataset(num_partitions=100,\n", - " batch_size=32)\n", - "\n", - "# first partition\n", - "train_partition = trainloaders[0].dataset\n", - "\n", - "# count data points\n", - "partition_indices = train_partition.indices\n", - "print(f\"number of images: {len(partition_indices)}\")\n", - "\n", - "# visualise histogram\n", - "plt.hist(train_partition.dataset.dataset.targets[partition_indices], bins=10)\n", - "plt.grid()\n", - "plt.xticks(range(10))\n", - "plt.xlabel('Label')\n", - "plt.ylabel('Number of images')\n", - "plt.title('Class labels distribution for MNIST')" + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Class labels distribution for MNIST')" ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "me-cNRBO_-fa" - }, - "source": [ - "As you can see, the histogram of this partition is a bit different from the one we obtained at the beginning where we took the entire dataset into consideration. Because our data partitions are artificially constructed by sampling the MNIST dataset in an IID fashion, our Federated Learning example will not face sever _data heterogeneity_ issues (which is a fairly [active research topic](https://arxiv.org/abs/1912.04977)).\n", - "\n", - "Let's next define how our FL clients will behave\n", - "\n", - "## Defining a Flower Client\n", - "\n", - "You can think of a client in FL as an entity that owns some data and trains a model using this data. The caveat is that the model is being trained _collaboratively_ in Federation by multiple clients (sometimes up to hundreds of thousands) and, in most instances of FL, is sent by a central server.\n", - "\n", - "A Flower Client is a simple Python class with four distinct methods:\n", - "\n", - "* `fit()`: With this method, the client does on-device training for a number of epochs using its own data. At the end, the resulting model is sent back to the server for aggregation.\n", - "\n", - "* `evaluate()`: With this method, the server can evaluate the performance of the global model on the local validation set of a client. This can be used for instance when there is no centralised dataset on the server for validation/test. Also, this method can be use to asses the degree of personalisation of the model being federated.\n", - "\n", - "* `set_parameters()`: This method takes the parameters sent by the server and uses them to initialise the parameters of the local model that is ML framework specific (e.g. TF, Pytorch, etc).\n", - "\n", - "* `get_parameters()`: It extract the parameters from the local model and transforms them into a list of NumPy arrays. This ML framework-agnostic representation of the model will be sent to the server.\n", - "\n", - "Let's start by importing Flower!" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "# construct histogram\n", + "all_labels = trainset.targets\n", + "num_possible_labels = len(set(all_labels.numpy().tolist())) # this counts unique labels (so it should be = 10)\n", + "plt.hist(all_labels, bins=num_possible_labels)\n", + "\n", + "# plot formatting\n", + "plt.xticks(range(num_possible_labels))\n", + "plt.grid()\n", + "plt.xlabel('Label')\n", + "plt.ylabel('Number of images')\n", + "plt.title('Class labels distribution for MNIST')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "K7-K0bKamho7" + }, + "source": [ + "Let's visualise 32 images from the dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "ExGypiVsjMUv" + }, + "outputs": [], + "source": [ + "import random\n", + "import numpy as np\n", + "\n", + "def visualise_n_random_examples(trainset_, n: int, verbose: bool = True):\n", + " # take n examples at random\n", + " idx =list(range(len(trainset_.data)))\n", + " random.shuffle(idx)\n", + " idx = idx[:n]\n", + " if verbose:\n", + " print(f\"will display images with idx: {idx}\")\n", + "\n", + "\n", + " # construct canvas\n", + " num_cols = 8\n", + " num_rows = int(np.ceil(len(idx)/num_cols))\n", + " fig, axs = plt.subplots(figsize=(16, num_rows*2), nrows=num_rows, ncols=num_cols)\n", + "\n", + " # display images on canvas\n", + " for c_i, i in enumerate(idx):\n", + " axs.flat[c_i].imshow(trainset_.data[i], cmap='gray')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 715 }, + "id": "xA2s8vqkmkga", + "outputId": "4e0988a8-388d-4acf-882b-089e4ea887bf" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "GckcVE2hH5UV" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-28 20:57:17,516\tINFO util.py:159 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n" - ] - } - ], - "source": [ - "import flwr as fl" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "will display images with idx: [59717, 34422, 26054, 1199, 3182, 18665, 27924, 45921, 19494, 40038, 31891, 22197, 14705, 4590, 46747, 15779, 2575, 32582, 47065, 19149, 41838, 24098, 28738, 39203, 35935, 55347, 16343, 40626, 31743, 34183, 18890, 47438]\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "w3zwIYgVH5wU" - }, - "source": [ - "Now let's define our Flower Client class:" + "data": { + "image/png": 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+ "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# it is likely that the plot this function will generate looks familiar to other plots you might have generated before\n", + "# or you might have encountered in other tutorials. So far, we aren't doing anything new, Federated Learning will start soon!\n", + "visualise_n_random_examples(trainset, n=32)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "PmGyjFEFhVwd" + }, + "source": [ + "# A CNN architecture\n", + "\n", + "This tutorial is not so much about novel architectural designs so we keep things simple and make use of a typical CNN that is adequate for the MNIST image classification task.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "Nr4jR6tspOh4" + }, + "outputs": [], + "source": [ + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "class Net(nn.Module):\n", + " def __init__(self, num_classes: int) -> None:\n", + " super(Net, self).__init__()\n", + " self.conv1 = nn.Conv2d(1, 6, 5)\n", + " self.pool = nn.MaxPool2d(2, 2)\n", + " self.conv2 = nn.Conv2d(6, 16, 5)\n", + " self.fc1 = nn.Linear(16 * 4 * 4, 120)\n", + " self.fc2 = nn.Linear(120, 84)\n", + " self.fc3 = nn.Linear(84, num_classes)\n", + "\n", + " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", + " x = self.pool(F.relu(self.conv1(x)))\n", + " x = self.pool(F.relu(self.conv2(x)))\n", + " x = x.view(-1, 16 * 4 * 4)\n", + " x = F.relu(self.fc1(x))\n", + " x = F.relu(self.fc2(x))\n", + " x = self.fc3(x)\n", + " return x" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "5TJrrCBlpZOp" + }, + "source": [ + "Similarly to what we did with the dataset you could inspect the model in various ways. We can, for instance, count the number of model parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "zdVK9c4hpYaC", + "outputId": "67d01ab4-cdd9-4661-8f01-eaa9aabf786d" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "uXdiNmCE_90y" - }, - "outputs": [], - "source": [ - "from collections import OrderedDict\n", - "from typing import Dict, Tuple\n", - "\n", - "import torch\n", - "from flwr.common import NDArrays, Scalar\n", - "\n", - "class FlowerClient(fl.client.NumPyClient):\n", - " def __init__(self,\n", - " trainloader,\n", - " vallodaer) -> None:\n", - " super().__init__()\n", - "\n", - " self.trainloader = trainloader\n", - " self.valloader = vallodaer\n", - " self.model = Net(num_classes=10)\n", - "\n", - " def set_parameters(self, parameters):\n", - " \"\"\"With the model parameters received from the server,\n", - " overwrite the uninitialise model in this class with them.\"\"\"\n", - "\n", - " params_dict = zip(self.model.state_dict().keys(), parameters)\n", - " state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})\n", - " # now replace the parameters\n", - " self.model.load_state_dict(state_dict, strict=True)\n", - "\n", - " def get_parameters(self, config: Dict[str, Scalar]):\n", - " \"\"\"Extract all model parameters and convert them to a list of\n", - " NumPy arrays. The server doesn't work with PyTorch/TF/etc.\"\"\"\n", - " return [val.cpu().numpy() for _, val in self.model.state_dict().items()]\n", - "\n", - " def fit(self, parameters, config):\n", - " \"\"\"This method train the model using the parameters sent by the\n", - " server on the dataset of this client. At then end, the parameters\n", - " of the locally trained model are communicated back to the server\"\"\"\n", - "\n", - " # copy parameters sent by the server into client's local model\n", - " self.set_parameters(parameters)\n", - "\n", - " # Define the optimizer -------------------------------------------------------------- Essentially the same as in the centralised example above\n", - " optim = torch.optim.SGD(self.model.parameters(), lr=0.01, momentum=0.9)\n", - "\n", - " # do local training -------------------------------------------------------------- Essentially the same as in the centralised example above (but now using the client's data instead of the whole dataset)\n", - " train(self.model, self.trainloader, optim, epochs=1)\n", - "\n", - " # return the model parameters to the server as well as extra info (number of training examples in this case)\n", - " return self.get_parameters({}), len(self.trainloader), {}\n", - "\n", - " def evaluate(self, parameters: NDArrays, config: Dict[str, Scalar]):\n", - " \"\"\"Evaluate the model sent by the server on this client's\n", - " local validation set. Then return performance metrics.\"\"\"\n", - "\n", - " self.set_parameters(parameters)\n", - " loss, accuracy = test(self.model, self.valloader) # <-------------------------- calls the `test` function, just what we did in the centralised setting (but this time using the client's local validation set)\n", - " # send statistics back to the server\n", - " return float(loss), len(self.valloader), {'accuracy': accuracy}" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "num_parameters = 44426\n" + ] + } + ], + "source": [ + "model = Net(num_classes=10)\n", + "num_parameters = sum(value.numel() for value in model.state_dict().values())\n", + "print(f\"{num_parameters = }\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "XAXzw1dlp_oO" + }, + "source": [ + "## The Training Loop\n", + "\n", + "A minimal training loop in PyTorch can be constructed with three functions:\n", + "* `train()` that will train the model given a dataloader.\n", + "* `test()` that will be used to evaluate the performance of the model on held-out data, e.g., a training set.\n", + "* `run_centralised()` which will define additional elements (e.g. the optimiser) and run the training loop over N epochs.\n", + "\n", + "Let's construct these functions!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "DRhz5bcfpw06" + }, + "outputs": [], + "source": [ + "def train(net, trainloader, optimizer, epochs):\n", + " \"\"\"Train the network on the training set.\"\"\"\n", + " criterion = torch.nn.CrossEntropyLoss()\n", + " net.train()\n", + " for _ in range(epochs):\n", + " for images, labels in trainloader:\n", + " optimizer.zero_grad()\n", + " loss = criterion(net(images), labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + " return net\n", + "\n", + "def test(net, testloader):\n", + " \"\"\"Validate the network on the entire test set.\"\"\"\n", + " criterion = torch.nn.CrossEntropyLoss()\n", + " correct, loss = 0, 0.0\n", + " net.eval()\n", + " with torch.no_grad():\n", + " for images, labels in testloader:\n", + " outputs = net(images)\n", + " loss += criterion(outputs, labels).item()\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " correct += (predicted == labels).sum().item()\n", + " accuracy = correct / len(testloader.dataset)\n", + " return loss, accuracy\n", + "\n", + "\n", + "def run_centralised(epochs: int, lr: float, momentum: float=0.9):\n", + " \"\"\"A minimal (but complete) training loop\"\"\"\n", + "\n", + " # instantiate the model\n", + " model = Net(num_classes=10)\n", + "\n", + " # define optimiser with hyperparameters supplied\n", + " optim = torch.optim.SGD(model.parameters(), lr=lr, momentum=momentum)\n", + "\n", + " # get dataset and construct a dataloaders\n", + " trainset, testset = get_mnist()\n", + " trainloader = DataLoader(trainset, batch_size=64, shuffle=True, num_workers=2)\n", + " testloader = DataLoader(testset, batch_size=128)\n", + "\n", + " # train for the specified number of epochs\n", + " trained_model = train(model, trainloader, optim, epochs)\n", + "\n", + " # training is completed, then evaluate model on the test set\n", + " loss, accuracy = test(trained_model, testloader)\n", + " print(f\"{loss = }\")\n", + " print(f\"{accuracy = }\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "76Q0UnqiukYT" + }, + "source": [ + "Let's run this for 5 epochs (you'll see it reaching close to 99% accuracy -- as expected from a centralised setup with the MNIST dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "xgJ6mdNSqzpI", + "outputId": "e8d9b429-178d-4924-e82f-4d4e52863788" + }, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "d5Ku-Z_1Jkvz" - }, - "source": [ - "Spend a few minutes to inspect the `FlowerClient` class above. Please ask questions if there is something unclear !\n", - "\n", - "Then keen-eyed among you might have realised that if we were to fuse the client's `fit()` and `evaluate()` methods, we'll end up with essentially the same as in the `run_centralised()` function we used in the Centralised Training part of this tutorial. And it is true!! In Federated Learning, the way clients perform local training makes use of the same principles as more traditional centralised setup. The key difference is that the dataset now is much smaller and it's never _\"seen\"_ by the entity running the FL workload (i.e. the central server).\n", - "\n", - "\n", - "Talking about the central server... we should define what strategy we want to make use of so the updated models sent from the clients back to the server at the end of the `fit()` method are aggregate.\n", - "\n", - "\n", - "## Chosing a Flower Strategy\n", - "\n", - "\n", - "A strategy sits at the core of the Federated Learning experiment. It is involved in all stages of a FL pipeline: sampling clients; sending the _global model_ to the clients so they can do `fit()`; receive the updated models from the clients and **aggregate** these to construct a new _global model_; define and execute global or federated evaluation; and more.\n", - "\n", - "Flower comes with [many strategies built-in](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy) and more to be available in the next release (`1.5` already!). For this tutorial, let's use what is arguable the most popular strategy out there: `FedAvg`.\n", - "\n", - "The way `FedAvg` works is simple but performs surprisingly well in practice. It is therefore one good strategy to start your experimentation. `FedAvg`, as its name implies, derives a new version of the _global model_ by taking the average of all the models sent by clients participating in the round. You can read all the details [in the paper](https://arxiv.org/abs/1602.05629).\n", - "\n", - "Let's see how we can define `FedAvg` using Flower. We use one of the callbacks called `evaluate_fn` so we can easily evaluate the state of the global model using a small centralised testset. Note this functionality is user-defined since it requires a choice in terms of ML-framework. (if you recall, Flower is framework agnostic).\n", - "\n", - "> This being said, centralised evaluation of the global model is only possible if there exists a centralised dataset that somewhat follows a similar distribution as the data that's spread across clients. In some cases having such centralised dataset for validation is not possible, so the only solution is to federate the evaluation of the _global model_. This is the default behaviour in Flower. If you don't specify teh `evaluate_fn` argument in your strategy, then, centralised global evaluation won't be performed." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "loss = 2.4290689574118005\n", + "accuracy = 0.9894\n" + ] + } + ], + "source": [ + "run_centralised(epochs=5, lr=0.01)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "pyz2gQaluw-5" + }, + "source": [ + "The above centralised formulation has worked just fine for some applications and to showcase the potential of AI in a variety of scenarios. However, as was discussed earlier in the session, centralised training is unsuitable for a larger range of settings were information cannot be collected in order to build a unified (centralised) dataset.\n", + "\n", + "# The Future of AI is Federated\n", + "\n", + "What are the key differences between Federated Learning and Centralised Training? I you could only pick you, probably you'd say:\n", + "* Federated Learning is distributed -- the model is trained on-device by the participating clients.\n", + "* Data remains private and is owned by a specific _client_ -- the data is never sent to the central server.\n", + "\n", + "The are several more differences. But the above two are the main ones to always consider and that are common to all flavours of Federated Learning (e.g. _cross-device_ or _cross-silo_). The remaining of this tutorial is going to focus in transforming the code we have written so far for the centralised setting and construct a Federated Learning pipeline using Flower and PyTorch.\n", + "\n", + "Let's begin! 🚀" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "-Jv-H2HBzbPA" + }, + "source": [ + "## One Client, One Data Partition\n", + "\n", + "To start designing a Federated Learning pipeline we need to meet one of the key properties in FL: each client has its own data partition. To accomplish this with the MNIST dataset, we are going to generate N random partitions, where N is the total number of clients in our FL system." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "Lgc5C6yltJCv" + }, + "outputs": [], + "source": [ + "from torch.utils.data import random_split\n", + "\n", + "def prepare_dataset(num_partitions: int,\n", + " batch_size: int,\n", + " val_ratio: float = 0.1):\n", + "\n", + " \"\"\"This function partitions the training set into N disjoint\n", + " subsets, each will become the local dataset of a client. This\n", + " function also subsequently partitions each traininset partition\n", + " into train and validation. The test set is left intact and will\n", + " be used by the central server to asses the performance of the\n", + " global model. \"\"\"\n", + "\n", + " # get the MNIST dataset\n", + " trainset, testset = get_mnist()\n", + "\n", + " # split trainset into `num_partitions` trainsets\n", + " num_images = len(trainset) // num_partitions\n", + "\n", + " partition_len = [num_images] * num_partitions\n", + "\n", + " trainsets = random_split(trainset, partition_len, torch.Generator().manual_seed(2023))\n", + "\n", + " # create dataloaders with train+val support\n", + " trainloaders = []\n", + " valloaders = []\n", + " for trainset_ in trainsets:\n", + " num_total = len(trainset_)\n", + " num_val = int(val_ratio * num_total)\n", + " num_train = num_total - num_val\n", + "\n", + " for_train, for_val = random_split(trainset_, [num_train, num_val], torch.Generator().manual_seed(2023))\n", + "\n", + " trainloaders.append(DataLoader(for_train, batch_size=batch_size, shuffle=True, num_workers=2))\n", + " valloaders.append(DataLoader(for_val, batch_size=batch_size, shuffle=False, num_workers=2))\n", + "\n", + " # create dataloader for the test set\n", + " testloader = DataLoader(testset, batch_size=128)\n", + "\n", + " return trainloaders, valloaders, testloader" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "9sXWjalq-G39" + }, + "source": [ + "Let's create 100 partitions and extract some statistics from one partition\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 508 }, + "id": "I0LbJhrC0evC", + "outputId": "0f53ca81-cb55-46ef-c8e0-4e19a4f060b2" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "id": "gUmUpH5t-Urn" - }, - "outputs": [], - "source": [ - "def get_evalulate_fn(testloader):\n", - " \"\"\"This is a function that returns a function. The returned\n", - " function (i.e. `evaluate_fn`) will be executed by the strategy\n", - " at the end of each round to evaluate the stat of the global\n", - " model.\"\"\"\n", - " def evaluate_fn(server_round: int, parameters, config):\n", - " \"\"\"This function is executed by the strategy it will instantiate\n", - " a model and replace its parameters with those from the global model.\n", - " The, the model will be evaluate on the test set (recall this is the\n", - " whole MNIST test set).\"\"\"\n", - "\n", - " model = Net(num_classes=10)\n", - "\n", - " # set parameters to the model\n", - " params_dict = zip(model.state_dict().keys(), parameters)\n", - " state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})\n", - " model.load_state_dict(state_dict, strict=True)\n", - "\n", - " # call test\n", - " loss, accuracy = test(model, testloader) # <-------------------------- calls the `test` function, just what we did in the centralised setting\n", - " return loss, {\"accuracy\": accuracy}\n", - "\n", - " return evaluate_fn\n", - "\n", - "\n", - "# now we can define the strategy\n", - "strategy = fl.server.strategy.FedAvg(fraction_fit=0.1, # let's sample 10% of the client each round to do local training\n", - " fraction_evaluate=0.1, # after each round, let's sample 20% of the clients to asses how well the global model is doing\n", - " min_available_clients=100, # total number of clients available in the experiment\n", - " evaluate_fn=get_evalulate_fn(testloader)) # a callback to a function that the strategy can execute to evaluate the state of the global model on a centralised dataset\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "number of images: 540\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "4UV_kBVGRbQT" - }, - "source": [ - "So far we have:\n", - "* created the dataset partitions (one for each client)\n", - "* defined the client class\n", - "* decided on a strategy to use\n", - "\n", - "Now we just need to launch the Flower FL experiment... not so fast! just one final function: let's create another callback that the Simulation Engine will use in order to span VirtualClients. As you can see this is really simple: construct a FlowerClient object, assigning each their own data partition." + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Class labels distribution for MNIST')" ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "frPHAxUg-3Ev" - }, - "outputs": [], - "source": [ - "def generate_client_fn(trainloaders, valloaders):\n", - " def client_fn(cid: str):\n", - " \"\"\"Returns a FlowerClient containing the cid-th data partition\"\"\"\n", - "\n", - " return FlowerClient(trainloader=trainloaders[int(cid)],\n", - " vallodaer=valloaders[int(cid)])\n", - " return client_fn\n", - "\n", - "client_fn_callback = generate_client_fn(trainloaders, valloaders)" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trainloaders, valloaders, testloader = prepare_dataset(num_partitions=100,\n", + " batch_size=32)\n", + "\n", + "# first partition\n", + "train_partition = trainloaders[0].dataset\n", + "\n", + "# count data points\n", + "partition_indices = train_partition.indices\n", + "print(f\"number of images: {len(partition_indices)}\")\n", + "\n", + "# visualise histogram\n", + "plt.hist(train_partition.dataset.dataset.targets[partition_indices], bins=10)\n", + "plt.grid()\n", + "plt.xticks(range(10))\n", + "plt.xlabel('Label')\n", + "plt.ylabel('Number of images')\n", + "plt.title('Class labels distribution for MNIST')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "me-cNRBO_-fa" + }, + "source": [ + "As you can see, the histogram of this partition is a bit different from the one we obtained at the beginning where we took the entire dataset into consideration. Because our data partitions are artificially constructed by sampling the MNIST dataset in an IID fashion, our Federated Learning example will not face sever _data heterogeneity_ issues (which is a fairly [active research topic](https://arxiv.org/abs/1912.04977)).\n", + "\n", + "Let's next define how our FL clients will behave\n", + "\n", + "## Defining a Flower Client\n", + "\n", + "You can think of a client in FL as an entity that owns some data and trains a model using this data. The caveat is that the model is being trained _collaboratively_ in Federation by multiple clients (sometimes up to hundreds of thousands) and, in most instances of FL, is sent by a central server.\n", + "\n", + "A Flower Client is a simple Python class with four distinct methods:\n", + "\n", + "* `fit()`: With this method, the client does on-device training for a number of epochs using its own data. At the end, the resulting model is sent back to the server for aggregation.\n", + "\n", + "* `evaluate()`: With this method, the server can evaluate the performance of the global model on the local validation set of a client. This can be used for instance when there is no centralised dataset on the server for validation/test. Also, this method can be use to asses the degree of personalisation of the model being federated.\n", + "\n", + "* `set_parameters()`: This method takes the parameters sent by the server and uses them to initialise the parameters of the local model that is ML framework specific (e.g. TF, Pytorch, etc).\n", + "\n", + "* `get_parameters()`: It extract the parameters from the local model and transforms them into a list of NumPy arrays. This ML framework-agnostic representation of the model will be sent to the server.\n", + "\n", + "Let's start by importing Flower!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "GckcVE2hH5UV" + }, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "uJ0swdTqSyuA" - }, - "source": [ - "Now we are ready to launch the FL experiment using Flower simulation:" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-28 20:57:17,516\tINFO util.py:159 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n" + ] + } + ], + "source": [ + "import flwr as fl" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "w3zwIYgVH5wU" + }, + "source": [ + "Now let's define our Flower Client class:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "uXdiNmCE_90y" + }, + "outputs": [], + "source": [ + "from collections import OrderedDict\n", + "from typing import Dict, Tuple\n", + "\n", + "import torch\n", + "from flwr.common import NDArrays, Scalar\n", + "\n", + "class FlowerClient(fl.client.NumPyClient):\n", + " def __init__(self,\n", + " trainloader,\n", + " vallodaer) -> None:\n", + " super().__init__()\n", + "\n", + " self.trainloader = trainloader\n", + " self.valloader = vallodaer\n", + " self.model = Net(num_classes=10)\n", + "\n", + " def set_parameters(self, parameters):\n", + " \"\"\"With the model parameters received from the server,\n", + " overwrite the uninitialise model in this class with them.\"\"\"\n", + "\n", + " params_dict = zip(self.model.state_dict().keys(), parameters)\n", + " state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})\n", + " # now replace the parameters\n", + " self.model.load_state_dict(state_dict, strict=True)\n", + "\n", + " def get_parameters(self, config: Dict[str, Scalar]):\n", + " \"\"\"Extract all model parameters and convert them to a list of\n", + " NumPy arrays. The server doesn't work with PyTorch/TF/etc.\"\"\"\n", + " return [val.cpu().numpy() for _, val in self.model.state_dict().items()]\n", + "\n", + " def fit(self, parameters, config):\n", + " \"\"\"This method train the model using the parameters sent by the\n", + " server on the dataset of this client. At then end, the parameters\n", + " of the locally trained model are communicated back to the server\"\"\"\n", + "\n", + " # copy parameters sent by the server into client's local model\n", + " self.set_parameters(parameters)\n", + "\n", + " # Define the optimizer -------------------------------------------------------------- Essentially the same as in the centralised example above\n", + " optim = torch.optim.SGD(self.model.parameters(), lr=0.01, momentum=0.9)\n", + "\n", + " # do local training -------------------------------------------------------------- Essentially the same as in the centralised example above (but now using the client's data instead of the whole dataset)\n", + " train(self.model, self.trainloader, optim, epochs=1)\n", + "\n", + " # return the model parameters to the server as well as extra info (number of training examples in this case)\n", + " return self.get_parameters({}), len(self.trainloader), {}\n", + "\n", + " def evaluate(self, parameters: NDArrays, config: Dict[str, Scalar]):\n", + " \"\"\"Evaluate the model sent by the server on this client's\n", + " local validation set. Then return performance metrics.\"\"\"\n", + "\n", + " self.set_parameters(parameters)\n", + " loss, accuracy = test(self.model, self.valloader) # <-------------------------- calls the `test` function, just what we did in the centralised setting (but this time using the client's local validation set)\n", + " # send statistics back to the server\n", + " return float(loss), len(self.valloader), {'accuracy': accuracy}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "d5Ku-Z_1Jkvz" + }, + "source": [ + "Spend a few minutes to inspect the `FlowerClient` class above. Please ask questions if there is something unclear !\n", + "\n", + "Then keen-eyed among you might have realised that if we were to fuse the client's `fit()` and `evaluate()` methods, we'll end up with essentially the same as in the `run_centralised()` function we used in the Centralised Training part of this tutorial. And it is true!! In Federated Learning, the way clients perform local training makes use of the same principles as more traditional centralised setup. The key difference is that the dataset now is much smaller and it's never _\"seen\"_ by the entity running the FL workload (i.e. the central server).\n", + "\n", + "\n", + "Talking about the central server... we should define what strategy we want to make use of so the updated models sent from the clients back to the server at the end of the `fit()` method are aggregate.\n", + "\n", + "\n", + "## Chosing a Flower Strategy\n", + "\n", + "\n", + "A strategy sits at the core of the Federated Learning experiment. It is involved in all stages of a FL pipeline: sampling clients; sending the _global model_ to the clients so they can do `fit()`; receive the updated models from the clients and **aggregate** these to construct a new _global model_; define and execute global or federated evaluation; and more.\n", + "\n", + "Flower comes with [many strategies built-in](https://github.com/adap/flower/tree/main/src/py/flwr/server/strategy) and more to be available in the next release (`1.5` already!). For this tutorial, let's use what is arguable the most popular strategy out there: `FedAvg`.\n", + "\n", + "The way `FedAvg` works is simple but performs surprisingly well in practice. It is therefore one good strategy to start your experimentation. `FedAvg`, as its name implies, derives a new version of the _global model_ by taking the average of all the models sent by clients participating in the round. You can read all the details [in the paper](https://arxiv.org/abs/1602.05629).\n", + "\n", + "Let's see how we can define `FedAvg` using Flower. We use one of the callbacks called `evaluate_fn` so we can easily evaluate the state of the global model using a small centralised testset. Note this functionality is user-defined since it requires a choice in terms of ML-framework. (if you recall, Flower is framework agnostic).\n", + "\n", + "> This being said, centralised evaluation of the global model is only possible if there exists a centralised dataset that somewhat follows a similar distribution as the data that's spread across clients. In some cases having such centralised dataset for validation is not possible, so the only solution is to federate the evaluation of the _global model_. This is the default behaviour in Flower. If you don't specify teh `evaluate_fn` argument in your strategy, then, centralised global evaluation won't be performed." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "gUmUpH5t-Urn" + }, + "outputs": [], + "source": [ + "def get_evalulate_fn(testloader):\n", + " \"\"\"This is a function that returns a function. The returned\n", + " function (i.e. `evaluate_fn`) will be executed by the strategy\n", + " at the end of each round to evaluate the stat of the global\n", + " model.\"\"\"\n", + " def evaluate_fn(server_round: int, parameters, config):\n", + " \"\"\"This function is executed by the strategy it will instantiate\n", + " a model and replace its parameters with those from the global model.\n", + " The, the model will be evaluate on the test set (recall this is the\n", + " whole MNIST test set).\"\"\"\n", + "\n", + " model = Net(num_classes=10)\n", + "\n", + " # set parameters to the model\n", + " params_dict = zip(model.state_dict().keys(), parameters)\n", + " state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})\n", + " model.load_state_dict(state_dict, strict=True)\n", + "\n", + " # call test\n", + " loss, accuracy = test(model, testloader) # <-------------------------- calls the `test` function, just what we did in the centralised setting\n", + " return loss, {\"accuracy\": accuracy}\n", + "\n", + " return evaluate_fn\n", + "\n", + "\n", + "# now we can define the strategy\n", + "strategy = fl.server.strategy.FedAvg(fraction_fit=0.1, # let's sample 10% of the client each round to do local training\n", + " fraction_evaluate=0.1, # after each round, let's sample 20% of the clients to asses how well the global model is doing\n", + " min_available_clients=100, # total number of clients available in the experiment\n", + " evaluate_fn=get_evalulate_fn(testloader)) # a callback to a function that the strategy can execute to evaluate the state of the global model on a centralised dataset\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "4UV_kBVGRbQT" + }, + "source": [ + "So far we have:\n", + "* created the dataset partitions (one for each client)\n", + "* defined the client class\n", + "* decided on a strategy to use\n", + "\n", + "Now we just need to launch the Flower FL experiment... not so fast! just one final function: let's create another callback that the Simulation Engine will use in order to span VirtualClients. As you can see this is really simple: construct a FlowerClient object, assigning each their own data partition." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "frPHAxUg-3Ev" + }, + "outputs": [], + "source": [ + "def generate_client_fn(trainloaders, valloaders):\n", + " def client_fn(cid: str):\n", + " \"\"\"Returns a FlowerClient containing the cid-th data partition\"\"\"\n", + "\n", + " return FlowerClient(trainloader=trainloaders[int(cid)],\n", + " vallodaer=valloaders[int(cid)])\n", + " return client_fn\n", + "\n", + "client_fn_callback = generate_client_fn(trainloaders, valloaders)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "uJ0swdTqSyuA" + }, + "source": [ + "Now we are ready to launch the FL experiment using Flower simulation:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "VpXEG9cxR9vu", + "outputId": "9ad8dcea-8004-4c6e-a025-e168da636c88" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "VpXEG9cxR9vu", - "outputId": "9ad8dcea-8004-4c6e-a025-e168da636c88" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO flwr 2023-08-28 20:59:31,625 | app.py:175 | Starting Flower simulation, config: ServerConfig(num_rounds=10, round_timeout=None)\n", - "2023-08-28 20:59:34,903\tINFO worker.py:1621 -- Started a local Ray instance.\n", - "INFO flwr 2023-08-28 20:59:35,673 | app.py:210 | Flower VCE: Ray initialized with resources: {'object_store_memory': 2147483648.0, 'CPU': 10.0, 'node:__internal_head__': 1.0, 'node:127.0.0.1': 1.0, 'memory': 19064990925.0}\n", - "INFO flwr 2023-08-28 20:59:35,673 | app.py:218 | No `client_resources` specified. Using minimal resources for clients.\n", - "INFO flwr 2023-08-28 20:59:35,674 | app.py:224 | Flower VCE: Resources for each Virtual Client: {'num_cpus': 1, 'num_gpus': 0.0}\n", - "INFO flwr 2023-08-28 20:59:35,682 | app.py:270 | Flower VCE: Creating VirtualClientEngineActorPool with 10 actors\n", - "INFO flwr 2023-08-28 20:59:35,682 | server.py:89 | Initializing global parameters\n", - "INFO flwr 2023-08-28 20:59:35,683 | server.py:276 | Requesting initial parameters from one random client\n", - "INFO flwr 2023-08-28 20:59:40,091 | server.py:280 | Received initial parameters from one random client\n", - "INFO flwr 2023-08-28 20:59:40,092 | server.py:91 | Evaluating initial parameters\n", - "INFO flwr 2023-08-28 20:59:40,780 | server.py:94 | initial parameters (loss, other metrics): 182.0281903743744, {'accuracy': 0.1114}\n", - "INFO flwr 2023-08-28 20:59:40,780 | server.py:104 | FL starting\n", - "DEBUG flwr 2023-08-28 20:59:40,781 | server.py:222 | fit_round 1: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 20:59:47,133 | server.py:236 | fit_round 1 received 10 results and 0 failures\n", - "WARNING flwr 2023-08-28 20:59:47,141 | fedavg.py:242 | No fit_metrics_aggregation_fn provided\n", - "INFO flwr 2023-08-28 20:59:47,821 | server.py:125 | fit progress: (1, 181.06436610221863, {'accuracy': 0.1341}, 7.040863708942197)\n", - "DEBUG flwr 2023-08-28 20:59:47,822 | server.py:173 | evaluate_round 1: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 20:59:52,362 | server.py:187 | evaluate_round 1 received 10 results and 0 failures\n", - "WARNING flwr 2023-08-28 20:59:52,363 | fedavg.py:273 | No evaluate_metrics_aggregation_fn provided\n", - "DEBUG flwr 2023-08-28 20:59:52,363 | server.py:222 | fit_round 2: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 20:59:56,935 | server.py:236 | fit_round 2 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 20:59:57,627 | server.py:125 | fit progress: (2, 179.6406238079071, {'accuracy': 0.2844}, 16.846019334043376)\n", - "DEBUG flwr 2023-08-28 20:59:57,627 | server.py:173 | evaluate_round 2: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:02,146 | server.py:187 | evaluate_round 2 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:02,146 | server.py:222 | fit_round 3: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:06,692 | server.py:236 | fit_round 3 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:07,369 | server.py:125 | fit progress: (3, 176.3769176006317, {'accuracy': 0.5013}, 26.587926791980863)\n", - "DEBUG flwr 2023-08-28 21:00:07,369 | server.py:173 | evaluate_round 3: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:11,854 | server.py:187 | evaluate_round 3 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:11,855 | server.py:222 | fit_round 4: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:16,728 | server.py:236 | fit_round 4 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:17,414 | server.py:125 | fit progress: (4, 165.48094844818115, {'accuracy': 0.4989}, 36.63336270896252)\n", - "DEBUG flwr 2023-08-28 21:00:17,415 | server.py:173 | evaluate_round 4: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:22,117 | server.py:187 | evaluate_round 4 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:22,118 | server.py:222 | fit_round 5: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:26,776 | server.py:236 | fit_round 5 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:27,456 | server.py:125 | fit progress: (5, 115.77451705932617, {'accuracy': 0.6265}, 46.67501679202542)\n", - "DEBUG flwr 2023-08-28 21:00:27,457 | server.py:173 | evaluate_round 5: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:31,981 | server.py:187 | evaluate_round 5 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:31,982 | server.py:222 | fit_round 6: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:36,573 | server.py:236 | fit_round 6 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:37,266 | server.py:125 | fit progress: (6, 51.16007122397423, {'accuracy': 0.8018}, 56.484427334042266)\n", - "DEBUG flwr 2023-08-28 21:00:37,266 | server.py:173 | evaluate_round 6: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:41,796 | server.py:187 | evaluate_round 6 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:41,797 | server.py:222 | fit_round 7: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:46,326 | server.py:236 | fit_round 7 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:47,010 | server.py:125 | fit progress: (7, 46.40866267681122, {'accuracy': 0.8081}, 66.22883979196195)\n", - "DEBUG flwr 2023-08-28 21:00:47,011 | server.py:173 | evaluate_round 7: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:51,524 | server.py:187 | evaluate_round 7 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:51,525 | server.py:222 | fit_round 8: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:56,240 | server.py:236 | fit_round 8 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:56,982 | server.py:125 | fit progress: (8, 33.36455833911896, {'accuracy': 0.8698}, 76.20065708400216)\n", - "DEBUG flwr 2023-08-28 21:00:56,983 | server.py:173 | evaluate_round 8: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:01:01,527 | server.py:187 | evaluate_round 8 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:01:01,527 | server.py:222 | fit_round 9: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:01:06,174 | server.py:236 | fit_round 9 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:01:06,874 | server.py:125 | fit progress: (9, 28.229523852467537, {'accuracy': 0.9001}, 86.09250316699035)\n", - "DEBUG flwr 2023-08-28 21:01:06,874 | server.py:173 | evaluate_round 9: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:01:11,251 | server.py:187 | evaluate_round 9 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:01:11,251 | server.py:222 | fit_round 10: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:01:15,794 | server.py:236 | fit_round 10 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:01:16,470 | server.py:125 | fit progress: (10, 22.725839115679264, {'accuracy': 0.9168}, 95.68810208397917)\n", - "DEBUG flwr 2023-08-28 21:01:16,470 | server.py:173 | evaluate_round 10: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:01:20,808 | server.py:187 | evaluate_round 10 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:01:20,809 | server.py:153 | FL finished in 100.02693225000985\n", - "INFO flwr 2023-08-28 21:01:20,809 | app.py:225 | app_fit: losses_distributed [(1, 4.5842246294021605), (2, 4.546195244789123), (3, 4.46350576877594), (4, 4.165321779251099), (5, 2.8972655892372132), (6, 1.3353233098983766), (7, 1.2181178748607635), (8, 0.9146452054381371), (9, 0.8028807744383812), (10, 0.4898006349802017)]\n", - "INFO flwr 2023-08-28 21:01:20,809 | app.py:226 | app_fit: metrics_distributed_fit {}\n", - "INFO flwr 2023-08-28 21:01:20,810 | app.py:227 | app_fit: metrics_distributed {}\n", - "INFO flwr 2023-08-28 21:01:20,810 | app.py:228 | app_fit: losses_centralized [(0, 182.0281903743744), (1, 181.06436610221863), (2, 179.6406238079071), (3, 176.3769176006317), (4, 165.48094844818115), (5, 115.77451705932617), (6, 51.16007122397423), (7, 46.40866267681122), (8, 33.36455833911896), (9, 28.229523852467537), (10, 22.725839115679264)]\n", - "INFO flwr 2023-08-28 21:01:20,810 | app.py:229 | app_fit: metrics_centralized {'accuracy': [(0, 0.1114), (1, 0.1341), (2, 0.2844), (3, 0.5013), (4, 0.4989), (5, 0.6265), (6, 0.8018), (7, 0.8081), (8, 0.8698), (9, 0.9001), (10, 0.9168)]}\n" - ] - } - ], - "source": [ - "history = fl.simulation.start_simulation(\n", - " client_fn=client_fn_callback, # a callback to construct a client\n", - " num_clients=100, # total number of clients in the experiment\n", - " config=fl.server.ServerConfig(num_rounds=10), # let's run for 10 rounds\n", - " strategy=strategy, # the strategy that will orchestrate the whole FL pipeline\n", - ")" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO flwr 2023-08-28 20:59:31,625 | app.py:175 | Starting Flower simulation, config: ServerConfig(num_rounds=10, round_timeout=None)\n", + "2023-08-28 20:59:34,903\tINFO worker.py:1621 -- Started a local Ray instance.\n", + "INFO flwr 2023-08-28 20:59:35,673 | app.py:210 | Flower VCE: Ray initialized with resources: {'object_store_memory': 2147483648.0, 'CPU': 10.0, 'node:__internal_head__': 1.0, 'node:127.0.0.1': 1.0, 'memory': 19064990925.0}\n", + "INFO flwr 2023-08-28 20:59:35,673 | app.py:218 | No `client_resources` specified. Using minimal resources for clients.\n", + "INFO flwr 2023-08-28 20:59:35,674 | app.py:224 | Flower VCE: Resources for each Virtual Client: {'num_cpus': 1, 'num_gpus': 0.0}\n", + "INFO flwr 2023-08-28 20:59:35,682 | app.py:270 | Flower VCE: Creating VirtualClientEngineActorPool with 10 actors\n", + "INFO flwr 2023-08-28 20:59:35,682 | server.py:89 | Initializing global parameters\n", + "INFO flwr 2023-08-28 20:59:35,683 | server.py:276 | Requesting initial parameters from one random client\n", + "INFO flwr 2023-08-28 20:59:40,091 | server.py:280 | Received initial parameters from one random client\n", + "INFO flwr 2023-08-28 20:59:40,092 | server.py:91 | Evaluating initial parameters\n", + "INFO flwr 2023-08-28 20:59:40,780 | server.py:94 | initial parameters (loss, other metrics): 182.0281903743744, {'accuracy': 0.1114}\n", + "INFO flwr 2023-08-28 20:59:40,780 | server.py:104 | FL starting\n", + "DEBUG flwr 2023-08-28 20:59:40,781 | server.py:222 | fit_round 1: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 20:59:47,133 | server.py:236 | fit_round 1 received 10 results and 0 failures\n", + "WARNING flwr 2023-08-28 20:59:47,141 | fedavg.py:242 | No fit_metrics_aggregation_fn provided\n", + "INFO flwr 2023-08-28 20:59:47,821 | server.py:125 | fit progress: (1, 181.06436610221863, {'accuracy': 0.1341}, 7.040863708942197)\n", + "DEBUG flwr 2023-08-28 20:59:47,822 | server.py:173 | evaluate_round 1: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 20:59:52,362 | server.py:187 | evaluate_round 1 received 10 results and 0 failures\n", + "WARNING flwr 2023-08-28 20:59:52,363 | fedavg.py:273 | No evaluate_metrics_aggregation_fn provided\n", + "DEBUG flwr 2023-08-28 20:59:52,363 | server.py:222 | fit_round 2: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 20:59:56,935 | server.py:236 | fit_round 2 received 10 results and 0 failures\n", + "INFO flwr 2023-08-28 20:59:57,627 | server.py:125 | fit progress: (2, 179.6406238079071, {'accuracy': 0.2844}, 16.846019334043376)\n", + "DEBUG flwr 2023-08-28 20:59:57,627 | server.py:173 | evaluate_round 2: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:02,146 | server.py:187 | evaluate_round 2 received 10 results and 0 failures\n", + "DEBUG flwr 2023-08-28 21:00:02,146 | server.py:222 | fit_round 3: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:06,692 | server.py:236 | fit_round 3 received 10 results and 0 failures\n", + "INFO flwr 2023-08-28 21:00:07,369 | server.py:125 | fit progress: (3, 176.3769176006317, {'accuracy': 0.5013}, 26.587926791980863)\n", + "DEBUG flwr 2023-08-28 21:00:07,369 | server.py:173 | evaluate_round 3: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:11,854 | server.py:187 | evaluate_round 3 received 10 results and 0 failures\n", + "DEBUG flwr 2023-08-28 21:00:11,855 | server.py:222 | fit_round 4: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:16,728 | server.py:236 | fit_round 4 received 10 results and 0 failures\n", + "INFO flwr 2023-08-28 21:00:17,414 | server.py:125 | fit progress: (4, 165.48094844818115, {'accuracy': 0.4989}, 36.63336270896252)\n", + "DEBUG flwr 2023-08-28 21:00:17,415 | server.py:173 | evaluate_round 4: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:22,117 | server.py:187 | evaluate_round 4 received 10 results and 0 failures\n", + "DEBUG flwr 2023-08-28 21:00:22,118 | server.py:222 | fit_round 5: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:26,776 | server.py:236 | fit_round 5 received 10 results and 0 failures\n", + "INFO flwr 2023-08-28 21:00:27,456 | server.py:125 | fit progress: (5, 115.77451705932617, {'accuracy': 0.6265}, 46.67501679202542)\n", + "DEBUG flwr 2023-08-28 21:00:27,457 | server.py:173 | evaluate_round 5: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:31,981 | server.py:187 | evaluate_round 5 received 10 results and 0 failures\n", + "DEBUG flwr 2023-08-28 21:00:31,982 | server.py:222 | fit_round 6: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:36,573 | server.py:236 | fit_round 6 received 10 results and 0 failures\n", + "INFO flwr 2023-08-28 21:00:37,266 | server.py:125 | fit progress: (6, 51.16007122397423, {'accuracy': 0.8018}, 56.484427334042266)\n", + "DEBUG flwr 2023-08-28 21:00:37,266 | server.py:173 | evaluate_round 6: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:41,796 | server.py:187 | evaluate_round 6 received 10 results and 0 failures\n", + "DEBUG flwr 2023-08-28 21:00:41,797 | server.py:222 | fit_round 7: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:46,326 | server.py:236 | fit_round 7 received 10 results and 0 failures\n", + "INFO flwr 2023-08-28 21:00:47,010 | server.py:125 | fit progress: (7, 46.40866267681122, {'accuracy': 0.8081}, 66.22883979196195)\n", + "DEBUG flwr 2023-08-28 21:00:47,011 | server.py:173 | evaluate_round 7: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:51,524 | server.py:187 | evaluate_round 7 received 10 results and 0 failures\n", + "DEBUG flwr 2023-08-28 21:00:51,525 | server.py:222 | fit_round 8: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:00:56,240 | server.py:236 | fit_round 8 received 10 results and 0 failures\n", + "INFO flwr 2023-08-28 21:00:56,982 | server.py:125 | fit progress: (8, 33.36455833911896, {'accuracy': 0.8698}, 76.20065708400216)\n", + "DEBUG flwr 2023-08-28 21:00:56,983 | server.py:173 | evaluate_round 8: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:01:01,527 | server.py:187 | evaluate_round 8 received 10 results and 0 failures\n", + "DEBUG flwr 2023-08-28 21:01:01,527 | server.py:222 | fit_round 9: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:01:06,174 | server.py:236 | fit_round 9 received 10 results and 0 failures\n", + "INFO flwr 2023-08-28 21:01:06,874 | server.py:125 | fit progress: (9, 28.229523852467537, {'accuracy': 0.9001}, 86.09250316699035)\n", + "DEBUG flwr 2023-08-28 21:01:06,874 | server.py:173 | evaluate_round 9: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:01:11,251 | server.py:187 | evaluate_round 9 received 10 results and 0 failures\n", + "DEBUG flwr 2023-08-28 21:01:11,251 | server.py:222 | fit_round 10: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:01:15,794 | server.py:236 | fit_round 10 received 10 results and 0 failures\n", + "INFO flwr 2023-08-28 21:01:16,470 | server.py:125 | fit progress: (10, 22.725839115679264, {'accuracy': 0.9168}, 95.68810208397917)\n", + "DEBUG flwr 2023-08-28 21:01:16,470 | server.py:173 | evaluate_round 10: strategy sampled 10 clients (out of 100)\n", + "DEBUG flwr 2023-08-28 21:01:20,808 | server.py:187 | evaluate_round 10 received 10 results and 0 failures\n", + "INFO flwr 2023-08-28 21:01:20,809 | server.py:153 | FL finished in 100.02693225000985\n", + "INFO flwr 2023-08-28 21:01:20,809 | app.py:225 | app_fit: losses_distributed [(1, 4.5842246294021605), (2, 4.546195244789123), (3, 4.46350576877594), (4, 4.165321779251099), (5, 2.8972655892372132), (6, 1.3353233098983766), (7, 1.2181178748607635), (8, 0.9146452054381371), (9, 0.8028807744383812), (10, 0.4898006349802017)]\n", + "INFO flwr 2023-08-28 21:01:20,809 | app.py:226 | app_fit: metrics_distributed_fit {}\n", + "INFO flwr 2023-08-28 21:01:20,810 | app.py:227 | app_fit: metrics_distributed {}\n", + "INFO flwr 2023-08-28 21:01:20,810 | app.py:228 | app_fit: losses_centralized [(0, 182.0281903743744), (1, 181.06436610221863), (2, 179.6406238079071), (3, 176.3769176006317), (4, 165.48094844818115), (5, 115.77451705932617), (6, 51.16007122397423), (7, 46.40866267681122), (8, 33.36455833911896), (9, 28.229523852467537), (10, 22.725839115679264)]\n", + "INFO flwr 2023-08-28 21:01:20,810 | app.py:229 | app_fit: metrics_centralized {'accuracy': [(0, 0.1114), (1, 0.1341), (2, 0.2844), (3, 0.5013), (4, 0.4989), (5, 0.6265), (6, 0.8018), (7, 0.8081), (8, 0.8698), (9, 0.9001), (10, 0.9168)]}\n" + ] + } + ], + "source": [ + "history = fl.simulation.start_simulation(\n", + " client_fn=client_fn_callback, # a callback to construct a client\n", + " num_clients=100, # total number of clients in the experiment\n", + " config=fl.server.ServerConfig(num_rounds=10), # let's run for 10 rounds\n", + " strategy=strategy, # the strategy that will orchestrate the whole FL pipeline\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "2hLLbDCEUat7" + }, + "source": [ + "Doing 10 rounds should take less than 2 minutes on a CPU-only Colab instance <-- Flower Simulation is fast! 🚀\n", + "\n", + "You can then use the returned `History` object to either save the results to disk or do some visualisation (or both of course, or neither if you like chaos). Below you can see how you can plot the centralised accuracy obtained at the end of each round (including at the very beginning of the experiment) for the _global model_. This is want the function `evaluate_fn()` that we passed to the strategy reports." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 508 }, + "id": "EQ8GnlFVTJkF", + "outputId": "d8eab106-cee9-4266-9082-0944882cdba8" + }, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "2hLLbDCEUat7" - }, - "source": [ - "Doing 10 rounds should take less than 2 minutes on a CPU-only Colab instance <-- Flower Simulation is fast! 🚀\n", - "\n", - "You can then use the returned `History` object to either save the results to disk or do some visualisation (or both of course, or neither if you like chaos). Below you can see how you can plot the centralised accuracy obtained at the end of each round (including at the very beginning of the experiment) for the _global model_. This is want the function `evaluate_fn()` that we passed to the strategy reports." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "history.metrics_centralized = {'accuracy': [(0, 0.1114), (1, 0.1341), (2, 0.2844), (3, 0.5013), (4, 0.4989), (5, 0.6265), (6, 0.8018), (7, 0.8081), (8, 0.8698), (9, 0.9001), (10, 0.9168)]}\n" + ] }, { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 508 - }, - "id": "EQ8GnlFVTJkF", - "outputId": "d8eab106-cee9-4266-9082-0944882cdba8" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "history.metrics_centralized = {'accuracy': [(0, 0.1114), (1, 0.1341), (2, 0.2844), (3, 0.5013), (4, 0.4989), (5, 0.6265), (6, 0.8018), (7, 0.8081), (8, 0.8698), (9, 0.9001), (10, 0.9168)]}\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'MNIST - IID - 100 clients with 10 clients per round')" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(f\"{history.metrics_centralized = }\")\n", - "\n", - "global_accuracy_centralised = history.metrics_centralized['accuracy']\n", - "round = [data[0] for data in global_accuracy_centralised]\n", - "acc = [100.0*data[1] for data in global_accuracy_centralised]\n", - "plt.plot(round, acc)\n", - "plt.grid()\n", - "plt.ylabel('Accuracy (%)')\n", - "plt.xlabel('Round')\n", - "plt.title('MNIST - IID - 100 clients with 10 clients per round')" + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'MNIST - IID - 100 clients with 10 clients per round')" ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What's next?\n", - "\n", - "Well, if you enjoyed this content, consider giving us a ⭐️ on GitHub -> https://github.com/adap/flower\n", - "\n", - "* **[DOCS]** How about running your Flower clients on the GPU? find out how to do it in the [Flower Simulation Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html)\n", - "\n", - "* **[VIDEO]** You can follow our [detailed line-by-line 9-videos tutorial](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB) about everything you need to know to design your own Flower Simulation pipelines\n", - "\n", - "* Check more advanced simulation examples the Flower GitHub:\n", - "\n", - " * Flower simulation with Tensorflow/Keras: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/adap/flower/tree/main/examples/simulation-tensorflow)\n", - " \n", - " * Flower simulation with Pytorch: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/adap/flower/tree/main/examples/simulation-pytorch)\n", - "\n", - "* **[DOCS]** All Flower examples: https://flower.dev/docs/examples/\n", - "\n", - "* **[VIDEO]** Our Youtube channel: https://www.youtube.com/@flowerlabs\n", - "\n", - "Don't forget to join our Slack channel: https://flower.dev/join-slack/\n" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "print(f\"{history.metrics_centralized = }\")\n", + "\n", + "global_accuracy_centralised = history.metrics_centralized['accuracy']\n", + "round = [data[0] for data in global_accuracy_centralised]\n", + "acc = [100.0*data[1] for data in global_accuracy_centralised]\n", + "plt.plot(round, acc)\n", + "plt.grid()\n", + "plt.ylabel('Accuracy (%)')\n", + "plt.xlabel('Round')\n", + "plt.title('MNIST - IID - 100 clients with 10 clients per round')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What's next?\n", + "\n", + "Well, if you enjoyed this content, consider giving us a ⭐️ on GitHub -> https://github.com/adap/flower\n", + "\n", + "* **[DOCS]** How about running your Flower clients on the GPU? find out how to do it in the [Flower Simulation Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html)\n", + "\n", + "* **[VIDEO]** You can follow our [detailed line-by-line 9-videos tutorial](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB) about everything you need to know to design your own Flower Simulation pipelines\n", + "\n", + "* Check more advanced simulation examples the Flower GitHub:\n", + "\n", + " * Flower simulation with Tensorflow/Keras: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/adap/flower/tree/main/examples/simulation-tensorflow)\n", + " \n", + " * Flower simulation with Pytorch: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/adap/flower/tree/main/examples/simulation-pytorch)\n", + "\n", + "* **[DOCS]** All Flower examples: https://flower.dev/docs/examples/\n", + "\n", + "* **[VIDEO]** Our Youtube channel: https://www.youtube.com/@flowerlabs\n", + "\n", + "Don't forget to join our Slack channel: https://flower.dev/join-slack/\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } From 23e895c9e580023b6a46d2e89c3c3c91ff3f0799 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 6 Sep 2023 10:23:31 +0900 Subject: [PATCH 059/133] fix dataset and models --- baselines/FedMeta/FedMeta/client.py | 9 +-- baselines/FedMeta/FedMeta/dataset.py | 32 ++++----- .../FedMeta/FedMeta/dataset_preparation.py | 67 +++++++++---------- baselines/FedMeta/FedMeta/main.py | 12 ++-- baselines/FedMeta/FedMeta/models.py | 34 +++++----- baselines/FedMeta/FedMeta/strategy.py | 3 +- 6 files changed, 79 insertions(+), 78 deletions(-) diff --git a/baselines/FedMeta/FedMeta/client.py b/baselines/FedMeta/FedMeta/client.py index 30b9f6309046..00e32fe86125 100644 --- a/baselines/FedMeta/FedMeta/client.py +++ b/baselines/FedMeta/FedMeta/client.py @@ -53,21 +53,22 @@ def fit( self.set_parameters(parameters) train( self.net, - self.trainloaders['train'][self.cid], + self.trainloaders['sup'][self.cid], self.device, epochs=self.num_epochs, learning_rate=self.learning_rate, ) - return self.get_parameters({}), len(self.trainloaders['train'][self.cid]), {} + return self.get_parameters({}), len(self.trainloaders['sup'][self.cid]), {} def evaluate( self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[float, int, Dict]: """Implements distributed evaluation for a given client.""" self.set_parameters(parameters) - loss, accuracy = test(self.net, self.valloaders['test'][self.cid], self.device) - return float(loss), len(self.valloaders['test'][self.cid]), {"correct": accuracy} + loss, accuracy, total = test(self.net, self.valloaders['qry'][self.cid], self.device) + # return float(loss), len(self.valloaders['test'][self.cid]), {"correct": accuracy} + return float(loss), total, {"correct": accuracy} def gen_client_fn( diff --git a/baselines/FedMeta/FedMeta/dataset.py b/baselines/FedMeta/FedMeta/dataset.py index 9e046a9b4d8b..8d89af88b4fd 100644 --- a/baselines/FedMeta/FedMeta/dataset.py +++ b/baselines/FedMeta/FedMeta/dataset.py @@ -42,35 +42,29 @@ def load_datasets( # pylint: disable=too-many-arguments ) -> Tuple[DataLoader, DataLoader, DataLoader]: print(f"Dataset partitioning config: {config}") - if config.algo == 'fedavg': - dataset = _partition_data( - dir_path=config.path - ) - - elif config.algo == 'fedmeta(maml)': - dataset = _partition_data( - dir_path=config.path, - support_ratio=config.support_ratio - ) + dataset = _partition_data( + dir_path=config.path, + support_ratio=config.support_ratio + ) clients_list = split_train_validation_test_clients( dataset[0]['users'] ) - trainloaders = {'train': [], 'test': []} - valloaders = {'train': [], 'test': []} - testloaders = {'train': [], 'test': []} + trainloaders = {'sup': [], 'qry': []} + valloaders = {'sup': [], 'qry': []} + testloaders = {'sup': [], 'qry': []} transform = transforms.Compose([transforms.ToTensor()]) for user in clients_list[0]: - trainloaders['train'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) - trainloaders['test'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform))) + trainloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) + trainloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) for user in clients_list[2]: - valloaders['train'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) - valloaders['test'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform))) + valloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) + valloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) for user in clients_list[1]: - testloaders['train'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) - testloaders['test'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform))) + testloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) + testloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) return trainloaders, valloaders, testloaders diff --git a/baselines/FedMeta/FedMeta/dataset_preparation.py b/baselines/FedMeta/FedMeta/dataset_preparation.py index 0234519281c1..88ce4499d9d5 100644 --- a/baselines/FedMeta/FedMeta/dataset_preparation.py +++ b/baselines/FedMeta/FedMeta/dataset_preparation.py @@ -85,37 +85,36 @@ def _partition_data( train_users, train_data = _read_dataset(train_path) test_users, test_data = _read_dataset(test_path) - if support_ratio is None: - train_dataset = {'users': [], 'user_data': {}, 'num_samples': []} - test_dataset = {'users': [], 'user_data': {}, 'num_samples': []} - - for user in train_users: - train_dataset['users'].append(user) - train_dataset['user_data'][user] = {'x': train_data[user]['x'], 'y': train_data[user]['y']} - train_dataset['num_samples'].append(len(train_data[user]['y'])) - - test_dataset['users'].append(user) - test_dataset['user_data'][user] = {'x': test_data[user]['x'], 'y': test_data[user]['y']} - test_dataset['num_samples'].append(len(test_data[user]['y'])) - - return train_dataset, test_dataset - - else: - support_dataset = {'users': [], 'user_data': {}, 'num_samples': []} - query_dataset = {'users': [], 'user_data': {}, 'num_samples': []} - - for user in train_users: - print(f'now preprocessing user : {user}') - all_x = np.asarray(train_data[user]['x'] + test_data[user]['x']) - all_y = np.asarray(train_data[user]['y'] + test_data[user]['y']) - sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio) - - support_dataset['users'].append(user) - support_dataset['user_data'][user] = {'x': sup_x, 'y': sup_y} - support_dataset['num_samples'].append(len(sup_y)) - - query_dataset['users'].append(user) - query_dataset['user_data'][user] = {'x': qry_x, 'y': qry_y} - query_dataset['num_samples'].append(len(qry_y)) - - return support_dataset, query_dataset + # if support_ratio is None: + # train_dataset = {'users': [], 'user_data': {}, 'num_samples': []} + # test_dataset = {'users': [], 'user_data': {}, 'num_samples': []} + # + # for user in train_users: + # train_dataset['users'].append(user) + # train_dataset['user_data'][user] = {'x': train_data[user]['x'], 'y': train_data[user]['y']} + # train_dataset['num_samples'].append(len(train_data[user]['y'])) + # + # test_dataset['users'].append(user) + # test_dataset['user_data'][user] = {'x': test_data[user]['x'], 'y': test_data[user]['y']} + # test_dataset['num_samples'].append(len(test_data[user]['y'])) + # + # return train_dataset, test_dataset + + support_dataset = {'users': [], 'user_data': {}, 'num_samples': []} + query_dataset = {'users': [], 'user_data': {}, 'num_samples': []} + + for user in train_users: + print(f'now preprocessing user : {user}') + all_x = np.asarray(train_data[user]['x'] + test_data[user]['x']) + all_y = np.asarray(train_data[user]['y'] + test_data[user]['y']) + sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio) + + support_dataset['users'].append(user) + support_dataset['user_data'][user] = {'x': sup_x, 'y': sup_y} + support_dataset['num_samples'].append(len(sup_y)) + + query_dataset['users'].append(user) + query_dataset['user_data'][user] = {'x': qry_x, 'y': qry_y} + query_dataset['num_samples'].append(len(qry_y)) + + return support_dataset, query_dataset diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index 18703c5eab2a..d1ceaf4ff058 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -11,11 +11,13 @@ from strategy import weighted_average from dataset import load_datasets from Fedmeta_client_manager import Fedmeta_client_manager - +import os import flwr as fl import client +os.environ['PYTHONHASHSEED'] = str(0) + @hydra.main(config_path="conf", config_name="config", version_base=None) def main(cfg: DictConfig) -> None: @@ -32,8 +34,8 @@ def main(cfg: DictConfig) -> None: # partition dataset and get dataloaders trainloaders, valloaders, testloaders= load_datasets(config=cfg.dataset) # Check config Clients value - if cfg.num_clients > len(trainloaders['train']): - raise ImportError(f"Total Clients num is {len(trainloaders['train'])}") + if cfg.num_clients > len(trainloaders['sup']): + raise ImportError(f"Total Clients num is {len(trainloaders['sup'])}") # prepare function that will be used to spawn each client client_fn = client.gen_client_fn( @@ -48,7 +50,7 @@ def main(cfg: DictConfig) -> None: cfg.strategy, # evaluate_fn=evaluate_fn, evaluate_metrics_aggregation_fn=weighted_average, - min_evaluate_clients=len(valloaders['train']) + # min_evaluate_clients=len(valloaders['sup']) ) # 5. Start Simulation @@ -60,7 +62,7 @@ def main(cfg: DictConfig) -> None: "num_cpus": cfg.client_resources.num_cpus, "num_gpus": cfg.client_resources.num_gpus, }, - client_manager=Fedmeta_client_manager(valid_client=len(valloaders['train'])), + client_manager=Fedmeta_client_manager(valid_client=len(valloaders['sup'])), strategy=strategy, ) diff --git a/baselines/FedMeta/FedMeta/models.py b/baselines/FedMeta/FedMeta/models.py index 5ae02b9a005a..616d0d666500 100644 --- a/baselines/FedMeta/FedMeta/models.py +++ b/baselines/FedMeta/FedMeta/models.py @@ -11,6 +11,8 @@ import torch import torch.nn as nn from torch.utils.data import DataLoader +import torch.nn.functional as F + class Femnist_network(nn.Module): @@ -24,14 +26,15 @@ class Femnist_network(nn.Module): def __init__(self) -> None: super(Femnist_network, self).__init__() + self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5, padding=2) - self.maxpool1 = nn.MaxPool2d(kernel_size=(2, 2)) + self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2) - self.maxpool2 = nn.MaxPool2d(kernel_size=(2, 2)) - self.linear1 = nn.Linear(7 * 7 * 64, 1024) - self.linear2 = nn.Linear(1024, 62) + self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) + self.fc1 = nn.Linear(7 * 7 * 64, 2048) + self.fc2 = nn.Linear(2048, 62) - def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: + def forward(self, x: torch.Tensor) -> torch.Tensor: """Forward pass of the CNN. Parameters @@ -44,14 +47,14 @@ def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: torch.Tensor The resulting Tensor after it has passed through the network """ - output_tensor = torch.relu(self.conv1(input_tensor)) - output_tensor = self.maxpool1(output_tensor) - output_tensor = torch.relu(self.conv2(output_tensor)) - output_tensor = self.maxpool2(output_tensor) - output_tensor = torch.flatten(output_tensor, start_dim=1) - output_tensor = torch.relu((self.linear1(output_tensor))) - output_tensor = self.linear2(output_tensor) - return output_tensor + x = F.relu(self.conv1(x)) + x = self.pool1(x) + x = F.relu(self.conv2(x)) + x = self.pool2(x) + x = x.view(-1, 7 * 7 * 64) # Flatten + x = F.relu(self.fc1(x)) + x = self.fc2(x) + return x def train( # pylint: disable=too-many-arguments @@ -77,7 +80,8 @@ def train( # pylint: disable=too-many-arguments The learning rate for the SGD optimizer. """ criterion = torch.nn.CrossEntropyLoss() - optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) + # optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) + optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate) net.train() for _ in range(epochs): net = _train_one_epoch( @@ -155,4 +159,4 @@ def test( raise ValueError("Testloader can't be 0, exiting...") loss /= len(testloader.dataset) accuracy = correct - return loss, accuracy + return loss, accuracy, total diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index bf5883231945..075c8110e93d 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -41,7 +41,7 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: """ # Multiply accuracy of each client by number of examples used # correct = [num_examples * m["correct"] for num_examples, m in metrics] - correct = [ m["correct"] for _, m in metrics] + correct = [m["correct"] for _, m in metrics] examples = [num_examples for num_examples, _ in metrics] # Aggregate and return custom metric (weighted average) @@ -118,6 +118,7 @@ def aggregate_fit( (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples) for _, fit_res in results ] + parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results)) # Aggregate custom metrics if aggregation fn was provided From d53a22f360f2cf98428587fcd5f3c2aa1340e030 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Tue, 26 Sep 2023 09:30:05 +0900 Subject: [PATCH 060/133] move to other gpu server --- .../FedMeta/FedMeta/Fedmeta_client_manager.py | 2 +- baselines/FedMeta/FedMeta/client.py | 63 +++- baselines/FedMeta/FedMeta/conf/config.yaml | 23 +- baselines/FedMeta/FedMeta/dataset.py | 70 ++++- .../FedMeta/FedMeta/dataset_preparation.py | 76 ++--- baselines/FedMeta/FedMeta/main.py | 13 +- baselines/FedMeta/FedMeta/models.py | 278 ++++++++++++++++-- baselines/FedMeta/FedMeta/strategy.py | 107 ++++++- baselines/FedMeta/FedMeta/utils.py | 58 ++++ 9 files changed, 580 insertions(+), 110 deletions(-) diff --git a/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py index e39b9a64a01a..ca27eac8ed16 100644 --- a/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py +++ b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py @@ -48,6 +48,6 @@ def sample( ) return [] if server_round is not None: - random.seed(server_round-1) + random.seed(server_round) sampled_cids = random.sample(available_cids, num_clients) return [self.clients[cid] for cid in sampled_cids] diff --git a/baselines/FedMeta/FedMeta/client.py b/baselines/FedMeta/FedMeta/client.py index 00e32fe86125..6f36c7c0e8c0 100644 --- a/baselines/FedMeta/FedMeta/client.py +++ b/baselines/FedMeta/FedMeta/client.py @@ -13,7 +13,7 @@ import torch.nn from torch.utils.data import DataLoader -from models import train, test +from models import train, test, train_meta, test_meta class FlowerClient( fl.client.NumPyClient @@ -38,6 +38,7 @@ def __init__( def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays: """Returns the parameters of the current net.""" + # return [val.cpu().numpy() for name, val in self.net.state_dict().items() if 'lr' not in name] return [val.cpu().numpy() for _, val in self.net.state_dict().items()] def set_parameters(self, parameters: NDArrays) -> None: @@ -46,29 +47,72 @@ def set_parameters(self, parameters: NDArrays) -> None: state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict}) self.net.load_state_dict(state_dict, strict=True) + # def fit( + # self, parameters: NDArrays, config: Dict[str, Scalar] + # ) -> Tuple[NDArrays, int, Dict]: + # """Implements distributed fit function for a given client.""" + # self.set_parameters(parameters) + # loss = train( + # self.net, self.trainloaders['sup'][self.cid], + # self.device, + # epochs=self.num_epochs, + # learning_rate=self.learning_rate + # ) + # total_len = len(self.trainloaders['qry'][self.cid].dataset) + len(self.trainloaders['sup'][self.cid].dataset) + # + # # return self.get_parameters({}), len(self.trainloaders['sup'][self.cid].dataset), {"loss" : loss} + # return self.get_parameters({}), total_len, {"loss" : loss} + # + # def evaluate( + # self, parameters: NDArrays, config: Dict[str, Scalar] + # ) -> Tuple[float, int, Dict]: + # """Implements distributed evaluation for a given client.""" + # self.set_parameters(parameters) + # loss, accuracy, total = test( + # self.net, + # self.valloaders['sup'][self.cid], + # self.valloaders['qry'][self.cid], + # self.device, + # learning_rate=self.learning_rate + # ) + # total_len = len(self.valloaders['qry'][self.cid].dataset) + len(self.valloaders['sup'][self.cid].dataset) + # + # return float(loss), total_len, {"correct": accuracy, "loss": loss} + def fit( self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[NDArrays, int, Dict]: """Implements distributed fit function for a given client.""" self.set_parameters(parameters) - train( + alpha = config["alpha"] + loss, grads = train_meta( self.net, self.trainloaders['sup'][self.cid], + self.trainloaders['qry'][self.cid], + alpha, self.device, - epochs=self.num_epochs, - learning_rate=self.learning_rate, + learning_rate=self.learning_rate ) + total_len = len(self.trainloaders['qry'][self.cid].dataset) + len(self.trainloaders['sup'][self.cid].dataset) + return self.get_parameters({}), total_len, {"loss": loss, "grads": grads} - return self.get_parameters({}), len(self.trainloaders['sup'][self.cid]), {} def evaluate( self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[float, int, Dict]: """Implements distributed evaluation for a given client.""" self.set_parameters(parameters) - loss, accuracy, total = test(self.net, self.valloaders['qry'][self.cid], self.device) - # return float(loss), len(self.valloaders['test'][self.cid]), {"correct": accuracy} - return float(loss), total, {"correct": accuracy} + alpha = config["alpha"] + loss, accuracy, total = test_meta( + self.net, + self.valloaders['sup'][self.cid], + self.valloaders['qry'][self.cid], + alpha, + self.device, + learning_rate = self.learning_rate + ) + total_len = len(self.valloaders['qry'][self.cid].dataset) + len(self.valloaders['sup'][self.cid].dataset) + return float(loss), total_len, {"correct": float(accuracy), "loss": loss} def gen_client_fn( @@ -108,7 +152,8 @@ def client_fn(cid: str) -> FlowerClient: print(f'cid : {cid}') # Load model - torch.manual_seed(123) + torch.manual_seed(42) + torch.cuda.manual_seed_all(42) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") net = instantiate(model).to(device) diff --git a/baselines/FedMeta/FedMeta/conf/config.yaml b/baselines/FedMeta/FedMeta/conf/config.yaml index 6255dcae7757..a278bfc510c7 100644 --- a/baselines/FedMeta/FedMeta/conf/config.yaml +++ b/baselines/FedMeta/FedMeta/conf/config.yaml @@ -3,29 +3,36 @@ # Please follow the provided structure (this will ensuring all baseline follow # a similar configuration structure and hence be easy to customise) -num_clients: 15 # total number of clients +#num_clients: 293 # total number of clients +#num_clients: 869 # total number of clients num_epochs: 1 # number of local epochs clients_per_round: 4 -num_rounds: 10 -learning_rate: 0.03 +num_rounds: 400 +learning_rate: 0.1 client_resources: + num_cpus: 8 + num_gpus: 0.5 server_device: cpu dataset: -# algo: fedavg #fedavg, fedavg(meta), fedmeta(maml), fedmeta(meta-sgd) - algo: fedmeta(maml) #fedavg, fedavg(meta), fedmeta(maml), fedmeta(meta-sgd) - path: # Leaf Dataset path (Femnist or Shakespeare) + algo: fedavg #fedavg, fedavg(meta), fedmeta(maml), fedmeta(meta-sgd) +# algo: fedmeta(maml) #fedavg, fedavg(meta), fedmeta(maml), fedmeta(meta-sgd) +# path: /home/ccl/fedmeta/fedmeta_origin/leaf_data # Leaf Dataset path (Femnist or Shakespeare) +# path: /home/ccl/fedmeta/leaf/data/femnist/data + path: /home/ccl/fedmeta/leaf/data/shakespeare/data + # Leaf Dataset path (Femnist or Shakespeare) support_ratio : 0.2 batch_size : 10 # dataset config model: - _target_: baselines.fedmeta.fedmeta.models.Femnist_network # model config +# _target_: models.Femnist_network # model config + _target_: models.StackedLSTM # model config strategy: - _target_: baselines.fedmeta.fedmeta.strategy.FedMeta + _target_: strategy.FedMeta # points to your strategy (either custom or exiting in Flower) fraction_fit: 0.00001 fraction_evaluate: 0.00001 diff --git a/baselines/FedMeta/FedMeta/dataset.py b/baselines/FedMeta/FedMeta/dataset.py index 8d89af88b4fd..20c504016a44 100644 --- a/baselines/FedMeta/FedMeta/dataset.py +++ b/baselines/FedMeta/FedMeta/dataset.py @@ -9,15 +9,32 @@ defined here of course. """ -import numpy as np from torch.utils.data import DataLoader, Dataset from omegaconf import DictConfig from typing import Optional, Tuple from dataset_preparation import _partition_data, split_train_validation_test_clients import numpy as np -from torch.utils.data import DataLoader import torchvision.transforms as transforms +from utils import word_to_indices, letter_to_vec +import torch + +class ShakespeareDataset(Dataset): + def __init__(self, data): + # self.x = x + # self.y = y + sentence, label = data['x'], data['y'] + sentences_to_indices = [word_to_indices(word) for word in sentence] + sentences_to_indices = np.array(sentences_to_indices) + self.sentences_to_indices = np.array(sentences_to_indices, dtype=np.int64) + self.labels = np.array([letter_to_vec(letter) for letter in label], dtype=np.int64) + + def __len__(self): + return len(self.labels) + + def __getitem__(self, index): + data, target = self.sentences_to_indices[index], self.labels[index] + return torch.tensor(data), torch.tensor(target) class FemnistDataset(Dataset): @@ -40,7 +57,6 @@ def __len__(self): def load_datasets( # pylint: disable=too-many-arguments config: DictConfig, ) -> Tuple[DataLoader, DataLoader, DataLoader]: - print(f"Dataset partitioning config: {config}") dataset = _partition_data( dir_path=config.path, @@ -51,20 +67,48 @@ def load_datasets( # pylint: disable=too-many-arguments dataset[0]['users'] ) + trainloaders = {'sup': [], 'qry': []} valloaders = {'sup': [], 'qry': []} testloaders = {'sup': [], 'qry': []} - transform = transforms.Compose([transforms.ToTensor()]) - for user in clients_list[0]: - trainloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) - trainloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) - for user in clients_list[2]: - valloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) - valloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) - for user in clients_list[1]: - testloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) - testloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) + data_type = 'shakespeare' + if data_type == 'shakespeare': + for user in clients_list[0]: + if len(dataset[0]['user_data'][user]['x']) > 10000: + print(f"over 1000 : {user}") + continue + trainloaders['sup'].append( + DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=10, shuffle=True)) + trainloaders['qry'].append( + DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=10)) + for user in clients_list[1]: + if len(dataset[0]['user_data'][user]['x']) > 10000: + print(f"over 1000 : {user}") + continue + valloaders['sup'].append(DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=10, shuffle=True)) + valloaders['qry'].append(DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=10)) + for user in clients_list[2]: + if len(dataset[0]['user_data'][user]['x']) > 10000: + print(f"over 1000 : {user}") + continue + testloaders['sup'].append( + DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=10, shuffle=True)) + testloaders['qry'].append( + DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=10)) + + else: + transform = transforms.Compose([transforms.ToTensor()]) + + for user in clients_list[0]: + trainloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) + trainloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) + for user in clients_list[1]: + valloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10)) + valloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) + for user in clients_list[2]: + testloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10)) + testloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) return trainloaders, valloaders, testloaders diff --git a/baselines/FedMeta/FedMeta/dataset_preparation.py b/baselines/FedMeta/FedMeta/dataset_preparation.py index 88ce4499d9d5..579b438efcab 100644 --- a/baselines/FedMeta/FedMeta/dataset_preparation.py +++ b/baselines/FedMeta/FedMeta/dataset_preparation.py @@ -11,8 +11,7 @@ from typing import List, Optional, Tuple, Dict, DefaultDict from collections import defaultdict import numpy as np -import torch -import random +from sklearn.model_selection import train_test_split # def _read_dataset() -> Dict[List, Dict, List]: @@ -28,6 +27,7 @@ def _read_dataset( """ users = [] data = defaultdict(lambda: None) + num_example = [] files = [f for f in os.listdir(path) if f.endswith('.json')] @@ -36,23 +36,19 @@ def _read_dataset( dataset = json.load(f) users.extend(dataset['users']) data.update(dataset['user_data']) + num_example.extend(dataset['num_samples']) users = list(sorted(data.keys())) - return users, data + return users, data, num_example def support_query_split( data: DefaultDict, label: List, - support_ratio: int, + support_ratio: float, ): - np.random.seed(42) - random_index = np.random.permutation(len(label)) - slice_index = int(len(label) * support_ratio) - train_index = random_index[:slice_index] - test_index = random_index[slice_index:] - return data[train_index].tolist(), data[test_index].tolist(), label[train_index].tolist(), label[ - test_index].tolist() + x_train, x_test, y_train, y_test = train_test_split(data, label, train_size=support_ratio, stratify=label, random_state=42) + return x_train, x_test, y_train, y_test def split_train_validation_test_clients( @@ -79,35 +75,49 @@ def _partition_data( support_ratio: Optional[float] = None, ) -> Tuple[Dict, Dict]: - train_path = f'{dir_path}/train' - test_path = f'{dir_path}/test' - - train_users, train_data = _read_dataset(train_path) - test_users, test_data = _read_dataset(test_path) - - # if support_ratio is None: - # train_dataset = {'users': [], 'user_data': {}, 'num_samples': []} - # test_dataset = {'users': [], 'user_data': {}, 'num_samples': []} - # - # for user in train_users: - # train_dataset['users'].append(user) - # train_dataset['user_data'][user] = {'x': train_data[user]['x'], 'y': train_data[user]['y']} - # train_dataset['num_samples'].append(len(train_data[user]['y'])) - # - # test_dataset['users'].append(user) - # test_dataset['user_data'][user] = {'x': test_data[user]['x'], 'y': test_data[user]['y']} - # test_dataset['num_samples'].append(len(test_data[user]['y'])) - # - # return train_dataset, test_dataset + data_type = 'shakespeare' + print("_partiton_data") + # train_path = f'{dir_path}/train' + train_path = f'{dir_path}/train_0.16' + # test_path = f'{dir_path}/test' + test_path = f'{dir_path}/test_0.16' + + train_users, train_data, train_num = _read_dataset(train_path) + test_users, test_data, test_num = _read_dataset(test_path) + all_dataset = {'users': [], 'user_data': {}, 'num_samples': []} support_dataset = {'users': [], 'user_data': {}, 'num_samples': []} query_dataset = {'users': [], 'user_data': {}, 'num_samples': []} for user in train_users: - print(f'now preprocessing user : {user}') all_x = np.asarray(train_data[user]['x'] + test_data[user]['x']) all_y = np.asarray(train_data[user]['y'] + test_data[user]['y']) - sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio) + + if data_type == 'femnist': + unique, counts = np.unique(all_y, return_counts=True) + class_counts = dict(zip(unique, counts)) + + # Find classes with only one sample + classes_to_remove = [cls for cls, count in class_counts.items() if count == 1] + + # Filter out the samples of those classes + mask = np.isin(all_y, classes_to_remove, invert=True) + + all_x = all_x[mask] + all_y = all_y[mask] + + try: + sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio) + except Exception as e: + print(f"Error occurred at iteration {user}: {e}") + continue + + else: + sup_x, qry_x, sup_y, qry_y = train_test_split(all_x, all_y, train_size=support_ratio, random_state=42) + + all_dataset['users'].append(user) + all_dataset['user_data'][user] = {'x': all_x.tolist(), 'y': all_y.tolist()} + all_dataset['num_samples'].append(len(all_y.tolist())) support_dataset['users'].append(user) support_dataset['user_data'][user] = {'x': sup_x, 'y': sup_y} diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index d1ceaf4ff058..d77996f5bb07 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -11,13 +11,10 @@ from strategy import weighted_average from dataset import load_datasets from Fedmeta_client_manager import Fedmeta_client_manager -import os import flwr as fl import client -os.environ['PYTHONHASHSEED'] = str(0) - @hydra.main(config_path="conf", config_name="config", version_base=None) def main(cfg: DictConfig) -> None: @@ -33,9 +30,6 @@ def main(cfg: DictConfig) -> None: # partition dataset and get dataloaders trainloaders, valloaders, testloaders= load_datasets(config=cfg.dataset) - # Check config Clients value - if cfg.num_clients > len(trainloaders['sup']): - raise ImportError(f"Total Clients num is {len(trainloaders['sup'])}") # prepare function that will be used to spawn each client client_fn = client.gen_client_fn( @@ -46,23 +40,22 @@ def main(cfg: DictConfig) -> None: model=cfg.model, ) + strategy = instantiate( cfg.strategy, - # evaluate_fn=evaluate_fn, evaluate_metrics_aggregation_fn=weighted_average, - # min_evaluate_clients=len(valloaders['sup']) ) # 5. Start Simulation history = fl.simulation.start_simulation( client_fn=client_fn, - num_clients=cfg.num_clients, + num_clients=len(trainloaders['sup']), config=fl.server.ServerConfig(num_rounds=cfg.num_rounds), client_resources={ "num_cpus": cfg.client_resources.num_cpus, "num_gpus": cfg.client_resources.num_gpus, }, - client_manager=Fedmeta_client_manager(valid_client=len(valloaders['sup'])), + client_manager=Fedmeta_client_manager(valid_client=len(valloaders['qry'])), strategy=strategy, ) diff --git a/baselines/FedMeta/FedMeta/models.py b/baselines/FedMeta/FedMeta/models.py index 616d0d666500..f1b7a398a6a6 100644 --- a/baselines/FedMeta/FedMeta/models.py +++ b/baselines/FedMeta/FedMeta/models.py @@ -11,9 +11,24 @@ import torch import torch.nn as nn from torch.utils.data import DataLoader -import torch.nn.functional as F +from copy import deepcopy +class StackedLSTM(nn.Module): + def __init__(self): + super(StackedLSTM, self).__init__() + + self.embedding = nn.Embedding(80, 8) + self.lstm = nn.LSTM(8, 256, num_layers=2, dropout=0.5, batch_first=True) + self.fc = nn.Linear(256, 80) + + def forward(self, text): + embedded = self.embedding(text) + self.lstm.flatten_parameters() + lstm_out, _ = self.lstm(embedded) + final_output = self.fc(lstm_out[:, -1, :]) + return final_output + class Femnist_network(nn.Module): """Convolutional Neural Network architecture. @@ -26,13 +41,14 @@ class Femnist_network(nn.Module): def __init__(self) -> None: super(Femnist_network, self).__init__() - self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5, padding=2) - self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) + self.maxpool1 = nn.MaxPool2d(kernel_size=(2, 2)) self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2) - self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) - self.fc1 = nn.Linear(7 * 7 * 64, 2048) - self.fc2 = nn.Linear(2048, 62) + self.maxpool2 = nn.MaxPool2d(kernel_size=(2, 2)) + self.linear1 = nn.Linear(7 * 7 * 64, 2048) + self.linear2 = nn.Linear(2048, 62) + + def forward(self, x: torch.Tensor) -> torch.Tensor: """Forward pass of the CNN. @@ -47,13 +63,13 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: torch.Tensor The resulting Tensor after it has passed through the network """ - x = F.relu(self.conv1(x)) - x = self.pool1(x) - x = F.relu(self.conv2(x)) - x = self.pool2(x) - x = x.view(-1, 7 * 7 * 64) # Flatten - x = F.relu(self.fc1(x)) - x = self.fc2(x) + x = torch.relu(self.conv1(x)) + x = self.maxpool1(x) + x = torch.relu(self.conv2(x)) + x = self.maxpool2(x) + x = torch.flatten(x, start_dim=1) + x = torch.relu((self.linear1(x))) + x = self.linear2(x) return x @@ -63,7 +79,7 @@ def train( # pylint: disable=too-many-arguments device: torch.device, epochs: int, learning_rate: float, -) -> None: +) -> Tuple[float]: """Train the network on the training set. Parameters @@ -80,13 +96,14 @@ def train( # pylint: disable=too-many-arguments The learning rate for the SGD optimizer. """ criterion = torch.nn.CrossEntropyLoss() - # optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) - optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate) + optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) + # optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate) net.train() for _ in range(epochs): - net = _train_one_epoch( + net, loss = _train_one_epoch( net, trainloader, device, criterion, optimizer ) + return loss def _train_one_epoch( # pylint: disable=too-many-arguments @@ -116,17 +133,26 @@ def _train_one_epoch( # pylint: disable=too-many-arguments nn.Module The model that has been trained for one epoch. """ + total_loss = 0.0 + for images, labels in trainloader: images, labels = images.to(device), labels.to(device) optimizer.zero_grad() - loss = criterion(net(images.to(torch.float32)), labels) + # loss = criterion(net(images.to(torch.float32)), labels) + loss = criterion(net(images), labels) + total_loss += loss.item() * labels.size(0) loss.backward() optimizer.step() - return net + total_loss = total_loss / len(trainloader.dataset) + return net, total_loss def test( - net: nn.Module, testloader: DataLoader, device: torch.device + net: nn.Module, + trainloader: DataLoader, + testloader: DataLoader, + device: torch.device, + learning_rate: float ) -> Tuple[float, float]: """Evaluate the network on the entire test set. @@ -144,19 +170,225 @@ def test( Tuple[float, float] The loss and the accuracy of the input model on the given data. """ + total_loss = 0.0 criterion = torch.nn.CrossEntropyLoss() + optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) + net.train() + for images, labels in trainloader: + images, labels = images.to(device), labels.to(device) + # loss = criterion(net(images.to(torch.float32)), labels) + loss = criterion(net(images), labels) + total_loss += loss * labels.size(0) + optimizer.zero_grad() + loss.backward() + optimizer.step() + # total_loss = total_loss / len(trainloader.dataset) + # optimizer.zero_grad() + # total_loss.backward() + # optimizer.step() + correct, total, loss = 0, 0, 0.0 net.eval() with torch.no_grad(): for images, labels in testloader: images, labels = images.to(device), labels.to(device) - outputs = net(images.to(torch.float32)) - loss += criterion(outputs, labels).item() + # outputs = net(images.to(torch.float32)) + outputs = net(images) + loss += criterion(outputs, labels).item() * labels.size(0) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() if len(testloader.dataset) == 0: raise ValueError("Testloader can't be 0, exiting...") loss /= len(testloader.dataset) - accuracy = correct + accuracy = correct / total return loss, accuracy, total +# +# +def train_meta( # pylint: disable=too-many-arguments + net: nn.Module, + supportloader: DataLoader, + queryloader: DataLoader, + alpha, + device: torch.device, + learning_rate: float, +) -> Tuple[float]: + """Train the network on the training set. + + Parameters + ---------- + net : nn.Module + The neural network to train. + trainloader : DataLoader + The DataLoader containing the data to train the network on. + device : torch.device + The device on which the model should be trained, either 'cpu' or 'cuda'. + epochs : int + The number of epochs the model should be trained for. + learning_rate : float + The learning rate for the SGD optimizer. + """ + criterion = torch.nn.CrossEntropyLoss() + for _ in range(1): + loss, grads = _train_meta_one_epoch( + net, supportloader, queryloader, alpha, criterion, learning_rate, device + ) + return loss, grads + + +def _train_meta_one_epoch( # pylint: disable=too-many-arguments + net: nn.Module, + supportloader: DataLoader, + queryloader: DataLoader, + alpha, + criterion: torch.nn.CrossEntropyLoss, + learning_rate: float, + device: torch.device, +) -> nn.Module: + """Train for one epoch. + + Parameters + ---------- + net : nn.Module + The neural network to train. + trainloader : DataLoader + The DataLoader containing the data to train the network on. + device : torch.device + The device on which the model should be trained, either 'cpu' or 'cuda'. + criterion : torch.nn.CrossEntropyLoss + The loss function to use for training + optimizer : torch.optim.Adam + The optimizer to use for training + + Returns + ------- + nn.Module + The model that has been trained for one epoch. + """ + num_adaptation_steps = 1 + all_adaptation_losses = [] + train_net = deepcopy(net) + # alpha = [alpha.to(device) for alpha in alpha] + for step in range(num_adaptation_steps): + loss_sum = 0.0 + sup_num_sample = [] + sup_total_loss = [] + for images, labels in supportloader: + images, labels = images.to(device), labels.to(device) + # loss = criterion(train_net(images.to(torch.float32)), labels) + loss = criterion(train_net(images), labels) + loss_sum += loss * labels.size(0) + sup_num_sample.append(labels.size(0)) + sup_total_loss.append(loss * labels.size(0)) + grads = torch.autograd.grad(loss, list(train_net.parameters()), create_graph=True, retain_graph=True) + + for p, g in zip(train_net.parameters(), grads): + p.data.add_(g.data, alpha=-learning_rate) + + for p in train_net.parameters(): + if p.grad is not None: + p.grad.zero_() + + # for p, g, a in zip(train_net.parameters(), grads, alpha): + # p.data = p.data - a * g + # + # for p in train_net.parameters(): + # if p.grad is not None: + # p.grad.zero_() + + qry_total_loss = [] + qry_num_sample = [] + loss_sum = 0.0 + for images, labels in queryloader: + images, labels = images.to(device), labels.to(device) + # loss = criterion(train_net(images.to(torch.float32)), labels) + loss = criterion(train_net(images), labels) + loss_sum += loss * labels.size(0) + qry_num_sample.append(labels.size(0)) + qry_total_loss.append(loss.item()) + loss_sum = loss_sum / sum(qry_num_sample) + grads = torch.autograd.grad(loss_sum, list(train_net.parameters())) + + for p in train_net.parameters(): + if p.grad is not None: + p.grad.zero_() + + grads = [g.cpu().numpy() for g in grads] + average_adaptation_loss = sum(sup_total_loss) / sum(sup_num_sample) + return average_adaptation_loss, grads + + +def test_meta( + net: nn.Module, + supportloader: DataLoader, + queryloader: DataLoader, + alpha, + device: torch.device, + learning_rate: float, +) -> Tuple[float, float]: + """Evaluate the network on the entire test set. + + Parameters + ---------- + net : nn.Module + The neural network to test. + testloader : DataLoader + The DataLoader containing the data to test the network on. + device : torch.device + The device on which the model should be tested, either 'cpu' or 'cuda'. + + Returns + ------- + Tuple[float, float] + The loss and the accuracy of the input model on the given data. + """ + criterion = torch.nn.CrossEntropyLoss() + test_net = deepcopy(net) + num_adaptation_steps = 1 + alpha = [alpha_tensor.to(device) for alpha_tensor in alpha] + test_net.train() + for step in range(num_adaptation_steps): + loss_sum = 0.0 + sup_num_sample = [] + sup_total_loss = [] + for images, labels in supportloader: + images, labels = images.to(device), labels.to(device) + # loss = criterion(test_net(images.to(torch.float32)), labels) + loss = criterion(test_net(images), labels) + loss_sum += loss * labels.size(0) + sup_num_sample.append(labels.size(0)) + sup_total_loss.append(loss) + grads = torch.autograd.grad(loss, list(test_net.parameters()), create_graph=True, retain_graph=True) + + for p, g in zip(test_net.parameters(), grads): + p.data.add_(g.data, alpha=-learning_rate) + + for p in test_net.parameters(): + if p.grad is not None: + p.grad.zero_() + + # for p, g, a in zip(test_net.parameters(), grads, alpha): + # p.data -= a * g + # + # for p in test_net.parameters(): + # if p.grad is not None: + # p.grad.zero_() + + test_net.eval() + correct, total, loss = 0, 0, 0.0 + for images, labels in queryloader: + images, labels = images.to(device), labels.to(device) + # outputs = test_net(images.to(torch.float32)) + outputs = test_net(images) + loss += criterion(outputs, labels).item() * labels.size(0) + _, predicted = torch.max(outputs.data, 1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + if len(queryloader.dataset) == 0: + raise ValueError("Testloader can't be 0, exiting...") + loss = loss / total + accuracy = correct / total + return loss, accuracy, total + + + diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index 075c8110e93d..62c6a7503d6a 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -5,12 +5,18 @@ """ from typing import Dict, List, Optional, Tuple, Union from logging import WARNING, INFO +from collections import OrderedDict from flwr.server.client_proxy import ClientProxy from flwr.server.strategy import FedAvg from flwr.server.strategy.aggregate import aggregate, weighted_loss_avg from flwr.server.client_manager import ClientManager from Fedmeta_client_manager import evaluate_client_Criterion +import numpy as np +import torch +from functools import reduce +from models import Femnist_network, StackedLSTM + from flwr.common.logger import log from flwr.common import ( @@ -23,6 +29,16 @@ Metrics, FitIns, EvaluateIns, + NDArrays, +) + +import wandb + +# start a new wandb run to track this script +wandb.init( + # set the wandb project where this run will be logged + project="SoR", + ) @@ -40,20 +56,52 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: The weighted average metric. """ # Multiply accuracy of each client by number of examples used - # correct = [num_examples * m["correct"] for num_examples, m in metrics] - correct = [m["correct"] for _, m in metrics] + correct = [num_examples * m["correct"] for num_examples, m in metrics] + # correct = [m["correct"] for _, m in metrics] examples = [num_examples for num_examples, _ in metrics] # Aggregate and return custom metric (weighted average) return {"accuracy": sum(correct) / sum(examples)} +def aggregate_grad(results: List[Tuple[NDArrays, int]]) -> NDArrays: + """Compute gradients average.""" + # Calculate the total number of examples used during training + num_examples_total = sum([num_examples for _, num_examples in results]) + + # Create a list of weights, each multiplied by the related number of examples + weighted_gradients = [ + [layer * num_examples for layer in gradients] for gradients, num_examples in results + ] + + # weighted_gradients = [gradients for gradients, _ in results] + + # Compute average weights of each layer + grdients_prime: NDArrays = [ + reduce(np.add, layer_updates) / num_examples_total + for layer_updates in zip(*weighted_gradients) + ] + + # grdients_prime: NDArrays = [ + # reduce(np.add, layer_updates) / len(weighted_gradients) + # for layer_updates in zip(*weighted_gradients) + # ] + + return grdients_prime + + class FedMeta(FedAvg): + def __init__(self, **kwargs): + super().__init__(**kwargs) + # self.alpha = [torch.full_like(p, 0.001) for p in Femnist_network().parameters()] + self.alpha = torch.nn.ParameterList([torch.nn.Parameter(torch.full_like(p, 0.001)) for p in Femnist_network().parameters()]) + def configure_fit( - self, server_round: int, parameters: Parameters, client_manager: ClientManager + self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, FitIns]]: """Configure the next round of training.""" - config = {} + # alpha_list = [param.data for param in self.alpha] + config = {"alpha" : self.alpha} if self.on_fit_config_fn is not None: # Custom fit config function provided config = self.on_fit_config_fn(server_round) @@ -73,7 +121,7 @@ def configure_fit( return [(client, fit_ins) for client in clients] def configure_evaluate( - self, server_round: int, parameters: Parameters, client_manager: ClientManager + self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, EvaluateIns]]: """Configure the next round of evaluation.""" # Do not configure federated evaluation if fraction eval is 0. @@ -81,7 +129,7 @@ def configure_evaluate( return [] # Parameters and config - config = {} + config = {"alpha" : self.alpha} if self.on_evaluate_config_fn is not None: # Custom evaluation config function provided config = self.on_evaluate_config_fn(server_round) @@ -93,6 +141,7 @@ def configure_evaluate( ) clients = client_manager.sample( num_clients=sample_size, + server_round=server_round, min_num_clients=min_num_clients, criterion=evaluate_client_Criterion(self.min_evaluate_clients), ) @@ -119,7 +168,32 @@ def aggregate_fit( for _, fit_res in results ] - parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results)) + # parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results)) + + grads_results = [ + (fit_res.metrics['grads'], fit_res.num_examples) + for _, fit_res in results + ] + gradients_aggregated = aggregate_grad(grads_results) + + # net = Femnist_network() + net = StackedLSTM() + params_dict = zip(net.state_dict().keys(), weights_results[0][0]) + state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) + net.load_state_dict(state_dict, strict=True) + # optimizer = torch.optim.Adam(list(net.parameters())+list(self.alpha), lr=0.0001, weight_decay=0.001) + optimizer = torch.optim.Adam(list(net.parameters()), lr=0.01) + for params, grad_ins, alphas in zip(net.parameters(), gradients_aggregated, self.alpha): + params.grad = torch.tensor(grad_ins).to(params.dtype) + alphas.grad = torch.tensor(grad_ins).to(params.dtype) + optimizer.step() + optimizer.zero_grad() + weights_prime = [val.cpu().numpy() for _, val in net.state_dict().items()] + + # weight_loss = sum([fit_res.metrics['loss'] * fit_res.num_examples for _, fit_res in results]) / sum( + # [fit_res.num_examples for _, fit_res in results]) + # wandb.log({"Training Loss": weight_loss}, step=server_round) + # log(INFO, f'Training Loss : {weight_loss}') # Aggregate custom metrics if aggregation fn was provided metrics_aggregated = {} @@ -129,13 +203,14 @@ def aggregate_fit( elif server_round == 1: # Only log this warning once log(WARNING, "No fit_metrics_aggregation_fn provided") - return parameters_aggregated, metrics_aggregated + return ndarrays_to_parameters(weights_prime), metrics_aggregated + # return parameters_aggregated, metrics_aggregated def aggregate_evaluate( - self, - server_round: int, - results: List[Tuple[ClientProxy, EvaluateRes]], - failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]], + self, + server_round: int, + results: List[Tuple[ClientProxy, EvaluateRes]], + failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]], ) -> Tuple[Optional[float], Dict[str, Scalar]]: """Aggregate evaluation losses using weighted average.""" if not results: @@ -152,12 +227,18 @@ def aggregate_evaluate( ] ) + weight_loss = sum([evaluate_res.metrics['loss'] * evaluate_res.num_examples for _, evaluate_res in results]) / sum( + [evaluate_res.num_examples for _, evaluate_res in results]) + wandb.log({"Training Loss": weight_loss}, step=server_round) + log(INFO, f'Training Loss : {weight_loss}') + # Aggregate custom metrics if aggregation fn was provided metrics_aggregated = {} if self.evaluate_metrics_aggregation_fn: eval_metrics = [(res.num_examples, res.metrics) for _, res in results] metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics) - log(INFO, f'Test Accuracy : {metrics_aggregated["accuracy"]:.3%}') + wandb.log({"Test_Accuracy ": round(metrics_aggregated['accuracy'] * 100, 3)}, step=server_round) + log(INFO, f'Test Accuracy : {round(metrics_aggregated["accuracy"] * 100, 3)}') elif server_round == 1: # Only log this warning once log(WARNING, "No evaluate_metrics_aggregation_fn provided") diff --git a/baselines/FedMeta/FedMeta/utils.py b/baselines/FedMeta/FedMeta/utils.py index 9a831719d623..dcd302a14a33 100644 --- a/baselines/FedMeta/FedMeta/utils.py +++ b/baselines/FedMeta/FedMeta/utils.py @@ -4,3 +4,61 @@ example, you may define here things like: loading a model from a checkpoint, saving results, plotting. """ + +import numpy as np +import pickle + +ALL_LETTERS = "\n !\"&'(),-.0123456789:;>?ABCDEFGHIJKLMNOPQRSTUVWXYZ[]abcdefghijklmnopqrstuvwxyz}" +NUM_LETTERS = len(ALL_LETTERS) + + +def _one_hot(index, size): + '''returns one-hot vector with given size and value 1 at given index + ''' + vec = [0 for _ in range(size)] + vec[int(index)] = 1 + return vec + + +def letter_to_vec(letter): + '''returns one-hot representation of given letter + ''' + index = ALL_LETTERS.find(letter) + return index + + +def word_to_indices(word): + '''returns a list of character indices + Args: + word: string + + Return: + indices: int list with length len(word) + ''' + indices = [] + for c in word: + indices.append(ALL_LETTERS.find(c)) + return indices + +# def compute_ema(data, smoothingWeight): +# smoothedData = [] +# last = 0 if len(data) > 0 else float('nan') +# numAccum = 0 +# debiasWeight = 0 +# +# for d in data: +# nextVal = d +# last = last * smoothingWeight + (1 - smoothingWeight) * nextVal +# numAccum += 1 +# debiasWeight = 1.0 - pow(smoothingWeight, numAccum) +# smoothedData.append(last / debiasWeight) +# +# return smoothedData +# +# +# data = [1, 2, 3, 4, 5] +# smoothingWeight = 0.8 +# result = compute_ema(data, smoothingWeight) +# print(result) + + From b18acc55bf3713e1119dde1fbfaf972341d94545 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 27 Sep 2023 18:18:18 +0900 Subject: [PATCH 061/133] update README.md --- baselines/FedMeta/README.md | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 0ddf72e01b90..3281aa2e028d 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -16,20 +16,23 @@ dataset: [Femnist, Shakespeare] # list of datasets you include in your baseline ## About this baseline -****What’s implemented:**** : I implement ~~ +****What’s implemented:**** : **We reimplemented the experiments from the paper 'FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication' by Fei Chen (2018). which proposed the FedMeta(MAML & Meta-SGD) algorithm. Specifically, we replicate the results from Table 2 and Figure 2 of the paper.** -****Datasets:**** : Dataset~ +****Datasets:**** : **Femnist and Shakespeare from Leaf Federated Learning Dataset** -****Hardware Setup:**** : Hardware Setup ~ +****Hardware Setup:**** : **These experiments were run on a machine with 16 CPU threads and 1 GPU(GeForce RTX 2080 Ti). However, the FedMeta experiment using the Shakespeare dataset required more computing power (more than 4 GPUs).** -****Contributors:**** : Jinsoo Kim and Kangyoon Lee +****Contributors:**** : **Jinsoo Kim and Kangyoon Lee** ## Experimental Setup -****Task:**** :warning: *_what’s the primary task that is being federated? (e.g. image classification, next-word prediction). If you have experiments for several, please list them_* +****Task:**** : **A comparison task of four algorithms(FedAvg, FedAvg(Meta), FedMeta(MAML), FedMeta(Meta-SGD)) in the categories of Image Classification and next-word prediction.** -****Model:**** :warning: *_provide details about the model you used in your experiments (if more than use a list). If your model is small, describing it as a table would be :100:. Some FL methods do not use an off-the-shelve model (e.g. ResNet18) instead they create your own. If this is your case, please provide a summary here and give pointers to where in the paper (e.g. Appendix B.4) is detailed._* +****Model:**** :This directory implements two models: +* A two-layer CNN network as used in the FedMeta paper (see `models/Femnist_`). This is the model used by default. +* A StackedLSTM model used in the FedMeta paper for Shakespeare (see `models/StackedLSTM`). +**You can see more detail model at Apendix.A in paper** ****Dataset:**** :warning: *_Earlier you listed already the datasets that your baseline uses. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table._* From e8e0e743d4a9c3ab9bad1d6520be609481aa58a4 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 27 Sep 2023 18:21:18 +0900 Subject: [PATCH 062/133] Test README --- baselines/FedMeta/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 3281aa2e028d..aa3918433bc5 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -16,18 +16,18 @@ dataset: [Femnist, Shakespeare] # list of datasets you include in your baseline ## About this baseline -****What’s implemented:**** : **We reimplemented the experiments from the paper 'FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication' by Fei Chen (2018). which proposed the FedMeta(MAML & Meta-SGD) algorithm. Specifically, we replicate the results from Table 2 and Figure 2 of the paper.** +****What’s implemented:**** : We reimplemented the experiments from the paper 'FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication' by Fei Chen (2018). which proposed the FedMeta(MAML & Meta-SGD) algorithm. Specifically, we replicate the results from Table 2 and Figure 2 of the paper. -****Datasets:**** : **Femnist and Shakespeare from Leaf Federated Learning Dataset** +****Datasets:**** : Femnist and Shakespeare from Leaf Federated Learning Dataset -****Hardware Setup:**** : **These experiments were run on a machine with 16 CPU threads and 1 GPU(GeForce RTX 2080 Ti). However, the FedMeta experiment using the Shakespeare dataset required more computing power (more than 4 GPUs).** +****Hardware Setup:**** : These experiments were run on a machine with 16 CPU threads and 1 GPU(GeForce RTX 2080 Ti). However, the FedMeta experiment using the Shakespeare dataset required more computing power (more than 4 GPUs). -****Contributors:**** : **Jinsoo Kim and Kangyoon Lee** +****Contributors:**** : Jinsoo Kim and Kangyoon Lee ## Experimental Setup -****Task:**** : **A comparison task of four algorithms(FedAvg, FedAvg(Meta), FedMeta(MAML), FedMeta(Meta-SGD)) in the categories of Image Classification and next-word prediction.** +****Task:**** : A comparison task of four algorithms(FedAvg, FedAvg(Meta), FedMeta(MAML), FedMeta(Meta-SGD)) in the categories of Image Classification and next-word prediction. ****Model:**** :This directory implements two models: * A two-layer CNN network as used in the FedMeta paper (see `models/Femnist_`). This is the model used by default. From 9236c3da08fbe769573b3c75d150d20c9f32ed7b Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 27 Sep 2023 18:47:06 +0900 Subject: [PATCH 063/133] fix README --- baselines/FedMeta/README.md | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index aa3918433bc5..b279fbe4a5fd 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -2,7 +2,7 @@ title: Federated Meta-Learning with Fast Convergence and Efficient Communication url: https://arxiv.org/abs/1802.07876 labels: [meta learning, maml, meta-sgd, personalization] # please add between 4 and 10 single-word (maybe two-words) labels (e.g. "system heterogeneity", "image classification", "asynchronous", "weight sharing", "cross-silo") -dataset: [Femnist, Shakespeare] # list of datasets you include in your baseline +dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline --- # FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication @@ -14,11 +14,11 @@ dataset: [Femnist, Shakespeare] # list of datasets you include in your baseline ****Abstract:**** :Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta-learning is a natural choice to handle these issues, and propose a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches. We conduct an extensive empirical evaluation on LEAF datasets and a real-world production dataset, and demonstrate that FedMeta achieves a reduction in required communication cost by 2.82-4.33 times with faster convergence, and an increase in accuracy by 3.23%-14.84% as compared to Federated Averaging (FedAvg) which is a leading optimization algorithm in federated learning. Moreover, FedMeta preserves user privacy since only the parameterized algorithm is transmitted between mobile devices and central servers, and no raw data is collected onto the servers. -## About this baseline +## About this baseline ****What’s implemented:**** : We reimplemented the experiments from the paper 'FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication' by Fei Chen (2018). which proposed the FedMeta(MAML & Meta-SGD) algorithm. Specifically, we replicate the results from Table 2 and Figure 2 of the paper. -****Datasets:**** : Femnist and Shakespeare from Leaf Federated Learning Dataset +****Datasets:**** : FEMNIST and SHAKESPEARE from Leaf Federated Learning Dataset ****Hardware Setup:**** : These experiments were run on a machine with 16 CPU threads and 1 GPU(GeForce RTX 2080 Ti). However, the FedMeta experiment using the Shakespeare dataset required more computing power (more than 4 GPUs). @@ -30,11 +30,17 @@ dataset: [Femnist, Shakespeare] # list of datasets you include in your baseline ****Task:**** : A comparison task of four algorithms(FedAvg, FedAvg(Meta), FedMeta(MAML), FedMeta(Meta-SGD)) in the categories of Image Classification and next-word prediction. ****Model:**** :This directory implements two models: -* A two-layer CNN network as used in the FedMeta paper (see `models/Femnist_`). This is the model used by default. +* A two-layer CNN network as used in the FedMeta paper (see `models/CNN_Network`). This is the model used by default. * A StackedLSTM model used in the FedMeta paper for Shakespeare (see `models/StackedLSTM`). + **You can see more detail model at Apendix.A in paper** -****Dataset:**** :warning: *_Earlier you listed already the datasets that your baseline uses. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table._* +****Dataset:**** : This baseline includes the FEMNIST dataset and SHAKESPEARE. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table. + +| Dataset | #Clients | #Samples | #Classes | partition settings | +|:-----------:|:--------:| :---: |:--------:|:---------------------------------------------:| +| FEMNIST | 1,068 | 235,683 | 62 | Support set : 0.2, Query set : 0.8 | +| SHAKESPEARE | 110 | 625,127 | 80 | Support set : 0.2, Query set : 0.8 | ****Training Hyperparameters:**** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._* From d4de2846ec8bcccf7e19d8498b57b3467fa45601 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 28 Sep 2023 12:03:52 +0900 Subject: [PATCH 064/133] Update ReadME --- baselines/FedMeta/README.md | 25 +++++++++++++++++++------ 1 file changed, 19 insertions(+), 6 deletions(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index b279fbe4a5fd..ba0630c74e15 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -33,17 +33,30 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline * A two-layer CNN network as used in the FedMeta paper (see `models/CNN_Network`). This is the model used by default. * A StackedLSTM model used in the FedMeta paper for Shakespeare (see `models/StackedLSTM`). -**You can see more detail model at Apendix.A in paper** +**You can see more detail at Apendix.A in paper** -****Dataset:**** : This baseline includes the FEMNIST dataset and SHAKESPEARE. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table. +****Dataset:**** : This baseline includes the FEMNIST dataset and SHAKESPEARE. For data partitioning and sampling per client, we use the Leaf GitHub([LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf)). The data and client specifications used in this experiment are listed in the table below (Table 1 in the paper). -| Dataset | #Clients | #Samples | #Classes | partition settings | -|:-----------:|:--------:| :---: |:--------:|:---------------------------------------------:| -| FEMNIST | 1,068 | 235,683 | 62 | Support set : 0.2, Query set : 0.8 | -| SHAKESPEARE | 110 | 625,127 | 80 | Support set : 0.2, Query set : 0.8 | +| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | +|:-----------:|:--------:| :---: |:--------:|:------------------------------------------------------------:|-----------------------------------| +| FEMNIST | 1,068 | 235,683 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Support set : 0.8, Query set : 0.2 | +| SHAKESPEARE | 110 | 625,127 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Support set : 0.8, Query set : 0.2| + +**The original specifications of the Leaf dataset can be found in the Leaf paper(_"LEAF: A Benchmark for Federated Settings"_).** ****Training Hyperparameters:**** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._* +| Algorithm | Dataset | clients per round | number of rounds | batch size | optimizer | Learning Rate(α, β) | client resources | +|:-----------------:|:--------------:|:-------------------:|:------------------:|:-----------:|:---------:|:-------------------:|--------------------------------------| +| FedAvg | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | 0.0001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | +| FedAVg | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | +| FedAvg(Meta) | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | 0.0001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | +| FedAvg(Meta) | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | +| FedMeta(MAML) | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | (0.001,0.0001) | {'num_cpus': 4.0, 'num_gpus': 0.25 } | +| FedMeta(MAML) | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | (0.1,0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | +| FedMeta(Meta-SGD | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | (0.001,0.0001) | {'num_cpus': 4.0, 'num_gpus': 0.25 } | +| FedMeta(Meta-SGD | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | (0.1,0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | + ## Environment Setup From 32a5170da4e98917729006aff1527d3728509785 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 28 Sep 2023 12:12:38 +0900 Subject: [PATCH 065/133] Test ReadME --- baselines/FedMeta/README.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index ba0630c74e15..41ea188764e4 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -37,25 +37,25 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline ****Dataset:**** : This baseline includes the FEMNIST dataset and SHAKESPEARE. For data partitioning and sampling per client, we use the Leaf GitHub([LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf)). The data and client specifications used in this experiment are listed in the table below (Table 1 in the paper). -| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | -|:-----------:|:--------:| :---: |:--------:|:------------------------------------------------------------:|-----------------------------------| -| FEMNIST | 1,068 | 235,683 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Support set : 0.8, Query set : 0.2 | -| SHAKESPEARE | 110 | 625,127 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Support set : 0.8, Query set : 0.2| +| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | +|:-----------:|:--------:| :---: |:--------:|:------------------------------------------------------------:|-----------------------------| +| FEMNIST | 1,068 | 235,683 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Support set(fraction) : 0.2 | +| SHAKESPEARE | 110 | 625,127 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Support set(fraction) : 0.2 | **The original specifications of the Leaf dataset can be found in the Leaf paper(_"LEAF: A Benchmark for Federated Settings"_).** ****Training Hyperparameters:**** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._* -| Algorithm | Dataset | clients per round | number of rounds | batch size | optimizer | Learning Rate(α, β) | client resources | -|:-----------------:|:--------------:|:-------------------:|:------------------:|:-----------:|:---------:|:-------------------:|--------------------------------------| -| FedAvg | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | 0.0001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | -| FedAVg | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | -| FedAvg(Meta) | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | 0.0001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | -| FedAvg(Meta) | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | -| FedMeta(MAML) | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | (0.001,0.0001) | {'num_cpus': 4.0, 'num_gpus': 0.25 } | -| FedMeta(MAML) | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | (0.1,0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | -| FedMeta(Meta-SGD | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | (0.001,0.0001) | {'num_cpus': 4.0, 'num_gpus': 0.25 } | -| FedMeta(Meta-SGD | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | (0.1,0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | +| Algorithm | Dataset | Clients per Round | Number of Rounds | Batch Size | Optimizer | Learning Rate(α, β) | Client Resources | Gradient Step | +|:-----------------:|:--------------:|:-----------------:|:----------------:|:----------:|:---------:|:-------------------:|--------------------------------------|:-------------:| +| FedAvg | FEMNST | 4 | 2000 | 10 | Adam | 0.0001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | +| FedAVg | SHAKESPEARE | 4 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | +| FedAvg(Meta) | FEMNST | 4 | 2000 | 10 | Adam | 0.0001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | +| FedAvg(Meta) | SHAKESPEARE | 4 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | +| FedMeta(MAML) | FEMNST | 4 | 2000 | 10 | Adam | (0.001, 0.0001) | {'num_cpus': 4.0, 'num_gpus': 0.25 } | 5 | +| FedMeta(MAML) | SHAKESPEARE | 4 | 400 | 10 | Adam | (0.1, 0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 1 | +| FedMeta(Meta-SGD | FEMNST | 4 | 2000 | 10 | Adam | (0.001, 0.0001) | {'num_cpus': 4.0, 'num_gpus': 0.25 } | 5 | +| FedMeta(Meta-SGD | SHAKESPEARE | 4 | 400 | 10 | Adam | (0.1, 0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 1 | ## Environment Setup From c16e34c300791f11ce6366cba32a20230a6e6c39 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Fri, 29 Sep 2023 13:01:14 +0900 Subject: [PATCH 066/133] fixed ReadME --- baselines/FedMeta/README.md | 34 ++++++++++++++++++++++------------ 1 file changed, 22 insertions(+), 12 deletions(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 41ea188764e4..ebfada0326ac 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -20,9 +20,9 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline ****Datasets:**** : FEMNIST and SHAKESPEARE from Leaf Federated Learning Dataset -****Hardware Setup:**** : These experiments were run on a machine with 16 CPU threads and 1 GPU(GeForce RTX 2080 Ti). However, the FedMeta experiment using the Shakespeare dataset required more computing power (more than 4 GPUs). +****Hardware Setup:**** : These experiments were run on a machine with 16 CPU threads and 1 GPU(GeForce RTX 2080 Ti). **However, the FedMeta experiment using the Shakespeare dataset required more computing power (more than 4 GPUs).** Out of Memory errors may occur with some clients, but federated learning can continue to operate. -****Contributors:**** : Jinsoo Kim and Kangyoon Lee +****Contributors:**** : **Jinsoo Kim and Kangyoon Lee** ## Experimental Setup @@ -33,18 +33,20 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline * A two-layer CNN network as used in the FedMeta paper (see `models/CNN_Network`). This is the model used by default. * A StackedLSTM model used in the FedMeta paper for Shakespeare (see `models/StackedLSTM`). -**You can see more detail at Apendix.A in paper** +**You can see more detail in Apendix.A of the paper** -****Dataset:**** : This baseline includes the FEMNIST dataset and SHAKESPEARE. For data partitioning and sampling per client, we use the Leaf GitHub([LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf)). The data and client specifications used in this experiment are listed in the table below (Table 1 in the paper). +****Dataset:**** : This baseline includes the FEMNIST dataset and SHAKESPEARE. For data partitioning and sampling per client, we use the Leaf GitHub([LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf)). The data and client specifications used in this experiment are listed in the table below (Table 1 in the paper). -| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | -|:-----------:|:--------:| :---: |:--------:|:------------------------------------------------------------:|-----------------------------| -| FEMNIST | 1,068 | 235,683 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Support set(fraction) : 0.2 | -| SHAKESPEARE | 110 | 625,127 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Support set(fraction) : 0.2 | +**Shakespeare Dataset Issue** : In the FedMeta paper experiment, the Shakespeare dataset had 1126 users. However, due to a current bug, the number of users has decreased to 660 users. Therefore, we have only maintained the total number of data. + +| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | +|:-----------:|:-----------:| :---: |:--------:|:------------------------------------------------------------:|----------------------| +| FEMNIST | 1,068 | 235,683 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | +| SHAKESPEARE | 550 --> 110 | 625,127 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | **The original specifications of the Leaf dataset can be found in the Leaf paper(_"LEAF: A Benchmark for Federated Settings"_).** -****Training Hyperparameters:**** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._* +****Training Hyperparameters:**** : The following table shows the main hyperparameters for this baseline with their default value (i.e. the value used if you run `python main.py` directly) | Algorithm | Dataset | Clients per Round | Number of Rounds | Batch Size | Optimizer | Learning Rate(α, β) | Client Resources | Gradient Step | |:-----------------:|:--------------:|:-----------------:|:----------------:|:----------:|:---------:|:-------------------:|--------------------------------------|:-------------:| @@ -52,9 +54,9 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline | FedAVg | SHAKESPEARE | 4 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | | FedAvg(Meta) | FEMNST | 4 | 2000 | 10 | Adam | 0.0001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | | FedAvg(Meta) | SHAKESPEARE | 4 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | -| FedMeta(MAML) | FEMNST | 4 | 2000 | 10 | Adam | (0.001, 0.0001) | {'num_cpus': 4.0, 'num_gpus': 0.25 } | 5 | +| FedMeta(MAML) | FEMNST | 4 | 2000 | 10 | Adam | (0.001, 0.0001) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 5 | | FedMeta(MAML) | SHAKESPEARE | 4 | 400 | 10 | Adam | (0.1, 0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 1 | -| FedMeta(Meta-SGD | FEMNST | 4 | 2000 | 10 | Adam | (0.001, 0.0001) | {'num_cpus': 4.0, 'num_gpus': 0.25 } | 5 | +| FedMeta(Meta-SGD | FEMNST | 4 | 2000 | 10 | Adam | (0.001, 0.0001) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 5 | | FedMeta(Meta-SGD | SHAKESPEARE | 4 | 400 | 10 | Adam | (0.1, 0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 1 | @@ -65,7 +67,15 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline ## Running the Experiments -:warning: _Provide instructions on the steps to follow to run all the experiments._ +****Download Dataset**** : . Go [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf) and Use the command below!. You can download dataset (FEMNIST and SHAKESPEARE). +```bash +#FEMNIST dataset Download command for these experiments +./preprocess.sh -s niid --iu 1068 --sf 0.3 -k 0 -t sample + +#SHAKESEPEARE dataset Download command for these experiments +./preprocess.sh -s niid --iu 1068 --sf 0.3 -k 0 -t sample +```` + ```bash # The main experiment implemented in your baseline using default hyperparameters (that should be setup in the Hydra configs) should run (including dataset download and necessary partitioning) by executing the command: From df3d0fac302438c68ceea5ff7c20514328d8c3d3 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Fri, 29 Sep 2023 13:05:14 +0900 Subject: [PATCH 067/133] updata README --- baselines/FedMeta/README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index ebfada0326ac..d4cfccb47ec6 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -39,14 +39,14 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline **Shakespeare Dataset Issue** : In the FedMeta paper experiment, the Shakespeare dataset had 1126 users. However, due to a current bug, the number of users has decreased to 660 users. Therefore, we have only maintained the total number of data. -| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | -|:-----------:|:-----------:| :---: |:--------:|:------------------------------------------------------------:|----------------------| -| FEMNIST | 1,068 | 235,683 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | -| SHAKESPEARE | 550 --> 110 | 625,127 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | +| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | +|:-----------:|:----------:| :---: |:--------:|:------------------------------------------------------------:|----------------------| +| FEMNIST | 1,068 | 235,683 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | +| SHAKESPEARE | 550 -> 110 | 625,127 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | **The original specifications of the Leaf dataset can be found in the Leaf paper(_"LEAF: A Benchmark for Federated Settings"_).** -****Training Hyperparameters:**** : The following table shows the main hyperparameters for this baseline with their default value (i.e. the value used if you run `python main.py` directly) +****Training Hyperparameters:**** : The following table shows the main hyperparameters for this baseline with their default value (i.e. the value used if you run `python main.py algo=? data=?` directly) | Algorithm | Dataset | Clients per Round | Number of Rounds | Batch Size | Optimizer | Learning Rate(α, β) | Client Resources | Gradient Step | |:-----------------:|:--------------:|:-----------------:|:----------------:|:----------:|:---------:|:-------------------:|--------------------------------------|:-------------:| @@ -67,7 +67,7 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline ## Running the Experiments -****Download Dataset**** : . Go [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf) and Use the command below!. You can download dataset (FEMNIST and SHAKESPEARE). +****Download Dataset**** : Go [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf) and Use the command below! You can download dataset (FEMNIST and SHAKESPEARE). ```bash #FEMNIST dataset Download command for these experiments ./preprocess.sh -s niid --iu 1068 --sf 0.3 -k 0 -t sample From 4ad8fa1b22a6a7fbbe77ff13b36120fd6047af57 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Fri, 29 Sep 2023 13:33:02 +0900 Subject: [PATCH 068/133] Finish Fedmeta experiments --- baselines/FedMeta/FedMeta/client.py | 109 ++++++++-------- .../FedMeta/FedMeta/conf/algo/fedavg.yaml | 14 +++ .../FedMeta/conf/algo/fedavg_meta.yaml | 13 ++ .../FedMeta/conf/algo/fedmeta_maml.yaml | 13 ++ .../FedMeta/conf/algo/fedmeta_meta_sgd.yaml | 13 ++ baselines/FedMeta/FedMeta/conf/config.yaml | 36 +----- baselines/FedMeta/FedMeta/dataset.py | 59 +++++---- .../FedMeta/FedMeta/dataset_preparation.py | 18 +-- baselines/FedMeta/FedMeta/main.py | 21 ++-- baselines/FedMeta/FedMeta/models.py | 99 +++++++-------- baselines/FedMeta/FedMeta/strategy.py | 116 ++++++++++-------- 11 files changed, 271 insertions(+), 240 deletions(-) create mode 100644 baselines/FedMeta/FedMeta/conf/algo/fedavg.yaml create mode 100644 baselines/FedMeta/FedMeta/conf/algo/fedavg_meta.yaml create mode 100644 baselines/FedMeta/FedMeta/conf/algo/fedmeta_maml.yaml create mode 100644 baselines/FedMeta/FedMeta/conf/algo/fedmeta_meta_sgd.yaml diff --git a/baselines/FedMeta/FedMeta/client.py b/baselines/FedMeta/FedMeta/client.py index 6f36c7c0e8c0..5cd045bb908b 100644 --- a/baselines/FedMeta/FedMeta/client.py +++ b/baselines/FedMeta/FedMeta/client.py @@ -26,7 +26,8 @@ def __init__( cid: str, device: torch.device, num_epochs: int, - learning_rate: float + learning_rate: float, + gradient_step: int ) -> object: self.net = net self.trainloaders = trainloaders @@ -35,10 +36,10 @@ def __init__( self.device = device self.num_epochs = num_epochs self.learning_rate = learning_rate + self.gradient_step = gradient_step def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays: """Returns the parameters of the current net.""" - # return [val.cpu().numpy() for name, val in self.net.state_dict().items() if 'lr' not in name] return [val.cpu().numpy() for _, val in self.net.state_dict().items()] def set_parameters(self, parameters: NDArrays) -> None: @@ -47,72 +48,65 @@ def set_parameters(self, parameters: NDArrays) -> None: state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict}) self.net.load_state_dict(state_dict, strict=True) - # def fit( - # self, parameters: NDArrays, config: Dict[str, Scalar] - # ) -> Tuple[NDArrays, int, Dict]: - # """Implements distributed fit function for a given client.""" - # self.set_parameters(parameters) - # loss = train( - # self.net, self.trainloaders['sup'][self.cid], - # self.device, - # epochs=self.num_epochs, - # learning_rate=self.learning_rate - # ) - # total_len = len(self.trainloaders['qry'][self.cid].dataset) + len(self.trainloaders['sup'][self.cid].dataset) - # - # # return self.get_parameters({}), len(self.trainloaders['sup'][self.cid].dataset), {"loss" : loss} - # return self.get_parameters({}), total_len, {"loss" : loss} - # - # def evaluate( - # self, parameters: NDArrays, config: Dict[str, Scalar] - # ) -> Tuple[float, int, Dict]: - # """Implements distributed evaluation for a given client.""" - # self.set_parameters(parameters) - # loss, accuracy, total = test( - # self.net, - # self.valloaders['sup'][self.cid], - # self.valloaders['qry'][self.cid], - # self.device, - # learning_rate=self.learning_rate - # ) - # total_len = len(self.valloaders['qry'][self.cid].dataset) + len(self.valloaders['sup'][self.cid].dataset) - # - # return float(loss), total_len, {"correct": accuracy, "loss": loss} - def fit( self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[NDArrays, int, Dict]: """Implements distributed fit function for a given client.""" self.set_parameters(parameters) - alpha = config["alpha"] - loss, grads = train_meta( - self.net, - self.trainloaders['sup'][self.cid], - self.trainloaders['qry'][self.cid], - alpha, - self.device, - learning_rate=self.learning_rate - ) + algo = config["algo"] total_len = len(self.trainloaders['qry'][self.cid].dataset) + len(self.trainloaders['sup'][self.cid].dataset) - return self.get_parameters({}), total_len, {"loss": loss, "grads": grads} - + if algo == 'fedavg' or algo == 'fedavg(meta)': + loss = train( + self.net, + self.trainloaders['sup'][self.cid], + self.trainloaders['qry'][self.cid], + self.device, + epochs=self.num_epochs, + learning_rate=self.learning_rate + ) + return self.get_parameters({}), total_len, {"loss" : loss} + + elif algo == 'fedmeta(maml)' or algo == 'fedmeta(meta-sgd)': + alpha = config["alpha"] + loss, grads = train_meta( + self.net, + self.trainloaders['sup'][self.cid], + self.trainloaders['qry'][self.cid], + alpha, + self.device, + self.gradient_step, + ) + return self.get_parameters({}), total_len, {"loss": loss, "grads": grads} def evaluate( self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[float, int, Dict]: """Implements distributed evaluation for a given client.""" self.set_parameters(parameters) - alpha = config["alpha"] - loss, accuracy, total = test_meta( - self.net, - self.valloaders['sup'][self.cid], - self.valloaders['qry'][self.cid], - alpha, - self.device, - learning_rate = self.learning_rate - ) total_len = len(self.valloaders['qry'][self.cid].dataset) + len(self.valloaders['sup'][self.cid].dataset) - return float(loss), total_len, {"correct": float(accuracy), "loss": loss} + if config["algo"] == 'fedavg' or config["algo"] == 'fedavg(meta)': + loss, accuracy, total = test( + self.net, + self.valloaders['sup'][self.cid], + self.valloaders['qry'][self.cid], + self.device, + config["algo"], + config["data"], + learning_rate=self.learning_rate, + ) + return float(loss), total_len, {"correct": accuracy, "loss": loss} + + elif config["algo"] == 'fedmeta(maml)' or config["algo"] == 'fedmeta(meta-sgd)': + alpha = config["alpha"] + loss, accuracy, total = test_meta( + self.net, + self.valloaders['sup'][self.cid], + self.valloaders['qry'][self.cid], + alpha, + self.device, + self.gradient_step, + ) + return float(loss), total_len, {"correct": float(accuracy), "loss": loss} def gen_client_fn( @@ -121,6 +115,7 @@ def gen_client_fn( valloaders: List[DataLoader], learning_rate: float, model: DictConfig, + gradient_step: int, ) -> Callable[[str], FlowerClient]: # pylint: disable=too-many-arguments """Generates the client function that creates the Flower Clients. @@ -157,9 +152,6 @@ def client_fn(cid: str) -> FlowerClient: device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") net = instantiate(model).to(device) - # Note: each client gets a different trainloader/valloader, so each client - # will train and evaluate on their own unique data - return FlowerClient( net, trainloaders, @@ -168,6 +160,7 @@ def client_fn(cid: str) -> FlowerClient: device, num_epochs, learning_rate, + gradient_step ) return client_fn diff --git a/baselines/FedMeta/FedMeta/conf/algo/fedavg.yaml b/baselines/FedMeta/FedMeta/conf/algo/fedavg.yaml new file mode 100644 index 000000000000..df5a5b4b6b65 --- /dev/null +++ b/baselines/FedMeta/FedMeta/conf/algo/fedavg.yaml @@ -0,0 +1,14 @@ +--- +# this is the config that will be loaded as default by main.py +# Please follow the provided structure (this will ensuring all baseline follow +# a similar configuration structure and hence be easy to customise) + +algo: fedavg +femnist: + alpha: 0.0001 + beta: 0 + +shakespeare: + alpha: 0.001 + beta: 0 + diff --git a/baselines/FedMeta/FedMeta/conf/algo/fedavg_meta.yaml b/baselines/FedMeta/FedMeta/conf/algo/fedavg_meta.yaml new file mode 100644 index 000000000000..01bcb0d8f218 --- /dev/null +++ b/baselines/FedMeta/FedMeta/conf/algo/fedavg_meta.yaml @@ -0,0 +1,13 @@ +--- +# this is the config that will be loaded as default by main.py +# Please follow the provided structure (this will ensuring all baseline follow +# a similar configuration structure and hence be easy to customise) + +algo: fedavg(meta) +femnist: + alpha: 0.0001 + beta: None + +shakespeare: + alpha: 0.001 + beta: None diff --git a/baselines/FedMeta/FedMeta/conf/algo/fedmeta_maml.yaml b/baselines/FedMeta/FedMeta/conf/algo/fedmeta_maml.yaml new file mode 100644 index 000000000000..1f6d0586bdad --- /dev/null +++ b/baselines/FedMeta/FedMeta/conf/algo/fedmeta_maml.yaml @@ -0,0 +1,13 @@ +--- +# this is the config that will be loaded as default by main.py +# Please follow the provided structure (this will ensuring all baseline follow +# a similar configuration structure and hence be easy to customise) + +algo: fedmeta(maml) +femnist: + alpha: 0.001 + beta: 0.0001 + +shakespeare: + alpha: 0.1 + beta: 0.01 diff --git a/baselines/FedMeta/FedMeta/conf/algo/fedmeta_meta_sgd.yaml b/baselines/FedMeta/FedMeta/conf/algo/fedmeta_meta_sgd.yaml new file mode 100644 index 000000000000..ebb53dd602a2 --- /dev/null +++ b/baselines/FedMeta/FedMeta/conf/algo/fedmeta_meta_sgd.yaml @@ -0,0 +1,13 @@ +--- +# this is the config that will be loaded as default by main.py +# Please follow the provided structure (this will ensuring all baseline follow +# a similar configuration structure and hence be easy to customise) + +algo: fedmeta(meta-sgd) +femnist: + alpha: 0.001 + beta: 0.0001 + +shakespeare: + alpha: 0.1 + beta: 0.01 diff --git a/baselines/FedMeta/FedMeta/conf/config.yaml b/baselines/FedMeta/FedMeta/conf/config.yaml index a278bfc510c7..94b68c515e9a 100644 --- a/baselines/FedMeta/FedMeta/conf/config.yaml +++ b/baselines/FedMeta/FedMeta/conf/config.yaml @@ -3,43 +3,19 @@ # Please follow the provided structure (this will ensuring all baseline follow # a similar configuration structure and hence be easy to customise) -#num_clients: 293 # total number of clients -#num_clients: 869 # total number of clients -num_epochs: 1 # number of local epochs +path: ??? +num_epochs: 1 clients_per_round: 4 -num_rounds: 400 -learning_rate: 0.1 -client_resources: - num_cpus: 8 - num_gpus: 0.5 - -server_device: cpu - -dataset: - algo: fedavg #fedavg, fedavg(meta), fedmeta(maml), fedmeta(meta-sgd) -# algo: fedmeta(maml) #fedavg, fedavg(meta), fedmeta(maml), fedmeta(meta-sgd) -# path: /home/ccl/fedmeta/fedmeta_origin/leaf_data # Leaf Dataset path (Femnist or Shakespeare) -# path: /home/ccl/fedmeta/leaf/data/femnist/data - path: /home/ccl/fedmeta/leaf/data/shakespeare/data - # Leaf Dataset path (Femnist or Shakespeare) - support_ratio : 0.2 - batch_size : 10 - # dataset config - -model: -# _target_: models.Femnist_network # model config - _target_: models.StackedLSTM # model config +defaults: + - _self_ + - algo: ??? + - data: ??? strategy: _target_: strategy.FedMeta - # points to your strategy (either custom or exiting in Flower) fraction_fit: 0.00001 fraction_evaluate: 0.00001 min_fit_clients : ${clients_per_round} min_evaluate_clients : ${clients_per_round} min_available_clients : ${clients_per_round} - # rest of strategy config - -client: - # client config diff --git a/baselines/FedMeta/FedMeta/dataset.py b/baselines/FedMeta/FedMeta/dataset.py index 20c504016a44..434e70e1d0f7 100644 --- a/baselines/FedMeta/FedMeta/dataset.py +++ b/baselines/FedMeta/FedMeta/dataset.py @@ -21,8 +21,6 @@ class ShakespeareDataset(Dataset): def __init__(self, data): - # self.x = x - # self.y = y sentence, label = data['x'], data['y'] sentences_to_indices = [word_to_indices(word) for word in sentence] sentences_to_indices = np.array(sentences_to_indices) @@ -48,7 +46,7 @@ def __getitem__(self, index): if self.transform: input_data = self.transform(input_data) target_data = self.y[index] - return input_data, target_data + return input_data.to(torch.float32), target_data def __len__(self): return len(self.y) @@ -56,10 +54,12 @@ def __len__(self): def load_datasets( # pylint: disable=too-many-arguments config: DictConfig, + path: str, ) -> Tuple[DataLoader, DataLoader, DataLoader]: dataset = _partition_data( - dir_path=config.path, + data_type=config.data, + dir_path=path, support_ratio=config.support_ratio ) @@ -67,48 +67,45 @@ def load_datasets( # pylint: disable=too-many-arguments dataset[0]['users'] ) - trainloaders = {'sup': [], 'qry': []} valloaders = {'sup': [], 'qry': []} testloaders = {'sup': [], 'qry': []} - data_type = 'shakespeare' - if data_type == 'shakespeare': + data_type = config.data + if data_type == 'femnist': + transform = transforms.Compose([transforms.ToTensor()]) for user in clients_list[0]: - if len(dataset[0]['user_data'][user]['x']) > 10000: - print(f"over 1000 : {user}") - continue trainloaders['sup'].append( - DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=10, shuffle=True)) + DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=config.batch_size, shuffle=True)) trainloaders['qry'].append( - DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=10)) + DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=config.batch_size)) for user in clients_list[1]: - if len(dataset[0]['user_data'][user]['x']) > 10000: - print(f"over 1000 : {user}") - continue - valloaders['sup'].append(DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=10, shuffle=True)) - valloaders['qry'].append(DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=10)) + valloaders['sup'].append( + DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=config.batch_size)) + valloaders['qry'].append( + DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=config.batch_size)) for user in clients_list[2]: - if len(dataset[0]['user_data'][user]['x']) > 10000: - print(f"over 1000 : {user}") - continue testloaders['sup'].append( - DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=10, shuffle=True)) + DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=config.batch_size)) testloaders['qry'].append( - DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=10)) - - else: - transform = transforms.Compose([transforms.ToTensor()]) + DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=config.batch_size)) + elif data_type == 'shakespeare': for user in clients_list[0]: - trainloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10, shuffle=True)) - trainloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) + trainloaders['sup'].append( + DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, shuffle=True)) + trainloaders['qry'].append( + DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=config.batch_size)) for user in clients_list[1]: - valloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10)) - valloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) + valloaders['sup'].append( + DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, shuffle=True)) + valloaders['qry'].append( + DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=config.batch_size)) for user in clients_list[2]: - testloaders['sup'].append(DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=10)) - testloaders['qry'].append(DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=10)) + testloaders['sup'].append( + DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, shuffle=True)) + testloaders['qry'].append( + DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=config.batch_size)) return trainloaders, valloaders, testloaders diff --git a/baselines/FedMeta/FedMeta/dataset_preparation.py b/baselines/FedMeta/FedMeta/dataset_preparation.py index 579b438efcab..3f932314740d 100644 --- a/baselines/FedMeta/FedMeta/dataset_preparation.py +++ b/baselines/FedMeta/FedMeta/dataset_preparation.py @@ -47,7 +47,9 @@ def support_query_split( label: List, support_ratio: float, ): + x_train, x_test, y_train, y_test = train_test_split(data, label, train_size=support_ratio, stratify=label, random_state=42) + return x_train, x_test, y_train, y_test @@ -56,6 +58,7 @@ def split_train_validation_test_clients( train_rate: Optional[float] = 0.8, val_rate: Optional[float] = 0.1, ) -> Tuple[List[str], List[str], List[str]]: + np.random.seed(42) train_rate = int(train_rate * len(clients)) val_rate = int(val_rate * len(clients)) @@ -71,16 +74,13 @@ def split_train_validation_test_clients( def _partition_data( + data_type: str, dir_path: str, - support_ratio: Optional[float] = None, + support_ratio: float, ) -> Tuple[Dict, Dict]: - data_type = 'shakespeare' - print("_partiton_data") - # train_path = f'{dir_path}/train' - train_path = f'{dir_path}/train_0.16' - # test_path = f'{dir_path}/test' - test_path = f'{dir_path}/test_0.16' + train_path = f'{dir_path}/train' + test_path = f'{dir_path}/test' train_users, train_data, train_num = _read_dataset(train_path) test_users, test_data, test_num = _read_dataset(test_path) @@ -109,10 +109,10 @@ def _partition_data( try: sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio) except Exception as e: - print(f"Error occurred at iteration {user}: {e}") + # print(f"Error occurred at iteration {user}: {e}") continue - else: + elif data_type == 'shakespeare': sup_x, qry_x, sup_y, qry_y = train_test_split(all_x, all_y, train_size=support_ratio, random_state=42) all_dataset['users'].append(user) diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index d77996f5bb07..e88086e7ac4e 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -29,31 +29,36 @@ def main(cfg: DictConfig) -> None: print(OmegaConf.to_yaml(cfg)) # partition dataset and get dataloaders - trainloaders, valloaders, testloaders= load_datasets(config=cfg.dataset) + trainloaders, valloaders, testloaders= load_datasets(config=cfg.data, path=cfg.path) # prepare function that will be used to spawn each client client_fn = client.gen_client_fn( num_epochs=cfg.num_epochs, trainloaders=trainloaders, valloaders=valloaders, - learning_rate=cfg.learning_rate, - model=cfg.model, + learning_rate=cfg.algo[cfg.data.data].alpha, + model=cfg.data.model, + gradient_step=cfg.data.gradient_step, ) - + # prepare strategy function strategy = instantiate( cfg.strategy, evaluate_metrics_aggregation_fn=weighted_average, + alpha=cfg.algo[cfg.data.data].alpha, + beta=cfg.algo[cfg.data.data].beta, + data=cfg.data.data, + algo=cfg.algo.algo, ) - # 5. Start Simulation + # Start Simulation history = fl.simulation.start_simulation( client_fn=client_fn, num_clients=len(trainloaders['sup']), - config=fl.server.ServerConfig(num_rounds=cfg.num_rounds), + config=fl.server.ServerConfig(num_rounds=cfg.data.num_rounds), client_resources={ - "num_cpus": cfg.client_resources.num_cpus, - "num_gpus": cfg.client_resources.num_gpus, + "num_cpus": cfg.data.client_resources.num_cpus, + "num_gpus": cfg.data.client_resources.num_gpus, }, client_manager=Fedmeta_client_manager(valid_client=len(valloaders['qry'])), strategy=strategy, diff --git a/baselines/FedMeta/FedMeta/models.py b/baselines/FedMeta/FedMeta/models.py index f1b7a398a6a6..5a6a473844fb 100644 --- a/baselines/FedMeta/FedMeta/models.py +++ b/baselines/FedMeta/FedMeta/models.py @@ -30,7 +30,7 @@ def forward(self, text): return final_output -class Femnist_network(nn.Module): +class CNN_network(nn.Module): """Convolutional Neural Network architecture. As described in McMahan 2017 paper : @@ -40,7 +40,7 @@ class Femnist_network(nn.Module): """ def __init__(self) -> None: - super(Femnist_network, self).__init__() + super(CNN_network, self).__init__() self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5, padding=2) self.maxpool1 = nn.MaxPool2d(kernel_size=(2, 2)) self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2) @@ -76,6 +76,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: def train( # pylint: disable=too-many-arguments net: nn.Module, trainloader: DataLoader, + testloader: DataLoader, device: torch.device, epochs: int, learning_rate: float, @@ -97,7 +98,6 @@ def train( # pylint: disable=too-many-arguments """ criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) - # optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate) net.train() for _ in range(epochs): net, loss = _train_one_epoch( @@ -138,7 +138,6 @@ def _train_one_epoch( # pylint: disable=too-many-arguments for images, labels in trainloader: images, labels = images.to(device), labels.to(device) optimizer.zero_grad() - # loss = criterion(net(images.to(torch.float32)), labels) loss = criterion(net(images), labels) total_loss += loss.item() * labels.size(0) loss.backward() @@ -152,7 +151,9 @@ def test( trainloader: DataLoader, testloader: DataLoader, device: torch.device, - learning_rate: float + algo: str, + data: str, + learning_rate: float, ) -> Tuple[float, float]: """Evaluate the network on the entire test set. @@ -170,29 +171,35 @@ def test( Tuple[float, float] The loss and the accuracy of the input model on the given data. """ - total_loss = 0.0 criterion = torch.nn.CrossEntropyLoss() - optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) - net.train() - for images, labels in trainloader: - images, labels = images.to(device), labels.to(device) - # loss = criterion(net(images.to(torch.float32)), labels) - loss = criterion(net(images), labels) - total_loss += loss * labels.size(0) - optimizer.zero_grad() - loss.backward() - optimizer.step() - # total_loss = total_loss / len(trainloader.dataset) - # optimizer.zero_grad() - # total_loss.backward() - # optimizer.step() + + if algo == 'fedavg(meta)': + total_loss = 0.0 + optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) + net.train() + if data == 'femnist': + for images, labels in trainloader: + images, labels = images.to(device), labels.to(device) + loss = criterion(net(images), labels) + total_loss += loss * labels.size(0) + total_loss = total_loss / len(trainloader.dataset) + optimizer.zero_grad() + total_loss.backward() + optimizer.step() + + elif data == 'shakespeare': + for images, labels in trainloader: + images, labels = images.to(device), labels.to(device) + loss = criterion(net(images), labels) + optimizer.zero_grad() + loss.backward() + optimizer.step() correct, total, loss = 0, 0, 0.0 net.eval() with torch.no_grad(): for images, labels in testloader: images, labels = images.to(device), labels.to(device) - # outputs = net(images.to(torch.float32)) outputs = net(images) loss += criterion(outputs, labels).item() * labels.size(0) _, predicted = torch.max(outputs.data, 1) @@ -211,7 +218,7 @@ def train_meta( # pylint: disable=too-many-arguments queryloader: DataLoader, alpha, device: torch.device, - learning_rate: float, + gradient_step: int ) -> Tuple[float]: """Train the network on the training set. @@ -231,7 +238,7 @@ def train_meta( # pylint: disable=too-many-arguments criterion = torch.nn.CrossEntropyLoss() for _ in range(1): loss, grads = _train_meta_one_epoch( - net, supportloader, queryloader, alpha, criterion, learning_rate, device + net, supportloader, queryloader, alpha, criterion, device, gradient_step ) return loss, grads @@ -242,8 +249,8 @@ def _train_meta_one_epoch( # pylint: disable=too-many-arguments queryloader: DataLoader, alpha, criterion: torch.nn.CrossEntropyLoss, - learning_rate: float, device: torch.device, + gradient_step: int, ) -> nn.Module: """Train for one epoch. @@ -265,43 +272,33 @@ def _train_meta_one_epoch( # pylint: disable=too-many-arguments nn.Module The model that has been trained for one epoch. """ - num_adaptation_steps = 1 - all_adaptation_losses = [] + num_adaptation_steps = gradient_step train_net = deepcopy(net) - # alpha = [alpha.to(device) for alpha in alpha] - for step in range(num_adaptation_steps): + alpha = [alpha.to(device) for alpha in alpha] + for _ in range(num_adaptation_steps): loss_sum = 0.0 sup_num_sample = [] sup_total_loss = [] for images, labels in supportloader: images, labels = images.to(device), labels.to(device) - # loss = criterion(train_net(images.to(torch.float32)), labels) loss = criterion(train_net(images), labels) loss_sum += loss * labels.size(0) sup_num_sample.append(labels.size(0)) sup_total_loss.append(loss * labels.size(0)) grads = torch.autograd.grad(loss, list(train_net.parameters()), create_graph=True, retain_graph=True) - for p, g in zip(train_net.parameters(), grads): - p.data.add_(g.data, alpha=-learning_rate) + for p, g, a in zip(train_net.parameters(), grads, alpha): + p.data = p.data - a * g for p in train_net.parameters(): if p.grad is not None: p.grad.zero_() - # for p, g, a in zip(train_net.parameters(), grads, alpha): - # p.data = p.data - a * g - # - # for p in train_net.parameters(): - # if p.grad is not None: - # p.grad.zero_() - qry_total_loss = [] qry_num_sample = [] loss_sum = 0.0 for images, labels in queryloader: images, labels = images.to(device), labels.to(device) - # loss = criterion(train_net(images.to(torch.float32)), labels) loss = criterion(train_net(images), labels) loss_sum += loss * labels.size(0) qry_num_sample.append(labels.size(0)) @@ -316,15 +313,15 @@ def _train_meta_one_epoch( # pylint: disable=too-many-arguments grads = [g.cpu().numpy() for g in grads] average_adaptation_loss = sum(sup_total_loss) / sum(sup_num_sample) return average_adaptation_loss, grads - - +# +# def test_meta( net: nn.Module, supportloader: DataLoader, queryloader: DataLoader, alpha, device: torch.device, - learning_rate: float, + gradient_step: int, ) -> Tuple[float, float]: """Evaluate the network on the entire test set. @@ -344,41 +341,32 @@ def test_meta( """ criterion = torch.nn.CrossEntropyLoss() test_net = deepcopy(net) - num_adaptation_steps = 1 + num_adaptation_steps = gradient_step alpha = [alpha_tensor.to(device) for alpha_tensor in alpha] test_net.train() - for step in range(num_adaptation_steps): + for _ in range(num_adaptation_steps): loss_sum = 0.0 sup_num_sample = [] sup_total_loss = [] for images, labels in supportloader: images, labels = images.to(device), labels.to(device) - # loss = criterion(test_net(images.to(torch.float32)), labels) loss = criterion(test_net(images), labels) loss_sum += loss * labels.size(0) sup_num_sample.append(labels.size(0)) sup_total_loss.append(loss) grads = torch.autograd.grad(loss, list(test_net.parameters()), create_graph=True, retain_graph=True) - for p, g in zip(test_net.parameters(), grads): - p.data.add_(g.data, alpha=-learning_rate) + for p, g, a in zip(test_net.parameters(), grads, alpha): + p.data -= a * g for p in test_net.parameters(): if p.grad is not None: p.grad.zero_() - # for p, g, a in zip(test_net.parameters(), grads, alpha): - # p.data -= a * g - # - # for p in test_net.parameters(): - # if p.grad is not None: - # p.grad.zero_() - test_net.eval() correct, total, loss = 0, 0, 0.0 for images, labels in queryloader: images, labels = images.to(device), labels.to(device) - # outputs = test_net(images.to(torch.float32)) outputs = test_net(images) loss += criterion(outputs, labels).item() * labels.size(0) _, predicted = torch.max(outputs.data, 1) @@ -391,4 +379,3 @@ def test_meta( return loss, accuracy, total - diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index 62c6a7503d6a..a0f68bf8a008 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -15,7 +15,7 @@ import numpy as np import torch from functools import reduce -from models import Femnist_network, StackedLSTM +from models import CNN_network, StackedLSTM from flwr.common.logger import log @@ -35,12 +35,40 @@ import wandb # start a new wandb run to track this script -wandb.init( - # set the wandb project where this run will be logged - project="SoR", - -) - +# wandb.init( +# # set the wandb project where this run will be logged +# project="SoR", +# +# ) + + +def fedmeta_update_meta_sgd(net, alpha, beta, weights_results, gradients_aggregated): + params_dict = zip(net.state_dict().keys(), weights_results) + state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) + net.load_state_dict(state_dict, strict=True) + optimizer = torch.optim.Adam(list(net.parameters()) + list(alpha), lr=beta, weight_decay=0.0001) + for params, grad_ins, alphas in zip(net.parameters(), gradients_aggregated, alpha): + params.grad = torch.tensor(grad_ins).to(params.dtype) + alphas.grad = torch.tensor(grad_ins).to(params.dtype) + optimizer.step() + optimizer.zero_grad() + weights_prime = [val.cpu().numpy() for _, val in net.state_dict().items()] + + return weights_prime, alpha + + +def fedmeta_update_maml(net, beta, weights_results, gradients_aggregated): + params_dict = zip(net.state_dict().keys(), weights_results) + state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) + net.load_state_dict(state_dict, strict=True) + optimizer = torch.optim.Adam(list(net.parameters()), lr=beta, weight_decay=0.0001) + for params, grad_ins in zip(net.parameters(), gradients_aggregated): + params.grad = torch.tensor(grad_ins).to(params.dtype) + optimizer.step() + optimizer.zero_grad() + weights_prime = [val.cpu().numpy() for _, val in net.state_dict().items()] + + return weights_prime def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: """Aggregation function for weighted average during evaluation. @@ -74,34 +102,33 @@ def aggregate_grad(results: List[Tuple[NDArrays, int]]) -> NDArrays: [layer * num_examples for layer in gradients] for gradients, num_examples in results ] - # weighted_gradients = [gradients for gradients, _ in results] - # Compute average weights of each layer grdients_prime: NDArrays = [ reduce(np.add, layer_updates) / num_examples_total for layer_updates in zip(*weighted_gradients) ] - # grdients_prime: NDArrays = [ - # reduce(np.add, layer_updates) / len(weighted_gradients) - # for layer_updates in zip(*weighted_gradients) - # ] - return grdients_prime class FedMeta(FedAvg): - def __init__(self, **kwargs): + def __init__(self, alpha, beta, data, algo, **kwargs): super().__init__(**kwargs) - # self.alpha = [torch.full_like(p, 0.001) for p in Femnist_network().parameters()] - self.alpha = torch.nn.ParameterList([torch.nn.Parameter(torch.full_like(p, 0.001)) for p in Femnist_network().parameters()]) + self.algo = algo + self.data = data + if self.data == 'femnist': + self.net = CNN_network() + elif self.data == 'shakespeare': + self.net = StackedLSTM() + self.alpha = torch.nn.ParameterList([torch.nn.Parameter(torch.full_like(p, alpha)) for p in self.net.parameters()]) + self.beta = beta def configure_fit( self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, FitIns]]: """Configure the next round of training.""" # alpha_list = [param.data for param in self.alpha] - config = {"alpha" : self.alpha} + config = {"alpha" : self.alpha, "algo": self.algo, "data": self.data} if self.on_fit_config_fn is not None: # Custom fit config function provided config = self.on_fit_config_fn(server_round) @@ -129,7 +156,7 @@ def configure_evaluate( return [] # Parameters and config - config = {"alpha" : self.alpha} + config = {"alpha" : self.alpha, "algo": self.algo, "data":self.data} if self.on_evaluate_config_fn is not None: # Custom evaluation config function provided config = self.on_evaluate_config_fn(server_round) @@ -168,32 +195,26 @@ def aggregate_fit( for _, fit_res in results ] - # parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results)) + parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results)) - grads_results = [ - (fit_res.metrics['grads'], fit_res.num_examples) - for _, fit_res in results - ] - gradients_aggregated = aggregate_grad(grads_results) - - # net = Femnist_network() - net = StackedLSTM() - params_dict = zip(net.state_dict().keys(), weights_results[0][0]) - state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) - net.load_state_dict(state_dict, strict=True) - # optimizer = torch.optim.Adam(list(net.parameters())+list(self.alpha), lr=0.0001, weight_decay=0.001) - optimizer = torch.optim.Adam(list(net.parameters()), lr=0.01) - for params, grad_ins, alphas in zip(net.parameters(), gradients_aggregated, self.alpha): - params.grad = torch.tensor(grad_ins).to(params.dtype) - alphas.grad = torch.tensor(grad_ins).to(params.dtype) - optimizer.step() - optimizer.zero_grad() - weights_prime = [val.cpu().numpy() for _, val in net.state_dict().items()] - - # weight_loss = sum([fit_res.metrics['loss'] * fit_res.num_examples for _, fit_res in results]) / sum( - # [fit_res.num_examples for _, fit_res in results]) - # wandb.log({"Training Loss": weight_loss}, step=server_round) - # log(INFO, f'Training Loss : {weight_loss}') + if self.algo == 'fedmeta(maml)': + grads_results = [ + (fit_res.metrics['grads'], fit_res.num_examples) + for _, fit_res in results + ] + gradients_aggregated = aggregate_grad(grads_results) + weights_prime = fedmeta_update_maml(self.net, self.beta, weights_results[0][0], gradients_aggregated) + parameters_aggregated = ndarrays_to_parameters(weights_prime) + + elif self.algo == 'fedmeta(meta-sgd)': + grads_results = [ + (fit_res.metrics['grads'], fit_res.num_examples) + for _, fit_res in results + ] + gradients_aggregated = aggregate_grad(grads_results) + weights_prime, update_alpha = fedmeta_update_meta_sgd(self.net, self.alpha, self.beta, weights_results[0][0], gradients_aggregated) + self.alpha = update_alpha + parameters_aggregated = ndarrays_to_parameters(weights_prime) # Aggregate custom metrics if aggregation fn was provided metrics_aggregated = {} @@ -203,8 +224,7 @@ def aggregate_fit( elif server_round == 1: # Only log this warning once log(WARNING, "No fit_metrics_aggregation_fn provided") - return ndarrays_to_parameters(weights_prime), metrics_aggregated - # return parameters_aggregated, metrics_aggregated + return parameters_aggregated, metrics_aggregated def aggregate_evaluate( self, @@ -229,7 +249,7 @@ def aggregate_evaluate( weight_loss = sum([evaluate_res.metrics['loss'] * evaluate_res.num_examples for _, evaluate_res in results]) / sum( [evaluate_res.num_examples for _, evaluate_res in results]) - wandb.log({"Training Loss": weight_loss}, step=server_round) + # wandb.log({"Training Loss": weight_loss}, step=server_round) log(INFO, f'Training Loss : {weight_loss}') # Aggregate custom metrics if aggregation fn was provided @@ -237,7 +257,7 @@ def aggregate_evaluate( if self.evaluate_metrics_aggregation_fn: eval_metrics = [(res.num_examples, res.metrics) for _, res in results] metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics) - wandb.log({"Test_Accuracy ": round(metrics_aggregated['accuracy'] * 100, 3)}, step=server_round) + # wandb.log({"Test_Accuracy ": round(metrics_aggregated['accuracy'] * 100, 3)}, step=server_round) log(INFO, f'Test Accuracy : {round(metrics_aggregated["accuracy"] * 100, 3)}') elif server_round == 1: # Only log this warning once From 7a93f792904ae7c1e6782f9da856bf6c1b523c6f Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Fri, 29 Sep 2023 13:56:22 +0900 Subject: [PATCH 069/133] update ReadMe and remove wandb for experiments --- baselines/FedMeta/FedMeta/dataset.py | 2 +- .../FedMeta/FedMeta/dataset_preparation.py | 2 -- baselines/FedMeta/FedMeta/strategy.py | 17 ++-------- baselines/FedMeta/README.md | 34 ++++++++++++------- 4 files changed, 26 insertions(+), 29 deletions(-) diff --git a/baselines/FedMeta/FedMeta/dataset.py b/baselines/FedMeta/FedMeta/dataset.py index 434e70e1d0f7..a34c68689b28 100644 --- a/baselines/FedMeta/FedMeta/dataset.py +++ b/baselines/FedMeta/FedMeta/dataset.py @@ -11,7 +11,7 @@ from torch.utils.data import DataLoader, Dataset from omegaconf import DictConfig -from typing import Optional, Tuple +from typing import Tuple from dataset_preparation import _partition_data, split_train_validation_test_clients import numpy as np import torchvision.transforms as transforms diff --git a/baselines/FedMeta/FedMeta/dataset_preparation.py b/baselines/FedMeta/FedMeta/dataset_preparation.py index 3f932314740d..aac392c2d872 100644 --- a/baselines/FedMeta/FedMeta/dataset_preparation.py +++ b/baselines/FedMeta/FedMeta/dataset_preparation.py @@ -14,7 +14,6 @@ from sklearn.model_selection import train_test_split -# def _read_dataset() -> Dict[List, Dict, List]: def _read_dataset( path: str ) -> Tuple[List, DefaultDict]: @@ -109,7 +108,6 @@ def _partition_data( try: sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio) except Exception as e: - # print(f"Error occurred at iteration {user}: {e}") continue elif data_type == 'shakespeare': diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index a0f68bf8a008..4c33b8cb1f09 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -32,15 +32,6 @@ NDArrays, ) -import wandb - -# start a new wandb run to track this script -# wandb.init( -# # set the wandb project where this run will be logged -# project="SoR", -# -# ) - def fedmeta_update_meta_sgd(net, alpha, beta, weights_results, gradients_aggregated): params_dict = zip(net.state_dict().keys(), weights_results) @@ -70,6 +61,7 @@ def fedmeta_update_maml(net, beta, weights_results, gradients_aggregated): return weights_prime + def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: """Aggregation function for weighted average during evaluation. @@ -127,7 +119,6 @@ def configure_fit( self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, FitIns]]: """Configure the next round of training.""" - # alpha_list = [param.data for param in self.alpha] config = {"alpha" : self.alpha, "algo": self.algo, "data": self.data} if self.on_fit_config_fn is not None: # Custom fit config function provided @@ -249,16 +240,14 @@ def aggregate_evaluate( weight_loss = sum([evaluate_res.metrics['loss'] * evaluate_res.num_examples for _, evaluate_res in results]) / sum( [evaluate_res.num_examples for _, evaluate_res in results]) - # wandb.log({"Training Loss": weight_loss}, step=server_round) - log(INFO, f'Training Loss : {weight_loss}') + log(INFO, f'Loss : {weight_loss}') # Aggregate custom metrics if aggregation fn was provided metrics_aggregated = {} if self.evaluate_metrics_aggregation_fn: eval_metrics = [(res.num_examples, res.metrics) for _, res in results] metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics) - # wandb.log({"Test_Accuracy ": round(metrics_aggregated['accuracy'] * 100, 3)}, step=server_round) - log(INFO, f'Test Accuracy : {round(metrics_aggregated["accuracy"] * 100, 3)}') + log(INFO, f'Accuracy : {round(metrics_aggregated["accuracy"] * 100, 3)}') elif server_round == 1: # Only log this warning once log(WARNING, "No evaluate_metrics_aggregation_fn provided") diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index d4cfccb47ec6..1ac6441bde24 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -73,25 +73,35 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline ./preprocess.sh -s niid --iu 1068 --sf 0.3 -k 0 -t sample #SHAKESEPEARE dataset Download command for these experiments -./preprocess.sh -s niid --iu 1068 --sf 0.3 -k 0 -t sample +./preprocess.sh -s niid --sf 0.16 -k 0 -t sample ```` - +****Start experiments**** : ```bash -# The main experiment implemented in your baseline using default hyperparameters (that should be setup in the Hydra configs) should run (including dataset download and necessary partitioning) by executing the command: +# FedAvg + Femnist Dataset +python main.py algo=fedavg data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data + +# FedAvg(Meta) + Femnist Dataset +python main.py algo=fedavg_meta data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data + +# FedMeta(MAML) + Femnist Dataset +python main.py algo=fedmeta_maml data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data + +# FedMeta(Meta-SGD) + Femnist Dataset +python main.py algo=fedmeta_meta_sgd data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data + -poetry run -m .main # where is the name of this directory and that of the only sub-directory in this directory (i.e. where all your source code is) -# If you are using a dataset that requires a complicated download (i.e. not using one natively supported by TF/PyTorch) + preprocessing logic, you might want to tell people to run one script first that will do all that. Please ensure the download + preprocessing can be configured to suit (at least!) a different download directory (and use as default the current directory). The expected command to run to do this is: +#FedAvg + Shakespeare Dataset +python main.py algo=fedavg data=shakespeare path=(your leaf dataset path)/leaf/data/shakespeare/data -poetry run -m .dataset_preparation +#FedAvg(Meta) + Shakespeare Dataset +python main.py algo=fedavg_meta data=shakespeare path=(your leaf dataset path)/leaf/data/shakespeare/data -# It is expected that you baseline supports more than one dataset and different FL settings (e.g. different number of clients, dataset partitioning methods, etc). Please provide a list of commands showing how these experiments are run. Include also a short explanation of what each one does. Here it is expected you'll be using the Hydra syntax to override the default config. +#FedMeta(MAML) + Shakespeare Dataset +python main.py algo=fedmeta_maml data=shakespeare path=(your leaf dataset path)/leaf/data/shakespeare/data -poetry run -m .main -. -. -. -poetry run -m .main +#FedMeta(Meta-SGD) + Shakespeare Dataset +python main.py algo=fedmeta_meta_sgd data=shakespeare path=(your leaf dataset path)/leaf/data/shakespeare/data ``` From 5e63db7329707e24844d47b1fc20d2db396313a3 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 09:07:07 +0900 Subject: [PATCH 070/133] Update Fedmeta --- .../FedMeta/FedMeta/Fedmeta_client_manager.py | 28 ++- baselines/FedMeta/FedMeta/client.py | 46 +++-- .../FedMeta/conf/algo/fedavg_meta.yaml | 2 +- .../FedMeta/conf/algo/fedmeta_maml.yaml | 2 +- .../FedMeta/conf/algo/fedmeta_meta_sgd.yaml | 2 +- baselines/FedMeta/FedMeta/dataset.py | 58 ++++-- .../FedMeta/FedMeta/dataset_preparation.py | 74 +++++++- baselines/FedMeta/FedMeta/main.py | 27 ++- baselines/FedMeta/FedMeta/models.py | 166 +++++++++++++----- .../shakespeare/graph_params/result_graph.png | Bin 0 -> 117804 bytes baselines/FedMeta/FedMeta/strategy.py | 142 +++++++++++---- baselines/FedMeta/FedMeta/utils.py | 160 +++++++++++++---- baselines/FedMeta/README.md | 15 +- 13 files changed, 563 insertions(+), 159 deletions(-) create mode 100644 baselines/FedMeta/FedMeta/shakespeare/graph_params/result_graph.png diff --git a/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py index ca27eac8ed16..2b472e13a9e7 100644 --- a/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py +++ b/baselines/FedMeta/FedMeta/Fedmeta_client_manager.py @@ -12,11 +12,35 @@ def __init__(self, min_evaluate_clients): self.min_evaluate_clients = min_evaluate_clients """Criterion to select evaluate clients.""" - def select(self, valid_client: int) -> bool: + def select( + self, + valid_client: int + ) -> List: + """ + Clients to be used in evaluation should be sampled from the validation client list. + + Parameters + ---------- + valid_client : int + Length of validation client list + + Returns + ------- + Return client cid list + + """ return [str(result) for result in range(0, valid_client)] class Fedmeta_client_manager(SimpleClientManager): + + """ + In the fit phase, clients must be sampled from the training client list. + And in the evaluate stage, clients must be sampled from the validation client list. + So we modify 'Fedmeta_client_manager' to sample clients from [cid: List] for each list. + + """ + def __init__(self, valid_client, **kwargs): super().__init__(**kwargs) self.valid_client = valid_client @@ -29,10 +53,12 @@ def sample( criterion: Optional[Criterion] = None, ) -> List[ClientProxy]: """Sample a number of Flower ClientProxy instances.""" + # Block until at least num_clients are connected. if min_num_clients is None: min_num_clients = num_clients self.wait_for(min_num_clients) + # Sample clients which meet the criterion available_cids = list(self.clients) if criterion is not None: diff --git a/baselines/FedMeta/FedMeta/client.py b/baselines/FedMeta/FedMeta/client.py index 5cd045bb908b..72e6e672a306 100644 --- a/baselines/FedMeta/FedMeta/client.py +++ b/baselines/FedMeta/FedMeta/client.py @@ -15,6 +15,7 @@ from models import train, test, train_meta, test_meta + class FlowerClient( fl.client.NumPyClient ): @@ -54,8 +55,12 @@ def fit( """Implements distributed fit function for a given client.""" self.set_parameters(parameters) algo = config["algo"] + + # Total number of data for Weighted Avg and Grad total_len = len(self.trainloaders['qry'][self.cid].dataset) + len(self.trainloaders['sup'][self.cid].dataset) - if algo == 'fedavg' or algo == 'fedavg(meta)': + + # FedAvg & FedAvg(Meta) train basic Learning + if algo == 'fedavg' or algo == 'fedavg_meta': loss = train( self.net, self.trainloaders['sup'][self.cid], @@ -64,9 +69,10 @@ def fit( epochs=self.num_epochs, learning_rate=self.learning_rate ) - return self.get_parameters({}), total_len, {"loss" : loss} + return self.get_parameters({}), total_len, {"loss": loss} - elif algo == 'fedmeta(maml)' or algo == 'fedmeta(meta-sgd)': + # FedMeta(MAML) & FedMeta(Meta-SGD) train inner and outer loop + elif algo == 'fedmeta_maml' or algo == 'fedmeta_meta_sgd': alpha = config["alpha"] loss, grads = train_meta( self.net, @@ -83,22 +89,27 @@ def evaluate( ) -> Tuple[float, int, Dict]: """Implements distributed evaluation for a given client.""" self.set_parameters(parameters) + + # Total number of data for Weighted Avg and Grad total_len = len(self.valloaders['qry'][self.cid].dataset) + len(self.valloaders['sup'][self.cid].dataset) - if config["algo"] == 'fedavg' or config["algo"] == 'fedavg(meta)': - loss, accuracy, total = test( - self.net, - self.valloaders['sup'][self.cid], - self.valloaders['qry'][self.cid], - self.device, - config["algo"], - config["data"], - learning_rate=self.learning_rate, + + # FedAvg & FedAvg(Meta) train basic Learning + if config["algo"] == 'fedavg' or config["algo"] == 'fedavg_meta': + loss, accuracy = test( + self.net, + self.valloaders['sup'][self.cid], + self.valloaders['qry'][self.cid], + self.device, + config["algo"], + config["data"], + learning_rate=self.learning_rate, ) return float(loss), total_len, {"correct": accuracy, "loss": loss} - elif config["algo"] == 'fedmeta(maml)' or config["algo"] == 'fedmeta(meta-sgd)': + # FedMeta(MAML) & FedMeta(Meta-SGD) train inner and outer loop + elif config["algo"] == 'fedmeta_maml' or config["algo"] == 'fedmeta_meta_sgd': alpha = config["alpha"] - loss, accuracy, total = test_meta( + loss, accuracy = test_meta( self.net, self.valloaders['sup'][self.cid], self.valloaders['qry'][self.cid], @@ -116,7 +127,7 @@ def gen_client_fn( learning_rate: float, model: DictConfig, gradient_step: int, -) -> Callable[[str], FlowerClient]: # pylint: disable=too-many-arguments +) -> Callable[[str], FlowerClient]: """Generates the client function that creates the Flower Clients. @@ -131,8 +142,13 @@ def gen_client_fn( valloaders: List[DataLoader] A list of DataLoaders, each pointing to the dataset validation partition belonging to a particular client. + model: DictConfig + The global Model for Federated Learning. learning_rate : float The learning rate for the SGD optimizer of clients. + gradient_step : int + The gradient step for Meta Learning of clients. + FedAvg and FedAvg(Meta) is None Returns ------- diff --git a/baselines/FedMeta/FedMeta/conf/algo/fedavg_meta.yaml b/baselines/FedMeta/FedMeta/conf/algo/fedavg_meta.yaml index 01bcb0d8f218..928fd0a96cb9 100644 --- a/baselines/FedMeta/FedMeta/conf/algo/fedavg_meta.yaml +++ b/baselines/FedMeta/FedMeta/conf/algo/fedavg_meta.yaml @@ -3,7 +3,7 @@ # Please follow the provided structure (this will ensuring all baseline follow # a similar configuration structure and hence be easy to customise) -algo: fedavg(meta) +algo: fedavg_meta femnist: alpha: 0.0001 beta: None diff --git a/baselines/FedMeta/FedMeta/conf/algo/fedmeta_maml.yaml b/baselines/FedMeta/FedMeta/conf/algo/fedmeta_maml.yaml index 1f6d0586bdad..2c3c86df4edb 100644 --- a/baselines/FedMeta/FedMeta/conf/algo/fedmeta_maml.yaml +++ b/baselines/FedMeta/FedMeta/conf/algo/fedmeta_maml.yaml @@ -3,7 +3,7 @@ # Please follow the provided structure (this will ensuring all baseline follow # a similar configuration structure and hence be easy to customise) -algo: fedmeta(maml) +algo: fedmeta_maml femnist: alpha: 0.001 beta: 0.0001 diff --git a/baselines/FedMeta/FedMeta/conf/algo/fedmeta_meta_sgd.yaml b/baselines/FedMeta/FedMeta/conf/algo/fedmeta_meta_sgd.yaml index ebb53dd602a2..cb72f1fe6e2c 100644 --- a/baselines/FedMeta/FedMeta/conf/algo/fedmeta_meta_sgd.yaml +++ b/baselines/FedMeta/FedMeta/conf/algo/fedmeta_meta_sgd.yaml @@ -3,7 +3,7 @@ # Please follow the provided structure (this will ensuring all baseline follow # a similar configuration structure and hence be easy to customise) -algo: fedmeta(meta-sgd) +algo: fedmeta_meta_sgd femnist: alpha: 0.001 beta: 0.0001 diff --git a/baselines/FedMeta/FedMeta/dataset.py b/baselines/FedMeta/FedMeta/dataset.py index a34c68689b28..08b6de853742 100644 --- a/baselines/FedMeta/FedMeta/dataset.py +++ b/baselines/FedMeta/FedMeta/dataset.py @@ -21,6 +21,16 @@ class ShakespeareDataset(Dataset): def __init__(self, data): + """ + [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf) + + We imported the preprocessing method for the Shakespeare dataset from GitHub. + + word_to_indices : returns a list of character indices + sentences_to_indices : returns one-hot vector with given size and value 1 at given index + letter_to_vec : returns one-hot representation of given letter + + """ sentence, label = data['x'], data['y'] sentences_to_indices = [word_to_indices(word) for word in sentence] sentences_to_indices = np.array(sentences_to_indices) @@ -37,6 +47,10 @@ def __getitem__(self, index): class FemnistDataset(Dataset): def __init__(self, dataset, transform): + """ + Using FemnistDataset for CNN_network() + + """ self.x = dataset['x'] self.y = dataset['y'] self.transform = transform @@ -52,10 +66,32 @@ def __len__(self): return len(self.y) -def load_datasets( # pylint: disable=too-many-arguments - config: DictConfig, - path: str, +def load_datasets( + config: DictConfig, + path: str, ) -> Tuple[DataLoader, DataLoader, DataLoader]: + """ + Creates the dataloaders to be fed into the model. + + Parameters + ---------- + config: DictConfig + Parameterises the dataset partitioning process + batch_size : int + The size of the batches to be fed into the model, + by default 10 + support_ratio : float + The ratio of Support set for each client.(between 0 and 1) + by default 0.2 + path : str + The path where the leaf dataset was downloaded + + Returns + ------- + Tuple[DataLoader, DataLoader, DataLoader] + The DataLoader for training, the DataLoader for validation, the DataLoader for testing. + + """ dataset = _partition_data( data_type=config.data, @@ -63,6 +99,7 @@ def load_datasets( # pylint: disable=too-many-arguments support_ratio=config.support_ratio ) + # Client list : 0.8, 0.1, 0.1 clients_list = split_train_validation_test_clients( dataset[0]['users'] ) @@ -76,7 +113,8 @@ def load_datasets( # pylint: disable=too-many-arguments transform = transforms.Compose([transforms.ToTensor()]) for user in clients_list[0]: trainloaders['sup'].append( - DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=config.batch_size, shuffle=True)) + DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=config.batch_size, + shuffle=True)) trainloaders['qry'].append( DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=config.batch_size)) for user in clients_list[1]: @@ -93,21 +131,21 @@ def load_datasets( # pylint: disable=too-many-arguments elif data_type == 'shakespeare': for user in clients_list[0]: trainloaders['sup'].append( - DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, shuffle=True)) + DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, + shuffle=True)) trainloaders['qry'].append( DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=config.batch_size)) for user in clients_list[1]: valloaders['sup'].append( - DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, shuffle=True)) + DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, + shuffle=True)) valloaders['qry'].append( DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=config.batch_size)) for user in clients_list[2]: testloaders['sup'].append( - DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, shuffle=True)) + DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, + shuffle=True)) testloaders['qry'].append( DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=config.batch_size)) return trainloaders, valloaders, testloaders - - - diff --git a/baselines/FedMeta/FedMeta/dataset_preparation.py b/baselines/FedMeta/FedMeta/dataset_preparation.py index aac392c2d872..ac0c642a1dac 100644 --- a/baselines/FedMeta/FedMeta/dataset_preparation.py +++ b/baselines/FedMeta/FedMeta/dataset_preparation.py @@ -16,13 +16,20 @@ def _read_dataset( path: str -) -> Tuple[List, DefaultDict]: - """Read (if necessary) and returns the leaf dataset. +) -> Tuple[List, DefaultDict, List]: + """ + Read (if necessary) and returns the leaf dataset. + + Parameters + ---------- + path : str + The path where the leaf dataset was downloaded Returns ------- - Tuple[user, data[x,y]] - The dataset for training and the dataset for testing Femnist. + Tuple[user, data[x,y], num_total_data] + The dataset for training and the dataset for testing. + """ users = [] data = defaultdict(lambda: None) @@ -45,7 +52,26 @@ def support_query_split( data: DefaultDict, label: List, support_ratio: float, -): +) -> Tuple[List, List, List, List]: + """ + Separate support set and query set + + Parameters + ---------- + data: DefaultDict, + Raw all Datasets + label: List, + Raw all Labels + support_ratio : float + The ratio of Support set for each client.(between 0 and 1) + by default 0.2 + + Returns + ------- + Tuple[List, List, List, List] + Support set and query set classification of data and labels + + """ x_train, x_test, y_train, y_test = train_test_split(data, label, train_size=support_ratio, stratify=label, random_state=42) @@ -57,7 +83,26 @@ def split_train_validation_test_clients( train_rate: Optional[float] = 0.8, val_rate: Optional[float] = 0.1, ) -> Tuple[List[str], List[str], List[str]]: + """ + Classification of all clients into train clients, valid clients, and test clients + + Parameters + ---------- + clients: List, + Full list of clients for the sampled leaf dataset. + train_rate: float, optional + The ratio of training clients to total clients + by default 0.8 + val_rate: float, optional + The ratio of validation clients to total clients + by default 0.1 + + Returns + ------- + Tuple[List, List, List] + List of each train client, valid client, and test client + """ np.random.seed(42) train_rate = int(train_rate * len(clients)) val_rate = int(val_rate * len(clients)) @@ -77,7 +122,25 @@ def _partition_data( dir_path: str, support_ratio: float, ) -> Tuple[Dict, Dict]: + """ + Classification of support sets and query sets by client + + Parameters + ---------- + data_type: str, + The type of femnist for classification or shakespeare for regression + dir_path: str, + The path where the leaf dataset was downloaded + support_ratio: float, + The ratio of Support set for each client.(between 0 and 1) + by default 0.2 + Returns + ------- + Tuple[Dict, Dict] + Return support set and query set for total data + + """ train_path = f'{dir_path}/train' test_path = f'{dir_path}/test' @@ -105,6 +168,7 @@ def _partition_data( all_x = all_x[mask] all_y = all_y[mask] + # Client filtering for support set and query set classification try: sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio) except Exception as e: diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index e88086e7ac4e..58ba5d8a4185 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -3,17 +3,18 @@ It includes processioning the dataset, instantiate strategy, specify how the global model is going to be evaluated, etc. At the end, this script saves the results. """ -# these are the basic packages you'll need here -# feel free to remove some if aren't needed + import hydra +from hydra.core.hydra_config import HydraConfig from omegaconf import DictConfig, OmegaConf from hydra.utils import instantiate from strategy import weighted_average from dataset import load_datasets from Fedmeta_client_manager import Fedmeta_client_manager - +import os import flwr as fl import client +from utils import save_graph_params, plot_from_pkl @hydra.main(config_path="conf", config_name="config", version_base=None) @@ -24,6 +25,10 @@ def main(cfg: DictConfig) -> None: ---------- cfg : DictConfig An omegaconf object that stores the hydra config. + + algo : FedAvg, FedAvg(Meta), FedMeta(MAML), FedMeta(Meta-SGD) + data : Femnist, Shakespeare + """ # print config structured as YAML print(OmegaConf.to_yaml(cfg)) @@ -74,6 +79,22 @@ def main(cfg: DictConfig) -> None: # can retrieve the path to that directory with this: # save_path = HydraConfig.get().runtime.output_dir + print("................") + print(history) + output_path = HydraConfig.get().runtime.cwd + '/' + cfg.data.data + '/graph_params' + os.makedirs(output_path, exist_ok=True) + + data_params = { + "algo": cfg.algo.algo, + "data": cfg.data.data, + "loss": history.losses_distributed, + "accuracy": history.metrics_distributed, + "path": output_path + } + + save_graph_params(data_params) + plot_from_pkl(directory=f"./{cfg.data.data}/graph_params") + print("................") if __name__ == "__main__": main() diff --git a/baselines/FedMeta/FedMeta/models.py b/baselines/FedMeta/FedMeta/models.py index 5a6a473844fb..51f28e1f7e52 100644 --- a/baselines/FedMeta/FedMeta/models.py +++ b/baselines/FedMeta/FedMeta/models.py @@ -6,7 +6,7 @@ the python code at all """ -from typing import Tuple +from typing import Tuple, List import torch import torch.nn as nn @@ -15,6 +15,15 @@ class StackedLSTM(nn.Module): + """ + StackedLSTM architecture. + + As described in Fei Chen 2018 paper : + + [FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication] + (https://arxiv.org/abs/1802.07876) + + """ def __init__(self): super(StackedLSTM, self).__init__() @@ -23,6 +32,20 @@ def __init__(self): self.fc = nn.Linear(256, 80) def forward(self, text): + """ + Forward pass of the StackedLSTM. + + Parameters + ---------- + text : torch.Tensor + Input Tensor that will pass through the network + + Returns + ------- + torch.Tensor + The resulting Tensor after it has passed through the network + + """ embedded = self.embedding(text) self.lstm.flatten_parameters() lstm_out, _ = self.lstm(embedded) @@ -31,12 +54,14 @@ def forward(self, text): class CNN_network(nn.Module): - """Convolutional Neural Network architecture. + """ + Convolutional Neural Network architecture. + + As described in Fei Chen 2018 paper : - As described in McMahan 2017 paper : + [FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication] + (https://arxiv.org/abs/1802.07876) - [Communication-Efficient Learning of Deep Networks from - Decentralized Data] (https://arxiv.org/pdf/1602.05629.pdf) """ def __init__(self) -> None: @@ -48,20 +73,19 @@ def __init__(self) -> None: self.linear1 = nn.Linear(7 * 7 * 64, 2048) self.linear2 = nn.Linear(2048, 62) - - def forward(self, x: torch.Tensor) -> torch.Tensor: """Forward pass of the CNN. Parameters ---------- - input_tensor : torch.Tensor + x : torch.Tensor Input Tensor that will pass through the network Returns ------- torch.Tensor The resulting Tensor after it has passed through the network + """ x = torch.relu(self.conv1(x)) x = self.maxpool1(x) @@ -73,7 +97,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return x -def train( # pylint: disable=too-many-arguments +def train( net: nn.Module, trainloader: DataLoader, testloader: DataLoader, @@ -81,7 +105,8 @@ def train( # pylint: disable=too-many-arguments epochs: int, learning_rate: float, ) -> Tuple[float]: - """Train the network on the training set. + """ + Train the network on the training set. Parameters ---------- @@ -89,12 +114,21 @@ def train( # pylint: disable=too-many-arguments The neural network to train. trainloader : DataLoader The DataLoader containing the data to train the network on. + testloader : DataLoader + The DataLoader containing the data to test the network on. device : torch.device The device on which the model should be trained, either 'cpu' or 'cuda'. epochs : int The number of epochs the model should be trained for. learning_rate : float - The learning rate for the SGD optimizer. + The learning rate for the optimizer. + + Returns + ------- + nn.Module + The model that has been trained for one epoch. + loss + The Loss that bas been trained for one epoch """ criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) @@ -106,7 +140,7 @@ def train( # pylint: disable=too-many-arguments return loss -def _train_one_epoch( # pylint: disable=too-many-arguments +def _train_one_epoch( net: nn.Module, trainloader: DataLoader, device: torch.device, @@ -132,6 +166,9 @@ def _train_one_epoch( # pylint: disable=too-many-arguments ------- nn.Module The model that has been trained for one epoch. + total_loss + The Loss that has been trained for one epoch. + """ total_loss = 0.0 @@ -153,27 +190,37 @@ def test( device: torch.device, algo: str, data: str, - learning_rate: float, + learning_rate: float, ) -> Tuple[float, float]: - """Evaluate the network on the entire test set. + """ + Evaluate the network on the entire test set. Parameters ---------- net : nn.Module The neural network to test. + trainloader: DataLoader, + The DataLoader containing the data to train the network on. testloader : DataLoader The DataLoader containing the data to test the network on. device : torch.device The device on which the model should be tested, either 'cpu' or 'cuda'. + algo: str + The Algorithm of Federated Learning + data: str + The training data type of Federated Learning + learning_rate: float + The learning rate for the optimizer. Returns ------- Tuple[float, float] The loss and the accuracy of the input model on the given data. + """ criterion = torch.nn.CrossEntropyLoss() - if algo == 'fedavg(meta)': + if algo == 'fedavg_meta': total_loss = 0.0 optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) net.train() @@ -209,31 +256,43 @@ def test( raise ValueError("Testloader can't be 0, exiting...") loss /= len(testloader.dataset) accuracy = correct / total - return loss, accuracy, total -# -# -def train_meta( # pylint: disable=too-many-arguments + return loss, accuracy + + +def train_meta( net: nn.Module, supportloader: DataLoader, queryloader: DataLoader, alpha, device: torch.device, - gradient_step: int -) -> Tuple[float]: + gradient_step: int, +) -> Tuple[float, List]: """Train the network on the training set. Parameters ---------- net : nn.Module The neural network to train. - trainloader : DataLoader - The DataLoader containing the data to train the network on. + supportloader : DataLoader + The DataLoader containing the data to inner loop train the network on. + queryloader : DataLoader + The DataLoader containing the data to outer loop train the network on. + alpha : int + The learning rate for the optimizer. device : torch.device The device on which the model should be trained, either 'cpu' or 'cuda'. - epochs : int - The number of epochs the model should be trained for. - learning_rate : float - The learning rate for the SGD optimizer. + gradient_step : int + The number of inner loop learning + + Returns + ------- + nn.Module + The model that has been trained for one meta epoch. + total_loss + The Loss that has been trained for one epoch. + grads + The gradients that has been trained for one epoch. + """ criterion = torch.nn.CrossEntropyLoss() for _ in range(1): @@ -243,34 +302,44 @@ def train_meta( # pylint: disable=too-many-arguments return loss, grads -def _train_meta_one_epoch( # pylint: disable=too-many-arguments +def _train_meta_one_epoch( net: nn.Module, supportloader: DataLoader, queryloader: DataLoader, - alpha, + alpha: torch.nn.ParameterList, criterion: torch.nn.CrossEntropyLoss, device: torch.device, gradient_step: int, ) -> nn.Module: - """Train for one epoch. + """ + Train for one epoch. Parameters ---------- net : nn.Module The neural network to train. - trainloader : DataLoader - The DataLoader containing the data to train the network on. - device : torch.device - The device on which the model should be trained, either 'cpu' or 'cuda'. + supportloader : DataLoader + The DataLoader containing the data to inner loop train the network on. + queryloader : DataLoader + The DataLoader containing the data to outer loop train the network on. + alpha : torch.nn.ParameterList + The learning rate for the optimizer. criterion : torch.nn.CrossEntropyLoss The loss function to use for training - optimizer : torch.optim.Adam - The optimizer to use for training + device : torch.device + The device on which the model should be trained, either 'cpu' or 'cuda'. + gradient_step : int + The number of inner loop learning Returns ------- nn.Module - The model that has been trained for one epoch. + The model that has been trained for one meta epoch. + total_loss + The Loss that has been trained for one epoch. + grads + The gradients that has been trained for one epoch. + """ num_adaptation_steps = gradient_step train_net = deepcopy(net) @@ -311,28 +380,35 @@ def _train_meta_one_epoch( # pylint: disable=too-many-arguments p.grad.zero_() grads = [g.cpu().numpy() for g in grads] - average_adaptation_loss = sum(sup_total_loss) / sum(sup_num_sample) - return average_adaptation_loss, grads -# -# + loss = sum(sup_total_loss) / sum(sup_num_sample) + return loss, grads + + def test_meta( net: nn.Module, supportloader: DataLoader, queryloader: DataLoader, - alpha, + alpha: torch.nn.ParameterList, device: torch.device, gradient_step: int, ) -> Tuple[float, float]: - """Evaluate the network on the entire test set. + """ + Evaluate the network on the entire test set. 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z0%Mm1_OPo-?LPKy?FTbc2HngB4BMA(Mq*yP*MBqW1{fbmwCwB-9A}sj4`}+;5|}TK pnoS$nE}yO>VVifa|HpTew&Lqs^{;;w+ezWI)!xyr@Kdk2zX3k?W^(`l literal 0 HcmV?d00001 diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index 4c33b8cb1f09..208b822fde41 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -12,10 +12,9 @@ from flwr.server.strategy.aggregate import aggregate, weighted_loss_avg from flwr.server.client_manager import ClientManager from Fedmeta_client_manager import evaluate_client_Criterion -import numpy as np import torch -from functools import reduce from models import CNN_network, StackedLSTM +from utils import update_ema from flwr.common.logger import log @@ -31,13 +30,53 @@ EvaluateIns, NDArrays, ) +import wandb +wandb.init( + # set the wandb project where this run will be logged + project="SoR", -def fedmeta_update_meta_sgd(net, alpha, beta, weights_results, gradients_aggregated): +) + + +def fedmeta_update_meta_sgd( + net : torch.nn.Module, + alpha : torch.nn.ParameterList, + beta : float, + weights_results : List[Tuple[NDArrays, int]], + gradients_aggregated : List[Tuple[NDArrays, int]], + WD : float, +) -> Tuple[List[Tuple[NDArrays, int]], torch.nn.ParameterList]: + """ + Update model parameters for FedMeta(Meta-SGD). + + Parameters + ---------- + net : torch.nn.Module + The list of metrics to aggregate. + alpha : torch.nn.ParameterList + alpha is the learning rate. it is updated with parameters in FedMeta (Meta-SGD). + beta : float + beta is the learning rate for updating parameters and alpha on the server. + weights_results : List[Tuple[NDArrays, int]] + These are the global model parameters for the current round. + gradients_aggregated : List[Tuple[NDArrays, int]] + Weighted average of the gradient in the current round. + WD : float + The weight decay for Adam optimizer + + Returns + ------- + weights_prime : List[Tuple[NDArrays, int]] + These are updated parameters. + alpha : torch.nn.ParameterLis + These are updated alpha. + + """ params_dict = zip(net.state_dict().keys(), weights_results) state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) net.load_state_dict(state_dict, strict=True) - optimizer = torch.optim.Adam(list(net.parameters()) + list(alpha), lr=beta, weight_decay=0.0001) + optimizer = torch.optim.Adam(list(net.parameters()) + list(alpha), lr=beta, weight_decay=WD) for params, grad_ins, alphas in zip(net.parameters(), gradients_aggregated, alpha): params.grad = torch.tensor(grad_ins).to(params.dtype) alphas.grad = torch.tensor(grad_ins).to(params.dtype) @@ -48,11 +87,39 @@ def fedmeta_update_meta_sgd(net, alpha, beta, weights_results, gradients_aggrega return weights_prime, alpha -def fedmeta_update_maml(net, beta, weights_results, gradients_aggregated): +def fedmeta_update_maml( + net : torch.nn.Module, + beta : float, + weights_results : List[Tuple[NDArrays, int]], + gradients_aggregated : List[Tuple[NDArrays, int]], + WD : float +) -> List[Tuple[NDArrays, int]]: + """ + Update model parameters for FedMeta(Meta-SGD). + + Parameters + ---------- + net : torch.nn.Module + The list of metrics to aggregate. + beta : float + beta is the learning rate for updating parameters on the server. + weights_results : List[Tuple[NDArrays, int]] + These are the global model parameters for the current round. + gradients_aggregated : List[Tuple[NDArrays, int]] + Weighted average of the gradient in the current round. + WD : float + The weight decay for Adam optimizer + + Returns + ------- + weights_prime : List[Tuple[NDArrays, int]] + These are updated parameters. + + """ params_dict = zip(net.state_dict().keys(), weights_results) state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) net.load_state_dict(state_dict, strict=True) - optimizer = torch.optim.Adam(list(net.parameters()), lr=beta, weight_decay=0.0001) + optimizer = torch.optim.Adam(list(net.parameters()), lr=beta, weight_decay=WD) for params, grad_ins in zip(net.parameters(), gradients_aggregated): params.grad = torch.tensor(grad_ins).to(params.dtype) optimizer.step() @@ -77,43 +144,32 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: """ # Multiply accuracy of each client by number of examples used correct = [num_examples * m["correct"] for num_examples, m in metrics] - # correct = [m["correct"] for _, m in metrics] examples = [num_examples for num_examples, _ in metrics] # Aggregate and return custom metric (weighted average) return {"accuracy": sum(correct) / sum(examples)} -def aggregate_grad(results: List[Tuple[NDArrays, int]]) -> NDArrays: - """Compute gradients average.""" - # Calculate the total number of examples used during training - num_examples_total = sum([num_examples for _, num_examples in results]) - - # Create a list of weights, each multiplied by the related number of examples - weighted_gradients = [ - [layer * num_examples for layer in gradients] for gradients, num_examples in results - ] - - # Compute average weights of each layer - grdients_prime: NDArrays = [ - reduce(np.add, layer_updates) / num_examples_total - for layer_updates in zip(*weighted_gradients) - ] - - return grdients_prime - - class FedMeta(FedAvg): + """ + FedMeta + The average of the gradient and the parameter update on the server through it. + """ + def __init__(self, alpha, beta, data, algo, **kwargs): super().__init__(**kwargs) self.algo = algo self.data = data + self.beta = beta + self.ema_loss = None + self.ema_acc = None + if self.data == 'femnist': self.net = CNN_network() elif self.data == 'shakespeare': self.net = StackedLSTM() + self.alpha = torch.nn.ParameterList([torch.nn.Parameter(torch.full_like(p, alpha)) for p in self.net.parameters()]) - self.beta = beta def configure_fit( self, server_round: int, parameters: Parameters, client_manager: ClientManager @@ -187,23 +243,29 @@ def aggregate_fit( ] parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results)) + if self.data == 'femnist': + WD = 0.001 + else: + WD = 0.0001 - if self.algo == 'fedmeta(maml)': + # Gradient Average and Update Parameter for FedMeta(MAML) + if self.algo == 'fedmeta_maml': grads_results = [ (fit_res.metrics['grads'], fit_res.num_examples) for _, fit_res in results ] - gradients_aggregated = aggregate_grad(grads_results) - weights_prime = fedmeta_update_maml(self.net, self.beta, weights_results[0][0], gradients_aggregated) + gradients_aggregated = aggregate(grads_results) + weights_prime = fedmeta_update_maml(self.net, self.beta, weights_results[0][0], gradients_aggregated, WD) parameters_aggregated = ndarrays_to_parameters(weights_prime) - elif self.algo == 'fedmeta(meta-sgd)': + # Gradient Average and Update Parameter for FedMeta(Meta-SGD) + elif self.algo == 'fedmeta_meta_sgd': grads_results = [ (fit_res.metrics['grads'], fit_res.num_examples) for _, fit_res in results ] - gradients_aggregated = aggregate_grad(grads_results) - weights_prime, update_alpha = fedmeta_update_meta_sgd(self.net, self.alpha, self.beta, weights_results[0][0], gradients_aggregated) + gradients_aggregated = aggregate(grads_results) + weights_prime, update_alpha = fedmeta_update_meta_sgd(self.net, self.alpha, self.beta, weights_results[0][0], gradients_aggregated, WD) self.alpha = update_alpha parameters_aggregated = ndarrays_to_parameters(weights_prime) @@ -238,16 +300,22 @@ def aggregate_evaluate( ] ) - weight_loss = sum([evaluate_res.metrics['loss'] * evaluate_res.num_examples for _, evaluate_res in results]) / sum( - [evaluate_res.num_examples for _, evaluate_res in results]) - log(INFO, f'Loss : {weight_loss}') + if self.data == 'femnist': + smoothing_weight = 0.9 + else: + smoothing_weight = 0.7 + self.ema_loss = update_ema(self.ema_loss, loss_aggregated, smoothing_weight) + wandb.log({"Training Loss": self.ema_loss}, step=server_round) + loss_aggregated = self.ema_loss # Aggregate custom metrics if aggregation fn was provided metrics_aggregated = {} if self.evaluate_metrics_aggregation_fn: eval_metrics = [(res.num_examples, res.metrics) for _, res in results] metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics) - log(INFO, f'Accuracy : {round(metrics_aggregated["accuracy"] * 100, 3)}') + self.ema_acc = update_ema(self.ema_acc, round(metrics_aggregated['accuracy'] * 100, 3), smoothing_weight) + wandb.log({"Test_Accuracy ": self.ema_acc}, step=server_round) + metrics_aggregated['accuracy'] = self.ema_acc elif server_round == 1: # Only log this warning once log(WARNING, "No evaluate_metrics_aggregation_fn provided") diff --git a/baselines/FedMeta/FedMeta/utils.py b/baselines/FedMeta/FedMeta/utils.py index dcd302a14a33..c27be4900e31 100644 --- a/baselines/FedMeta/FedMeta/utils.py +++ b/baselines/FedMeta/FedMeta/utils.py @@ -5,60 +5,158 @@ results, plotting. """ -import numpy as np +from typing import List, Dict import pickle +import os +import matplotlib.pyplot as plt +# Encoding list for the Shakespeare dataset ALL_LETTERS = "\n !\"&'(),-.0123456789:;>?ABCDEFGHIJKLMNOPQRSTUVWXYZ[]abcdefghijklmnopqrstuvwxyz}" -NUM_LETTERS = len(ALL_LETTERS) -def _one_hot(index, size): - '''returns one-hot vector with given size and value 1 at given index - ''' +def _one_hot( + index: int, + size: int, +) -> List: + """ + returns one-hot vector with given size and value 1 at given index + + """ + vec = [0 for _ in range(size)] vec[int(index)] = 1 return vec -def letter_to_vec(letter): - '''returns one-hot representation of given letter - ''' +def letter_to_vec( + letter: str, +) -> int: + """ + returns one-hot representation of given letter + + """ + index = ALL_LETTERS.find(letter) return index -def word_to_indices(word): - '''returns a list of character indices +def word_to_indices( + word: str, +) -> List: + """ + returns a list of character indices Args: word: string Return: indices: int list with length len(word) - ''' + + """ + indices = [] for c in word: indices.append(ALL_LETTERS.find(c)) return indices -# def compute_ema(data, smoothingWeight): -# smoothedData = [] -# last = 0 if len(data) > 0 else float('nan') -# numAccum = 0 -# debiasWeight = 0 -# -# for d in data: -# nextVal = d -# last = last * smoothingWeight + (1 - smoothingWeight) * nextVal -# numAccum += 1 -# debiasWeight = 1.0 - pow(smoothingWeight, numAccum) -# smoothedData.append(last / debiasWeight) -# -# return smoothedData -# -# -# data = [1, 2, 3, 4, 5] -# smoothingWeight = 0.8 -# result = compute_ema(data, smoothingWeight) -# print(result) +def update_ema( + prev_ema: float, + current_value: float, + smoothing_weight: float, +) -> float: + """ + We use EMA to visually enhance the learning trend for each round. + + Parameters + ---------- + prev_ema : float + The list of metrics to aggregate. + current_value : float + The list of metrics to aggregate. + smoothing_weight : float + The list of metrics to aggregate. + + + Returns + ------- + EMA_Loss or EMA_ACC + The weighted average metric. + + """ + if prev_ema is None: + return current_value + return (1 - smoothing_weight) * current_value + smoothing_weight * prev_ema + + +def save_graph_params(data_info: Dict): + """ + Save parameters to visualize experiment results (Loss, ACC) + + Parameters + ---------- + data_info : Dict + This is a parameter dictionary of data from which the experiment was completed. + """ + + if os.path.exists(f"{data_info['path']}/{data_info['algo']}.pkl"): + raise ValueError(f"'{data_info['path']}/{data_info['algo']}.pkl' is already exists!") + + with open(f"{data_info['path']}/{data_info['algo']}.pkl", 'wb') as f: + pickle.dump(data_info, f) + + +def plot_from_pkl(directory="."): + """ + Visualization of algorithms for each data (FedAvg, FedAvg_Meta, FedMeta_MAML, FedMeta_Meta-SGD) + + Parameters + ---------- + directory : str + Graph params directory path for Femnist or Shakespeare + + """ + + color_mapping = { + "fedavg.pkl": "green", + "fedavg_meta.pkl": "blue", + "fedmeta_maml.pkl": "orange", + "fedmeta_meta_sgd.pkl": "red", + # ... 여기에 추가 파일 이름과 색상을 매핑 ... + } + + pkl_files = [f for f in os.listdir(directory) if f.endswith('.pkl')] + + all_data = {} + + for file in pkl_files: + with open(os.path.join(directory, file), 'rb') as f: + data = pickle.load(f) + all_data[file] = data + + plt.figure(figsize=(14, 6)) + + # Acc graph + plt.subplot(1, 2, 1) + for file, data in all_data.items(): + accuracies = [acc for _, acc in data["accuracy"]['accuracy']] + plt.plot(accuracies, label=file, color=color_mapping.get(file, "black")) + plt.title("Accuracy") + plt.legend() + + # Loss graph + plt.subplot(1, 2, 2) + for file, data in all_data.items(): + loss = [loss for _, loss in data["loss"]] + plt.plot(loss, label=file, color=color_mapping.get(file, "black")) + plt.title("Loss") + plt.legend() + + plt.tight_layout() + + save_path = f"{directory}/result_graph.png" + plt.savefig(save_path) + + plt.show() +if __name__ == '__main__': + plot_from_pkl() diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 1ac6441bde24..dca7ebd671bb 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -30,7 +30,7 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline ****Task:**** : A comparison task of four algorithms(FedAvg, FedAvg(Meta), FedMeta(MAML), FedMeta(Meta-SGD)) in the categories of Image Classification and next-word prediction. ****Model:**** :This directory implements two models: -* A two-layer CNN network as used in the FedMeta paper (see `models/CNN_Network`). This is the model used by default. +* A two-layer CNN network as used in the FedMeta paper for Femnist (see `models/CNN_Network`). * A StackedLSTM model used in the FedMeta paper for Shakespeare (see `models/StackedLSTM`). **You can see more detail in Apendix.A of the paper** @@ -102,20 +102,17 @@ python main.py algo=fedmeta_maml data=shakespeare path=(your leaf dataset path)/ #FedMeta(Meta-SGD) + Shakespeare Dataset python main.py algo=fedmeta_meta_sgd data=shakespeare path=(your leaf dataset path)/leaf/data/shakespeare/data + ``` ## Expected Results - -:warning: _Your baseline implementation should replicate several of the experiments in the original paper. Please include here the exact command(s) needed to run each of those experiments followed by a figure (e.g. a line plot) or table showing the results you obtained when you ran the code. Below is an example of how you can present this. Please add command followed by results for all your experiments._ - +If you proceed with all of the above experiments, You can get a graph of your experiment results as shown below along that `./femnist or shakespeare/graph_params/result_graph.png`. ```bash -# it is likely that for one experiment you need to sweep over different hyperparameters. You are encouraged to use Hydra's multirun functionality for this. This is an example of how you could achieve this for some typical FL hyperparameteres - -poetry run -m .main --multirun num_client_per_round=5,10,50 dataset=femnist,cifar10 -# the above command will run a total of 6 individual experiments (because 3client_configs x 2datasets = 6 -- you can think of it as a grid). - +#If you want to see the graph, use the command below. +python [Now show a figure/table displaying the results of the above command] # add more commands + plots for additional experiments. +![](shakespeare/graph_params/result_graph.png) ``` From 14667f98deb6acd7d13ac5a59d0fd52ab9347d2e Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 09:11:39 +0900 Subject: [PATCH 071/133] fix readme --- .../FedMeta/FedMeta/docs/result_graph.png | Bin 0 -> 117804 bytes baselines/FedMeta/README.md | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 baselines/FedMeta/FedMeta/docs/result_graph.png diff --git a/baselines/FedMeta/FedMeta/docs/result_graph.png 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similarity index 100% rename from baselines/FedMeta/FedMeta/docs/result_graph.png rename to baselines/FedMeta/docs/result_graph.png From 2765987334ed1e7cd5d3818956d6337495d89e0f Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 09:16:35 +0900 Subject: [PATCH 073/133] test graph --- baselines/FedMeta/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 0ade7ef7158b..a2d665798ac0 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -114,5 +114,5 @@ python [Now show a figure/table displaying the results of the above command] # add more commands + plots for additional experiments. -![](docs/result_graph.png) ``` +![](docs/result_graph.png) From 80cfbafe1592924732c8edf0536273aec42e0a1f Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 09:17:23 +0900 Subject: [PATCH 074/133] delete dir --- .../shakespeare/graph_params/result_graph.png | Bin 117804 -> 0 bytes 1 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z0%Mm1_OPo-?LPKy?FTbc2HngB4BMA(Mq*yP*MBqW1{fbmwCwB-9A}sj4`}+;5|}TK pnoS$nE}yO>VVifa|HpTew&Lqs^{;;w+ezWI)!xyr@Kdk2zX3k?W^(`l From 978f00033bf381a9c282da426c0c20a819fa596a Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 09:44:37 +0900 Subject: [PATCH 075/133] fix code tree --- baselines/FedMeta/FedMeta/utils.py | 4 +-- baselines/FedMeta/README.md | 24 ++++++++++-------- baselines/FedMeta/docs/result_graph.png | Bin 117804 -> 0 bytes .../FedMeta/docs/shakespeare_result_graph.png | Bin 0 -> 108181 bytes 4 files changed, 15 insertions(+), 13 deletions(-) delete mode 100644 baselines/FedMeta/docs/result_graph.png create mode 100644 baselines/FedMeta/docs/shakespeare_result_graph.png diff --git a/baselines/FedMeta/FedMeta/utils.py b/baselines/FedMeta/FedMeta/utils.py index c27be4900e31..387b62063c2e 100644 --- a/baselines/FedMeta/FedMeta/utils.py +++ b/baselines/FedMeta/FedMeta/utils.py @@ -121,7 +121,6 @@ def plot_from_pkl(directory="."): "fedavg_meta.pkl": "blue", "fedmeta_maml.pkl": "orange", "fedmeta_meta_sgd.pkl": "red", - # ... 여기에 추가 파일 이름과 색상을 매핑 ... } pkl_files = [f for f in os.listdir(directory) if f.endswith('.pkl')] @@ -158,5 +157,6 @@ def plot_from_pkl(directory="."): plt.show() + if __name__ == '__main__': - plot_from_pkl() + plot_from_pkl('./femnist/graph_params') diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index a2d665798ac0..e849e2f41326 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -75,33 +75,34 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline #SHAKESEPEARE dataset Download command for these experiments ./preprocess.sh -s niid --sf 0.16 -k 0 -t sample ```` + ****Start experiments**** : ```bash # FedAvg + Femnist Dataset python main.py algo=fedavg data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data # FedAvg(Meta) + Femnist Dataset -python main.py algo=fedavg_meta data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data +python main.py algo=fedavg_meta data=femnist path=../leaf/data/shakespeare/data # FedMeta(MAML) + Femnist Dataset -python main.py algo=fedmeta_maml data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data +python main.py algo=fedmeta_maml data=femnist path=../leaf/data/shakespeare/data # FedMeta(Meta-SGD) + Femnist Dataset -python main.py algo=fedmeta_meta_sgd data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data +python main.py algo=fedmeta_meta_sgd data=femnist path=../leaf/data/shakespeare/data #FedAvg + Shakespeare Dataset -python main.py algo=fedavg data=shakespeare path=(your leaf dataset path)/leaf/data/shakespeare/data +python main.py algo=fedavg data=shakespeare path=../leaf/data/shakespeare/data #FedAvg(Meta) + Shakespeare Dataset -python main.py algo=fedavg_meta data=shakespeare path=(your leaf dataset path)/leaf/data/shakespeare/data +python main.py algo=fedavg_meta data=shakespeare path=../leaf/data/shakespeare/data #FedMeta(MAML) + Shakespeare Dataset -python main.py algo=fedmeta_maml data=shakespeare path=(your leaf dataset path)/leaf/data/shakespeare/data +python main.py algo=fedmeta_maml data=shakespeare path=../leaf/data/shakespeare/data #FedMeta(Meta-SGD) + Shakespeare Dataset -python main.py algo=fedmeta_meta_sgd data=shakespeare path=(your leaf dataset path)/leaf/data/shakespeare/data +python main.py algo=fedmeta_meta_sgd data=shakespeare path=../leaf/data/shakespeare/data ``` @@ -109,10 +110,11 @@ python main.py algo=fedmeta_meta_sgd data=shakespeare path=(your leaf dataset pa ## Expected Results If you proceed with all of the above experiments, You can get a graph of your experiment results as shown below along that `./femnist or shakespeare/graph_params/result_graph.png`. ```bash -#If you want to see the graph, use the command below. +#You can check the graph using the command below. python -[Now show a figure/table displaying the results of the above command] -# add more commands + plots for additional experiments. ``` -![](docs/result_graph.png) +**Femnist dataset experiment results** + +**Shakespeare dataset experiment results** +![](docs/shakespeare_result_graph.png) diff --git a/baselines/FedMeta/docs/result_graph.png b/baselines/FedMeta/docs/result_graph.png deleted file mode 100644 index 9c4661b71097b9347e050ee8e5dc5ebfcc07af31..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 117804 zcmdSBhduQan67GPx%YRq*f#UGebF zXq+R0pY-E}gu-8&$q=i;&np(R9GWqcFgI;eV4(GsDBj!;^b>U(GdsdBnvd zc|E>&EPkBvxM$S-?`Ve;Oi{+S%%(-RHF;7f4J7o`{G^709{?Ef$)H<8|-oyWS zWQS5#GX2j(^W*=2zv~-c%Bg8U6+BCROr{?2&qtb6F=_~^MHS>(kBH%ZWUsF3tNw7B zHt4!=$d^a&^HFWIJF6p0i?;t=j_EsTXLH^@AzB%$Ns8oD-&{ybqNJp(E47=_rdNr( zqg^__c=6w-Qa^*2Vt(Bad|kFZUes-GBEW6yk8C$${qe(xoh3GU@2I)orKPbFo~Qn* z5Pc_J%!vbuu(aeNASVCnxX|V5<`%}ICpB1XrJ$j46;C0G3wN2uO_XU4j3Typn6`!( zUL%zqrsnggkB{pw_GYyl?PFCN 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z0%Mm1_OPo-?LPKy?FTbc2HngB4BMA(Mq*yP*MBqW1{fbmwCwB-9A}sj4`}+;5|}TK pnoS$nE}yO>VVifa|HpTew&Lqs^{;;w+ezWI)!xyr@Kdk2zX3k?W^(`l literal 0 HcmV?d00001 diff --git a/datasets/README.md b/datasets/README.md index 29a201f3bd04..223b0c612178 100644 --- a/datasets/README.md +++ b/datasets/README.md @@ -1,65 +1 @@ # Flower Datasets - -[![GitHub license](https://img.shields.io/github/license/adap/flower)](https://github.com/adap/flower/blob/main/LICENSE) -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md) -![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg) -![Downloads](https://pepy.tech/badge/flwr) -[![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.dev/join-slack) - -Flower Datasets (`flwr-datasets`) is a library to quickly and easily create datasets for federated learning, federated evaluation, and federated analytics. It was created by the `Flower Labs` team that also created Flower: A Friendly Federated Learning Framework. -Flower Datasets library supports: -* **downloading datasets** - choose the dataset from Hugging Face's `datasets`, -* **partitioning datasets** - customize the partitioning scheme, -* **creating centralized datasets** - leave parts of the dataset unpartitioned (e.g. for centralized evaluation). - -Thanks to using Hugging Face's `datasets` used under the hood, Flower Datasets integrates with the following popular formats/frameworks: -* Hugging Face, -* PyTorch, -* TensorFlow, -* Numpy, -* Pandas, -* Jax, -* Arrow. - -Create **custom partitioning schemes** or choose from the **implemented partitioning schemes**: -* IID partitioning `IidPartitioner(num_partitions)` -* more to come in future releases. - -# Installation - -## With pip - -Flower Datasets can be installed from PyPi - -```bash -pip install flwr-datasets - -If you plan to change the type of the dataset to run the code with your ML framework, make sure to have it installed too. - -# Usage - -The Flower Datasets exposes `FederatedDataset(dataset, partitioners)` abstraction to represent the dataset needed for federated learning/analytics. It has two powerful methods that let you handle the dataset preprocessing. They are `load_partition(idx, split)` and `load_full(split)`. - -Here's a quick example of how to partition the MNIST dataset: - - -`FederatedDataset(dataset, partitioners)` allows you specification of: - -* `dataset:str` - the name of the dataset. - -* `partitioners: Dict[str: int]` - `{split_name: str` to `number-of-partitions: int}` - partitioner that will be used with an associated split of the dataset e.g. `{"train": 100}`. It assumes by default the i.i.d. partitioning. - -More customization of `partitioners` is coming in future releases. - -# Future release - -Here are a few of the things that we will work on in future releases: - -* Support for more datasets (especially the ones that have user id present). -* Creation of custom `Partitioner`s. -* More out-of-the-box `Partitioner`s. -* Passing `Partitioner`s via `FederatedDataset`'s `partitioner` argument. -* Customization of the dataset splitting before the partitioning. -* Simplification of the dataset transformation to the popular frameworks/types. -* Creation of the synthetic data, -* Support for Vertical FL. diff --git a/datasets/flwr_datasets/__init__.py b/datasets/flwr_datasets/__init__.py index 0149a53b7b0a..1a48df5e45f0 100644 --- a/datasets/flwr_datasets/__init__.py +++ b/datasets/flwr_datasets/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2023 Flower Labs GmbH. All Rights Reserved. +# Copyright 2023 Adap GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -13,8 +13,3 @@ # limitations under the License. # ============================================================================== """Flower Datasets main package.""" - - -from .federated_dataset import FederatedDataset - -__all__ = ["FederatedDataset"] diff --git a/datasets/pyproject.toml b/datasets/pyproject.toml index d0a1d80d80d4..e805b710018c 100644 --- a/datasets/pyproject.toml +++ b/datasets/pyproject.toml @@ -54,7 +54,6 @@ exclude = [ [tool.poetry.dependencies] python = "^3.8" numpy = "^1.21.0" -datasets = "^2.14.3" [tool.poetry.dev-dependencies] isort = "==5.11.5" @@ -63,7 +62,6 @@ docformatter = "==1.7.1" mypy = "==1.4.0" pylint = "==2.13.9" flake8 = "==3.9.2" -parameterized = "==0.9.0" pytest = "==7.1.2" pytest-watch = "==4.2.0" ruff = "==0.0.277" From d2118e01c039e86deda86587ec1dff07011761b2 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 09:58:18 +0900 Subject: [PATCH 077/133] test pull --- datasets/README.md | 3 --- datasets/pyproject.toml | 3 --- src/py/flwr/simulation/ray_transport/ray_client_proxy.py | 7 ------- 3 files changed, 13 deletions(-) diff --git a/datasets/README.md b/datasets/README.md index b8381cacbaad..982da3760cc7 100644 --- a/datasets/README.md +++ b/datasets/README.md @@ -1,6 +1,4 @@ # Flower Datasets -<<<<<<< HEAD -======= [![GitHub license](https://img.shields.io/github/license/adap/flower)](https://github.com/adap/flower/blob/main/LICENSE) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md) @@ -90,4 +88,3 @@ Here are a few of the things that we will work on in future releases: * Simplification of the dataset transformation to the popular frameworks/types. * Creation of the synthetic data, * Support for Vertical FL. ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 diff --git a/datasets/pyproject.toml b/datasets/pyproject.toml index 574132f51528..aa35ddca4fce 100644 --- a/datasets/pyproject.toml +++ b/datasets/pyproject.toml @@ -54,13 +54,10 @@ exclude = [ [tool.poetry.dependencies] python = "^3.8" numpy = "^1.21.0" -<<<<<<< HEAD -======= datasets = "^2.14.3" pillow = { version = ">=6.2.1", optional = true } soundfile = {version = ">=0.12.1", optional = true} librosa = { version = ">=0.10.0.post2", optional = true } ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 [tool.poetry.dev-dependencies] isort = "==5.11.5" diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index 335ba78c932b..a309c9f37aea 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -17,11 +17,7 @@ import traceback from logging import ERROR -<<<<<<< HEAD from typing import Callable, Dict, Optional, Union, cast -======= -from typing import Dict, Optional, cast ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 import ray @@ -40,13 +36,10 @@ JobFn, VirtualClientEngineActorPool, ) -<<<<<<< HEAD ClientFn = Callable[[str], ClientLike] ClientRes = Union[ common.GetPropertiesRes, common.GetParametersRes, common.FitRes, common.EvaluateRes ] -======= ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 class RayClientProxy(ClientProxy): From 2e622225000b2d4579aab5ac732db4afbdfc0750 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 10:26:08 +0900 Subject: [PATCH 078/133] fix codeline --- baselines/FedMeta/FedMeta/main.py | 1 + baselines/FedMeta/FedMeta/utils.py | 4 ---- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/baselines/FedMeta/FedMeta/main.py b/baselines/FedMeta/FedMeta/main.py index 58ba5d8a4185..b3bd6dfc2758 100644 --- a/baselines/FedMeta/FedMeta/main.py +++ b/baselines/FedMeta/FedMeta/main.py @@ -96,5 +96,6 @@ def main(cfg: DictConfig) -> None: plot_from_pkl(directory=f"./{cfg.data.data}/graph_params") print("................") + if __name__ == "__main__": main() diff --git a/baselines/FedMeta/FedMeta/utils.py b/baselines/FedMeta/FedMeta/utils.py index 387b62063c2e..9c6de78757e0 100644 --- a/baselines/FedMeta/FedMeta/utils.py +++ b/baselines/FedMeta/FedMeta/utils.py @@ -156,7 +156,3 @@ def plot_from_pkl(directory="."): plt.savefig(save_path) plt.show() - - -if __name__ == '__main__': - plot_from_pkl('./femnist/graph_params') From d2da352303b4a696c18fb0bb8cef295467e90524 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 10:35:44 +0900 Subject: [PATCH 079/133] add branch --- baselines/FedMeta/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index e849e2f41326..53065a2b51fc 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -115,6 +115,7 @@ python ``` **Femnist dataset experiment results** +path **Shakespeare dataset experiment results** ![](docs/shakespeare_result_graph.png) From 930cf516c24fcb0e50dd240feaeee354247aac8a Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 10:42:38 +0900 Subject: [PATCH 080/133] Update Fedmeta --- .../femnist/graph_params/result_graph.png | Bin 0 -> 151963 bytes baselines/FedMeta/FedMeta/utils.py | 7 +++++-- baselines/FedMeta/README.md | 11 ++++++----- .../FedMeta/docs/femnist_result_graph.png | Bin 0 -> 151963 bytes 4 files changed, 11 insertions(+), 7 deletions(-) create mode 100644 baselines/FedMeta/FedMeta/femnist/graph_params/result_graph.png create mode 100644 baselines/FedMeta/docs/femnist_result_graph.png diff --git a/baselines/FedMeta/FedMeta/femnist/graph_params/result_graph.png b/baselines/FedMeta/FedMeta/femnist/graph_params/result_graph.png new file mode 100644 index 0000000000000000000000000000000000000000..d03f43dcfa848246c4bedcf07a6a0deeb51aa990 GIT binary patch literal 151963 zcmc$GWn7ip*DYPrpeP_AC?MSp(g;$L(nxoAx6(*Sm(r5b4Fb{)(%sUrflb`C^}PRh 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os.listdir(directory) if f.endswith('.pkl')] @@ -138,7 +139,8 @@ def plot_from_pkl(directory="."): plt.subplot(1, 2, 1) for file, data in all_data.items(): accuracies = [acc for _, acc in data["accuracy"]['accuracy']] - plt.plot(accuracies, label=file, color=color_mapping.get(file, "black")) + legend_ = file[:-4] if file.endswith('.pkl') else file + plt.plot(accuracies, label=legend_, color=color_mapping.get(file, "black")) plt.title("Accuracy") plt.legend() @@ -146,7 +148,8 @@ def plot_from_pkl(directory="."): plt.subplot(1, 2, 2) for file, data in all_data.items(): loss = [loss for _, loss in data["loss"]] - plt.plot(loss, label=file, color=color_mapping.get(file, "black")) + legend_ = file[:-4] if file.endswith('.pkl') else file + plt.plot(loss, label=legend_, color=color_mapping.get(file, "black")) plt.title("Loss") plt.legend() diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 53065a2b51fc..e357dc0a0a48 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -109,13 +109,14 @@ python main.py algo=fedmeta_meta_sgd data=shakespeare path=../leaf/data/shakespe ## Expected Results If you proceed with all of the above experiments, You can get a graph of your experiment results as shown below along that `./femnist or shakespeare/graph_params/result_graph.png`. -```bash -#You can check the graph using the command below. -python -``` **Femnist dataset experiment results** -path + + +![](docs/femnist_result_graph.png) + **Shakespeare dataset experiment results** + + ![](docs/shakespeare_result_graph.png) diff --git a/baselines/FedMeta/docs/femnist_result_graph.png b/baselines/FedMeta/docs/femnist_result_graph.png new file mode 100644 index 0000000000000000000000000000000000000000..d03f43dcfa848246c4bedcf07a6a0deeb51aa990 GIT binary patch literal 151963 zcmc$GWn7ip*DYPrpeP_AC?MSp(g;$L(nxoAx6(*Sm(r5b4Fb{)(%sUrflb`C^}PRh zfA_=vcF#HJ5g(rY?6u~abBr;^+#eO?rLZuFG2r0fuwF}xzlDQCc7uaM&_F{0-}wa> z6%Bslb&}9Z2o=M=si@e&APFSR(j30R| 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zCcpCbrkLr1xUmR4!^B$ldzQ49qi%Ha^76B2WHQ>FW?wt^Q5q41J>)a(V0T5vUF=Pj zkx@~;5S~?6bR>w{JycOq*$b21-Ndd8S=F&gN!3+!CDu9wVUJU%Bb}ovkILYL3z^txv9%kg_6 z_k(P4ibk4btif3BA8neBjuj37ex{H?>El$0OFu?LWD25|NLwmr2V?^uOn|i5_ePhh z0Y>cXh7wyAM@XoYK&H?XazUElIZJ)X=%P2m+vySJ9^c)zbQF37aTyQI znfAoeU%wpNunuOWX6XLK+AZD7;^IRe5)Th{AI$e;h2ePKc}`AK*b?Q@$;o;|OS9`& z%m=Pq77|j$F=HO+SwX>c?LL@|!OAL7DYNon+t;*k02cSqMiNZfkN(_zr?ok>JQ zgyxGEB@S&uXk(DF-}Q@X`JL_JOA@%1gZle9@5fkYet()Xh83|G{P=mL+VqGFn_xyg z4htY9V+^*gX>6R`pK^l$o0FAvReR3_Q|Xov3f(<676z5`RC3Tcvg`b7U@4ETTZf&)tGtO2KW>UcH;{FA?>C|f9_ H|LVU0PlQNk literal 0 HcmV?d00001 From 1011dd4a90197343cf6d613be76c601c3d4f4f7c Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 10:52:02 +0900 Subject: [PATCH 081/133] remove wandb for test --- baselines/FedMeta/EXTENDED_README.md | 123 -------------------------- baselines/FedMeta/FedMeta/strategy.py | 9 -- 2 files changed, 132 deletions(-) delete mode 100644 baselines/FedMeta/EXTENDED_README.md diff --git a/baselines/FedMeta/EXTENDED_README.md b/baselines/FedMeta/EXTENDED_README.md deleted file mode 100644 index 9c8f5bc72fa9..000000000000 --- a/baselines/FedMeta/EXTENDED_README.md +++ /dev/null @@ -1,123 +0,0 @@ - -# Extended Readme - -> The baselines are expected to run in a machine running Ubuntu 22.04 - -While `README.md` should include information about the baseline you implement and how to run it, this _extended_ readme provides info on what's the expected directory structure for a new baseline and more generally the instructions to follow before your baseline can be merged into the Flower repository. Please follow closely these instructions. It is likely that you have already completed steps 1-2. - -1. Fork the Flower repository and clone it. -2. Navigate to the `baselines/` directory and from there run: - ```bash - # This will create a new directory with the same structure as this `baseline_template` directory. - ./dev/create-baseline.sh - ``` -3. All your code and configs should go into a sub-directory with the same name as the name of your baseline. - * The sub-directory contains a series of Python scripts that you can edit. Please stick to these files and consult with us if you need additional ones. - * There is also a basic config structure in `/conf` ready be parsed by [Hydra](https://hydra.cc/) when executing your `main.py`. -4. Therefore, the directory structure in your baseline should look like: - ```bash - baselines/ - ├── README.md # describes your baseline and everything needed to use it - ├── EXTENDED_README.md # to remove before creating your PR - ├── pyproject.toml # details your Python environment - └── - ├── *.py # several .py files including main.py and __init__.py - └── conf - └── *.yaml # one or more Hydra config files - - ``` -> :warning: Make sure the variable `name` in `pyproject.toml` is set to the name of the sub-directory containing all your code. - -5. Add your dependencies to the `pyproject.toml` (see below a few examples on how to do it). Read more about Poetry below in this `EXTENDED_README.md`. -6. Regularly check that your coding style and the documentation you add follow good coding practices. To test whether your code meets the requirements, please run the following: - ```bash - # After activating your environment and from your baseline's directory - cd .. # to go to the top-level directory of all baselines - ./dev/test-baseline.sh - ./dev/test-baseline-structure.sh - ``` - Both `test-baseline.sh` and `test-baseline-structure.sh` will also be automatically run when you create a PR, and both tests need to pass for the baseline to be merged. - To automatically solve some formatting issues and apply easy fixes, please run the formatting script: - ```bash - # After activating your environment and from your baseline's directory - cd .. # to go to the top-level directory of all baselines - ./dev/format-baseline.sh - ``` -7. Ensure that the Python environment for your baseline can be created without errors by simply running `poetry install` and that this is properly described later when you complete the `Environment Setup` section in `README.md`. This is specially important if your environment requires additional steps after doing `poetry install`. -8. Ensure that your baseline runs with default arguments by running `poetry run python -m .main`. Then, describe this and other forms of running your code in the `Running the Experiments` section in `README.md`. -9. Once your code is ready and you have checked: - * that following the instructions in your `README.md` the Python environment can be created correctly - - * that running the code following your instructions can reproduce the experiments in the paper - - , then you just need to create a Pull Request (PR) to kickstart the process of merging your baseline into the Flower repository. - -> Once you are happy to merge your baseline contribution, please delete this `EXTENDED_README.md` file. - - -## About Poetry - -We use Poetry to manage the Python environment for each individual baseline. You can follow the instructions [here](https://python-poetry.org/docs/) to install Poetry in your machine. - - -### Specifying a Python Version (optional) -By default, Poetry will use the Python version in your system. In some settings, you might want to specify a particular version of Python to use inside your Poetry environment. You can do so with [`pyenv`](https://github.com/pyenv/pyenv). Check the documentation for the different ways of installing `pyenv`, but one easy way is using the [automatic installer](https://github.com/pyenv/pyenv-installer): -```bash -curl https://pyenv.run | bash # then, don't forget links to your .bashrc/.zshrc -``` - -You can then install any Python version with `pyenv install ` (e.g. `pyenv install 3.9.17`). Then, in order to use that version for your baseline, you'd do the following: - -```bash -# cd to your baseline directory (i.e. where the `pyproject.toml` is) -pyenv local - -# set that version for poetry -poetry env use - -# then you can install your Poetry environment (see the next setp) -``` - -### Installing Your Environment -With the Poetry tool already installed, you can create an environment for this baseline with commands: -```bash -# run this from the same directory as the `pyproject.toml` file is -poetry install -``` - -This will create a basic Python environment with just Flower and additional packages, including those needed for simulation. Next, you should add the dependencies for your code. It is **critical** that you fix the version of the packages you use using a `=` not a `=^`. You can do so via [`poetry add`](https://python-poetry.org/docs/cli/#add). Below are some examples: - -```bash -# For instance, if you want to install tqdm -poetry add tqdm==4.65.0 - -# If you already have a requirements.txt, you can add all those packages (but ensure you have fixed the version) in one go as follows: -poetry add $( cat requirements.txt ) -``` -With each `poetry add` command, the `pyproject.toml` gets automatically updated so you don't need to keep that `requirements.txt` as part of this baseline. - - -More critically however, is adding your ML framework of choice to the list of dependencies. For some frameworks you might be able to do so with the `poetry add` command. Check [the Poetry documentation](https://python-poetry.org/docs/cli/#add) for how to add packages in various ways. For instance, let's say you want to use PyTorch: - -```bash -# with plain `pip` you'd run a command such as: -pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117 - -# to add the same 3 dependencies to your Poetry environment you'd need to add the URL to the wheel that the above pip command auto-resolves for you. -# You can find those wheels in `https://download.pytorch.org/whl/cu117`. Copy the link and paste it after the `poetry add` command. -# For instance to add `torch==1.13.1+cu117` and a x86 Linux system with Python3.8 you'd: -poetry add https://download.pytorch.org/whl/cu117/torch-1.13.1%2Bcu117-cp38-cp38-linux_x86_64.whl -# you'll need to repeat this for both `torchvision` and `torchaudio` -``` -The above is just an example of how you can add these dependencies. Please refer to the Poetry documentation to extra reference. - -If all attempts fail, you can still install packages via standard `pip`. You'd first need to source/activate your Poetry environment. -```bash -# first ensure you have created your environment -# and installed the base packages provided in the template -poetry install - -# then activate it -poetry shell -``` -Now you are inside your environment (pretty much as when you use `virtualenv` or `conda`) so you can install further packages with `pip`. Please note that, unlike with `poetry add`, these extra requirements won't be captured by `pyproject.toml`. Therefore, please ensure that you provide all instructions needed to: (1) create the base environment with Poetry and (2) install any additional dependencies via `pip` when you complete your `README.md`. \ No newline at end of file diff --git a/baselines/FedMeta/FedMeta/strategy.py b/baselines/FedMeta/FedMeta/strategy.py index 208b822fde41..6042cb0246a8 100644 --- a/baselines/FedMeta/FedMeta/strategy.py +++ b/baselines/FedMeta/FedMeta/strategy.py @@ -30,13 +30,6 @@ EvaluateIns, NDArrays, ) -import wandb - -wandb.init( - # set the wandb project where this run will be logged - project="SoR", - -) def fedmeta_update_meta_sgd( @@ -305,7 +298,6 @@ def aggregate_evaluate( else: smoothing_weight = 0.7 self.ema_loss = update_ema(self.ema_loss, loss_aggregated, smoothing_weight) - wandb.log({"Training Loss": self.ema_loss}, step=server_round) loss_aggregated = self.ema_loss # Aggregate custom metrics if aggregation fn was provided @@ -314,7 +306,6 @@ def aggregate_evaluate( eval_metrics = [(res.num_examples, res.metrics) for _, res in results] metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics) self.ema_acc = update_ema(self.ema_acc, round(metrics_aggregated['accuracy'] * 100, 3), smoothing_weight) - wandb.log({"Test_Accuracy ": self.ema_acc}, step=server_round) metrics_aggregated['accuracy'] = self.ema_acc elif server_round == 1: # Only log this warning once From 8d4e380db854183a745c96c76f9cf5e79a7d9070 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 11:06:04 +0900 Subject: [PATCH 082/133] Pull request Fedmeta version 1.0 --- baselines/FedMeta/README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index e357dc0a0a48..68fbfcecc306 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -62,8 +62,6 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline ## Environment Setup -:warning: _The Python environment for all baselines should follow these guidelines in the `EXTENDED_README`. Specify the steps to create and activate your environment. If there are any external system-wide requirements, please include instructions for them too. These instructions should be comprehensive enough so anyone can run them (if non standard, describe them step-by-step)._ - ## Running the Experiments From eee5d5435381874751b9d5c070f764f5d3360c4a Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 11:15:42 +0900 Subject: [PATCH 083/133] fix README.md --- baselines/FedMeta/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 68fbfcecc306..5079f9e93603 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -61,7 +61,7 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline ## Environment Setup - +We will update after testing. ## Running the Experiments From 787a10d60a70e4786b4dd349f599e67b5f1792ee Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sun, 1 Oct 2023 11:40:12 +0900 Subject: [PATCH 084/133] Update README.md - add Environment Setup --- baselines/FedMeta/README.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/baselines/FedMeta/README.md b/baselines/FedMeta/README.md index 5079f9e93603..321827839f31 100644 --- a/baselines/FedMeta/README.md +++ b/baselines/FedMeta/README.md @@ -61,7 +61,14 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline ## Environment Setup -We will update after testing. +```bash +#Environment Setup +Poetry install +Poetry shell +Pip install torch torch vision +Pip install matplotlib +pip install scikit-learn +``` ## Running the Experiments @@ -72,7 +79,7 @@ We will update after testing. #SHAKESEPEARE dataset Download command for these experiments ./preprocess.sh -s niid --sf 0.16 -k 0 -t sample -```` +``` ****Start experiments**** : ```bash From fe23e6a9be39c64639f394ad01aa6c98c6424b8b Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Mon, 2 Oct 2023 03:18:45 +0900 Subject: [PATCH 085/133] Update data.yaml --- .../FedMeta/FedMeta/conf/data/femnist.yaml | 17 +++++++++++++++++ .../FedMeta/FedMeta/conf/data/shakespeare.yaml | 17 +++++++++++++++++ 2 files changed, 34 insertions(+) create mode 100644 baselines/FedMeta/FedMeta/conf/data/femnist.yaml create mode 100644 baselines/FedMeta/FedMeta/conf/data/shakespeare.yaml diff --git a/baselines/FedMeta/FedMeta/conf/data/femnist.yaml b/baselines/FedMeta/FedMeta/conf/data/femnist.yaml new file mode 100644 index 000000000000..6ad6b64498fa --- /dev/null +++ b/baselines/FedMeta/FedMeta/conf/data/femnist.yaml @@ -0,0 +1,17 @@ +--- +# this is the config that will be loaded as default by main.py +# Please follow the provided structure (this will ensuring all baseline follow +# a similar configuration structure and hence be easy to customise) + +model: + _target_: models.CNN_network # model config + +client_resources: + num_cpus: 4 + num_gpus: 0.25 + +num_rounds: 2000 +data: femnist +support_ratio: 0.2 +batch_size: 10 +gradient_step: 5 diff --git a/baselines/FedMeta/FedMeta/conf/data/shakespeare.yaml b/baselines/FedMeta/FedMeta/conf/data/shakespeare.yaml new file mode 100644 index 000000000000..36c4744669b4 --- /dev/null +++ b/baselines/FedMeta/FedMeta/conf/data/shakespeare.yaml @@ -0,0 +1,17 @@ +--- +# this is the config that will be loaded as default by main.py +# Please follow the provided structure (this will ensuring all baseline follow +# a similar configuration structure and hence be easy to customise) + +model: + _target_: models.StackedLSTM + +client_resources: + num_cpus: 4 + num_gpus: 1.0 + +num_rounds: 400 +data: shakespeare +support_ratio: 0.2 +batch_size: 10 +gradient_step: 1 From 1241e96f20a1b56466743d3a484bac60cd60c091 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Mon, 2 Oct 2023 03:29:06 +0900 Subject: [PATCH 086/133] change name lowercase --- .../FedMeta/Fedmeta_client_manager.py | 0 baselines/{FedMeta => fedmeta}/FedMeta/__init__.py | 0 baselines/{FedMeta => fedmeta}/FedMeta/client.py | 0 .../FedMeta/conf/algo/fedavg.yaml | 0 .../FedMeta/conf/algo/fedavg_meta.yaml | 0 .../FedMeta/conf/algo/fedmeta_maml.yaml | 0 .../FedMeta/conf/algo/fedmeta_meta_sgd.yaml | 0 .../{FedMeta => fedmeta}/FedMeta/conf/config.yaml | 0 .../FedMeta/conf/data/femnist.yaml | 0 .../FedMeta/conf/data/shakespeare.yaml | 0 baselines/{FedMeta => fedmeta}/FedMeta/dataset.py | 0 .../FedMeta/dataset_preparation.py | 0 .../FedMeta/femnist/graph_params/result_graph.png | Bin baselines/{FedMeta => fedmeta}/FedMeta/main.py | 0 baselines/{FedMeta => fedmeta}/FedMeta/models.py | 0 baselines/{FedMeta => fedmeta}/FedMeta/server.py | 0 .../shakespeare/graph_params/result_graph.png | Bin baselines/{FedMeta => fedmeta}/FedMeta/strategy.py | 0 baselines/{FedMeta => fedmeta}/FedMeta/utils.py | 0 baselines/{FedMeta => fedmeta}/LICENSE | 0 baselines/{FedMeta => fedmeta}/README.md | 0 .../docs/femnist_result_graph.png | Bin .../docs/shakespeare_result_graph.png | Bin baselines/{FedMeta => fedmeta}/pyproject.toml | 0 24 files changed, 0 insertions(+), 0 deletions(-) rename baselines/{FedMeta => 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baselines/fedmeta/fedmeta/server.py diff --git a/baselines/fedmeta/FedMeta/shakespeare/graph_params/result_graph.png b/baselines/fedmeta/fedmeta/shakespeare/graph_params/result_graph.png similarity index 100% rename from baselines/fedmeta/FedMeta/shakespeare/graph_params/result_graph.png rename to baselines/fedmeta/fedmeta/shakespeare/graph_params/result_graph.png diff --git a/baselines/fedmeta/FedMeta/strategy.py b/baselines/fedmeta/fedmeta/strategy.py similarity index 100% rename from baselines/fedmeta/FedMeta/strategy.py rename to baselines/fedmeta/fedmeta/strategy.py diff --git a/baselines/fedmeta/FedMeta/utils.py b/baselines/fedmeta/fedmeta/utils.py similarity index 100% rename from baselines/fedmeta/FedMeta/utils.py rename to baselines/fedmeta/fedmeta/utils.py From 2fb5ef80aa29151d409cbcbc59f466419d3ccd9f Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Mon, 2 Oct 2023 03:33:37 +0900 Subject: [PATCH 088/133] delete annotation --- baselines/fedmeta/fedmeta/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/baselines/fedmeta/fedmeta/utils.py b/baselines/fedmeta/fedmeta/utils.py index 6e8759f3075d..f8ab08ba66c6 100644 --- a/baselines/fedmeta/fedmeta/utils.py +++ b/baselines/fedmeta/fedmeta/utils.py @@ -121,7 +121,6 @@ def plot_from_pkl(directory="."): "fedavg_meta.pkl": "blue", "fedmeta_maml.pkl": "orange", "fedmeta_meta_sgd.pkl": "red", - # ... 여기에 추가 파일 이름과 색상을 매핑 ... } pkl_files = [f for f in os.listdir(directory) if f.endswith('.pkl')] From dec7c7a09e413ee9b21dc8866aedb0e8ff441a5b Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Wed, 4 Oct 2023 09:20:44 +0900 Subject: [PATCH 089/133] Update baselines/fedmeta/README.md remove annotation Co-authored-by: Javier --- baselines/fedmeta/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 321827839f31..7903db192f9f 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -1,8 +1,8 @@ --- title: Federated Meta-Learning with Fast Convergence and Efficient Communication url: https://arxiv.org/abs/1802.07876 -labels: [meta learning, maml, meta-sgd, personalization] # please add between 4 and 10 single-word (maybe two-words) labels (e.g. "system heterogeneity", "image classification", "asynchronous", "weight sharing", "cross-silo") -dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline +labels: [meta learning, maml, meta-sgd, personalization] +dataset: [FEMNIST, SHAKESPEARE] --- # FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication From 274a14503d092fd6ec636c2becd316a377ae5a09 Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Wed, 4 Oct 2023 09:21:44 +0900 Subject: [PATCH 090/133] Update baselines/fedmeta/README.md Co-authored-by: Javier --- baselines/fedmeta/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 7903db192f9f..1830cb30df66 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -7,7 +7,7 @@ dataset: [FEMNIST, SHAKESPEARE] # FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication -****Paper:**** : https://arxiv.org/abs/1802.07876 +****Paper:**** : [arxiv.org/abs/1802.07876](https://arxiv.org/abs/1802.07876) ****Authors:**** :Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, Xiuqiang He From cb7e9a7df6abe0c5fc5d4c2e78876f6f8f35a33c Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Wed, 4 Oct 2023 09:22:14 +0900 Subject: [PATCH 091/133] Update baselines/fedmeta/pyproject.toml Co-authored-by: Javier --- baselines/fedmeta/pyproject.toml | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml index 013db24e8871..d40378cf9519 100644 --- a/baselines/fedmeta/pyproject.toml +++ b/baselines/fedmeta/pyproject.toml @@ -37,10 +37,13 @@ classifiers = [ ] [tool.poetry.dependencies] -python = ">=3.8.15, <3.12.0" # don't change this -flwr = "1.3.0" # don't change this -ray = "1.11.1" # don't change this +python = ">=3.10.0, <3.11.0" +flwr = { extras = ["simulation"], version = "1.5.0" } hydra-core = "1.3.2" # don't change this +matplotlib = "3.7.1" +torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl"} +torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.14.1%2Bcu116-cp310-cp310-linux_x86_64.whl"} +scikit-learn = "^1.3.1" [tool.poetry.dev-dependencies] isort = "==5.11.5" From 62ed0010e2f0cbb7de47ccb8fd3434d873766b9d Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Wed, 4 Oct 2023 09:22:31 +0900 Subject: [PATCH 092/133] Update baselines/fedmeta/README.md Co-authored-by: Javier --- baselines/fedmeta/README.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 1830cb30df66..7f19869b29d1 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -65,9 +65,6 @@ dataset: [FEMNIST, SHAKESPEARE] #Environment Setup Poetry install Poetry shell -Pip install torch torch vision -Pip install matplotlib -pip install scikit-learn ``` ## Running the Experiments From f89b38938ab9ca45a52debdf49b42e3fea3cd5b3 Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Wed, 4 Oct 2023 09:22:43 +0900 Subject: [PATCH 093/133] Update baselines/fedmeta/README.md Co-authored-by: Javier --- baselines/fedmeta/README.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 7f19869b29d1..62a6807a48fe 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -63,7 +63,15 @@ dataset: [FEMNIST, SHAKESPEARE] ## Environment Setup ```bash #Environment Setup -Poetry install +# Set python version +pyenv install 3.10.6 +pyenv local 3.10.6 + +# Tell poetry to use python 3.10 +poetry env use 3.10.6 + +# install the base Poetry environment +poetry install Poetry shell ``` From 55f2bd88f579f42e6414cb558e407881a4a3da0d Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Wed, 4 Oct 2023 09:30:06 +0900 Subject: [PATCH 094/133] Update baselines/fedmeta/README.md Co-authored-by: Javier --- baselines/fedmeta/README.md | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 62a6807a48fe..c7b45c030610 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -50,14 +50,10 @@ dataset: [FEMNIST, SHAKESPEARE] | Algorithm | Dataset | Clients per Round | Number of Rounds | Batch Size | Optimizer | Learning Rate(α, β) | Client Resources | Gradient Step | |:-----------------:|:--------------:|:-----------------:|:----------------:|:----------:|:---------:|:-------------------:|--------------------------------------|:-------------:| -| FedAvg | FEMNST | 4 | 2000 | 10 | Adam | 0.0001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | -| FedAVg | SHAKESPEARE | 4 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | -| FedAvg(Meta) | FEMNST | 4 | 2000 | 10 | Adam | 0.0001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | -| FedAvg(Meta) | SHAKESPEARE | 4 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | -| FedMeta(MAML) | FEMNST | 4 | 2000 | 10 | Adam | (0.001, 0.0001) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 5 | -| FedMeta(MAML) | SHAKESPEARE | 4 | 400 | 10 | Adam | (0.1, 0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 1 | -| FedMeta(Meta-SGD | FEMNST | 4 | 2000 | 10 | Adam | (0.001, 0.0001) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 5 | -| FedMeta(Meta-SGD | SHAKESPEARE | 4 | 400 | 10 | Adam | (0.1, 0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 1 | +| FedAvg | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | 0.0001
    0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | +| FedAvg(Meta) | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | 0.0001
    0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | +| FedMeta(MAML) | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | (0.001, 0.0001)
    (0.1, 0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 5
    1 | +| FedMeta(Meta-SGD) | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | (0.001, 0.0001)
    (0.1, 0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 5
    1 | ## Environment Setup From 336cf3faea6c133fd0cceb364dec8d15be19d15f Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 4 Oct 2023 09:59:56 +0900 Subject: [PATCH 095/133] rename file name --- ...{Fedmeta_client_manager.py => fedmeta_client_manager.py} | 4 ++-- baselines/fedmeta/fedmeta/main.py | 6 +++--- baselines/fedmeta/fedmeta/strategy.py | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) rename baselines/fedmeta/fedmeta/{Fedmeta_client_manager.py => fedmeta_client_manager.py} (95%) diff --git a/baselines/fedmeta/fedmeta/Fedmeta_client_manager.py b/baselines/fedmeta/fedmeta/fedmeta_client_manager.py similarity index 95% rename from baselines/fedmeta/fedmeta/Fedmeta_client_manager.py rename to baselines/fedmeta/fedmeta/fedmeta_client_manager.py index 2b472e13a9e7..f0e08aa2750b 100644 --- a/baselines/fedmeta/fedmeta/Fedmeta_client_manager.py +++ b/baselines/fedmeta/fedmeta/fedmeta_client_manager.py @@ -32,12 +32,12 @@ def select( return [str(result) for result in range(0, valid_client)] -class Fedmeta_client_manager(SimpleClientManager): +class fedmeta_client_manager(SimpleClientManager): """ In the fit phase, clients must be sampled from the training client list. And in the evaluate stage, clients must be sampled from the validation client list. - So we modify 'Fedmeta_client_manager' to sample clients from [cid: List] for each list. + So we modify 'fedmeta_client_manager' to sample clients from [cid: List] for each list. """ diff --git a/baselines/fedmeta/fedmeta/main.py b/baselines/fedmeta/fedmeta/main.py index b3bd6dfc2758..250cde1f655f 100644 --- a/baselines/fedmeta/fedmeta/main.py +++ b/baselines/fedmeta/fedmeta/main.py @@ -8,9 +8,9 @@ from hydra.core.hydra_config import HydraConfig from omegaconf import DictConfig, OmegaConf from hydra.utils import instantiate -from strategy import weighted_average +from fedmeta.strategy import weighted_average from dataset import load_datasets -from Fedmeta_client_manager import Fedmeta_client_manager +from fedmeta.fedmeta_client_manager import fedmeta_client_manager import os import flwr as fl import client @@ -65,7 +65,7 @@ def main(cfg: DictConfig) -> None: "num_cpus": cfg.data.client_resources.num_cpus, "num_gpus": cfg.data.client_resources.num_gpus, }, - client_manager=Fedmeta_client_manager(valid_client=len(valloaders['qry'])), + client_manager=fedmeta_client_manager(valid_client=len(valloaders['qry'])), strategy=strategy, ) diff --git a/baselines/fedmeta/fedmeta/strategy.py b/baselines/fedmeta/fedmeta/strategy.py index 6042cb0246a8..7d77b81186a9 100644 --- a/baselines/fedmeta/fedmeta/strategy.py +++ b/baselines/fedmeta/fedmeta/strategy.py @@ -11,7 +11,7 @@ from flwr.server.strategy import FedAvg from flwr.server.strategy.aggregate import aggregate, weighted_loss_avg from flwr.server.client_manager import ClientManager -from Fedmeta_client_manager import evaluate_client_Criterion +from fedmeta_client_manager import evaluate_client_Criterion import torch from models import CNN_network, StackedLSTM from utils import update_ema From 3ad860895bf6617f9228b8e9b3553572be8b495f Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 4 Oct 2023 10:54:54 +0900 Subject: [PATCH 096/133] python main.py algo=fedavg data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data --> python -m fedmeta.main algo=fedavg data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data --- baselines/fedmeta/fedmeta/client.py | 2 +- baselines/fedmeta/fedmeta/conf/config.yaml | 2 +- baselines/fedmeta/fedmeta/conf/data/femnist.yaml | 5 +++-- .../fedmeta/fedmeta/conf/data/shakespeare.yaml | 2 +- baselines/fedmeta/fedmeta/dataset.py | 10 ++++++---- baselines/fedmeta/fedmeta/main.py | 16 +++++++++------- baselines/fedmeta/fedmeta/models.py | 1 + baselines/fedmeta/fedmeta/strategy.py | 12 +++++++----- 8 files changed, 29 insertions(+), 21 deletions(-) diff --git a/baselines/fedmeta/fedmeta/client.py b/baselines/fedmeta/fedmeta/client.py index 72e6e672a306..806583665d42 100644 --- a/baselines/fedmeta/fedmeta/client.py +++ b/baselines/fedmeta/fedmeta/client.py @@ -13,7 +13,7 @@ import torch.nn from torch.utils.data import DataLoader -from models import train, test, train_meta, test_meta +from fedmeta.models import train, test, train_meta, test_meta class FlowerClient( diff --git a/baselines/fedmeta/fedmeta/conf/config.yaml b/baselines/fedmeta/fedmeta/conf/config.yaml index 94b68c515e9a..db1bfb11f169 100644 --- a/baselines/fedmeta/fedmeta/conf/config.yaml +++ b/baselines/fedmeta/fedmeta/conf/config.yaml @@ -13,7 +13,7 @@ defaults: - data: ??? strategy: - _target_: strategy.FedMeta + _target_: fedmeta.strategy.FedMeta fraction_fit: 0.00001 fraction_evaluate: 0.00001 min_fit_clients : ${clients_per_round} diff --git a/baselines/fedmeta/fedmeta/conf/data/femnist.yaml b/baselines/fedmeta/fedmeta/conf/data/femnist.yaml index 6ad6b64498fa..463e3de613bf 100644 --- a/baselines/fedmeta/fedmeta/conf/data/femnist.yaml +++ b/baselines/fedmeta/fedmeta/conf/data/femnist.yaml @@ -4,13 +4,14 @@ # a similar configuration structure and hence be easy to customise) model: - _target_: models.CNN_network # model config + _target_: fedmeta.models.CNN_network # model config client_resources: num_cpus: 4 num_gpus: 0.25 -num_rounds: 2000 +#num_rounds: 2000 +num_rounds: 3 data: femnist support_ratio: 0.2 batch_size: 10 diff --git a/baselines/fedmeta/fedmeta/conf/data/shakespeare.yaml b/baselines/fedmeta/fedmeta/conf/data/shakespeare.yaml index 36c4744669b4..300e11da77f6 100644 --- a/baselines/fedmeta/fedmeta/conf/data/shakespeare.yaml +++ b/baselines/fedmeta/fedmeta/conf/data/shakespeare.yaml @@ -4,7 +4,7 @@ # a similar configuration structure and hence be easy to customise) model: - _target_: models.StackedLSTM + _target_: fedmeta.models.StackedLSTM client_resources: num_cpus: 4 diff --git a/baselines/fedmeta/fedmeta/dataset.py b/baselines/fedmeta/fedmeta/dataset.py index 08b6de853742..db11e2324f09 100644 --- a/baselines/fedmeta/fedmeta/dataset.py +++ b/baselines/fedmeta/fedmeta/dataset.py @@ -9,14 +9,16 @@ defined here of course. """ -from torch.utils.data import DataLoader, Dataset from omegaconf import DictConfig from typing import Tuple -from dataset_preparation import _partition_data, split_train_validation_test_clients + import numpy as np -import torchvision.transforms as transforms -from utils import word_to_indices, letter_to_vec import torch +import torchvision.transforms as transforms +from torch.utils.data import DataLoader, Dataset + +from fedmeta.dataset_preparation import _partition_data, split_train_validation_test_clients +from fedmeta.utils import word_to_indices, letter_to_vec class ShakespeareDataset(Dataset): diff --git a/baselines/fedmeta/fedmeta/main.py b/baselines/fedmeta/fedmeta/main.py index 250cde1f655f..49f454b43bc9 100644 --- a/baselines/fedmeta/fedmeta/main.py +++ b/baselines/fedmeta/fedmeta/main.py @@ -8,13 +8,15 @@ from hydra.core.hydra_config import HydraConfig from omegaconf import DictConfig, OmegaConf from hydra.utils import instantiate -from fedmeta.strategy import weighted_average -from dataset import load_datasets -from fedmeta.fedmeta_client_manager import fedmeta_client_manager import os + import flwr as fl -import client -from utils import save_graph_params, plot_from_pkl + +import fedmeta.client as client +from fedmeta.fedmeta_client_manager import fedmeta_client_manager +from fedmeta.dataset import load_datasets +from fedmeta.strategy import weighted_average +from fedmeta.utils import save_graph_params, plot_from_pkl @hydra.main(config_path="conf", config_name="config", version_base=None) @@ -81,7 +83,7 @@ def main(cfg: DictConfig) -> None: print("................") print(history) - output_path = HydraConfig.get().runtime.cwd + '/' + cfg.data.data + '/graph_params' + output_path = HydraConfig.get().runtime.cwd + '/fedmeta/' + cfg.data.data + '/graph_params' os.makedirs(output_path, exist_ok=True) data_params = { @@ -93,7 +95,7 @@ def main(cfg: DictConfig) -> None: } save_graph_params(data_params) - plot_from_pkl(directory=f"./{cfg.data.data}/graph_params") + plot_from_pkl(directory=output_path) print("................") diff --git a/baselines/fedmeta/fedmeta/models.py b/baselines/fedmeta/fedmeta/models.py index 51f28e1f7e52..13183d891202 100644 --- a/baselines/fedmeta/fedmeta/models.py +++ b/baselines/fedmeta/fedmeta/models.py @@ -11,6 +11,7 @@ import torch import torch.nn as nn from torch.utils.data import DataLoader + from copy import deepcopy diff --git a/baselines/fedmeta/fedmeta/strategy.py b/baselines/fedmeta/fedmeta/strategy.py index 7d77b81186a9..22dc4df84dd6 100644 --- a/baselines/fedmeta/fedmeta/strategy.py +++ b/baselines/fedmeta/fedmeta/strategy.py @@ -4,17 +4,19 @@ extend or modify the functionality of an existing strategy. """ from typing import Dict, List, Optional, Tuple, Union -from logging import WARNING, INFO +from logging import WARNING from collections import OrderedDict +import torch + from flwr.server.client_proxy import ClientProxy from flwr.server.strategy import FedAvg from flwr.server.strategy.aggregate import aggregate, weighted_loss_avg from flwr.server.client_manager import ClientManager -from fedmeta_client_manager import evaluate_client_Criterion -import torch -from models import CNN_network, StackedLSTM -from utils import update_ema + +from fedmeta.models import CNN_network, StackedLSTM +from fedmeta.fedmeta_client_manager import evaluate_client_Criterion +from fedmeta.utils import update_ema from flwr.common.logger import log From f743f17d72318b3dc9b9b12ba127ca6b6699716d Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Wed, 4 Oct 2023 10:55:14 +0900 Subject: [PATCH 097/133] Update baselines/fedmeta/README.md Co-authored-by: Javier --- baselines/fedmeta/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index c7b45c030610..34855738c87f 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -85,7 +85,7 @@ Poetry shell ****Start experiments**** : ```bash # FedAvg + Femnist Dataset -python main.py algo=fedavg data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data +python -m fedmeta.main algo=fedavg data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data # FedAvg(Meta) + Femnist Dataset python main.py algo=fedavg_meta data=femnist path=../leaf/data/shakespeare/data From 1bcd4d421703b2a20911ddd2dc101d47b222fc14 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 4 Oct 2023 10:57:22 +0900 Subject: [PATCH 098/133] Update README.md --- baselines/fedmeta/README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 34855738c87f..faebf0ebab88 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -88,27 +88,27 @@ Poetry shell python -m fedmeta.main algo=fedavg data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data # FedAvg(Meta) + Femnist Dataset -python main.py algo=fedavg_meta data=femnist path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedavg_meta data=femnist path=../leaf/data/shakespeare/data # FedMeta(MAML) + Femnist Dataset -python main.py algo=fedmeta_maml data=femnist path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedmeta_maml data=femnist path=../leaf/data/shakespeare/data # FedMeta(Meta-SGD) + Femnist Dataset -python main.py algo=fedmeta_meta_sgd data=femnist path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedmeta_meta_sgd data=femnist path=../leaf/data/shakespeare/data #FedAvg + Shakespeare Dataset -python main.py algo=fedavg data=shakespeare path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedavg data=shakespeare path=../leaf/data/shakespeare/data #FedAvg(Meta) + Shakespeare Dataset -python main.py algo=fedavg_meta data=shakespeare path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedavg_meta data=shakespeare path=../leaf/data/shakespeare/data #FedMeta(MAML) + Shakespeare Dataset -python main.py algo=fedmeta_maml data=shakespeare path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedmeta_maml data=shakespeare path=../leaf/data/shakespeare/data #FedMeta(Meta-SGD) + Shakespeare Dataset -python main.py algo=fedmeta_meta_sgd data=shakespeare path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedmeta_meta_sgd data=shakespeare path=../leaf/data/shakespeare/data ``` From e02d6bd4b96b52a3c2423d95346988de22713748 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 4 Oct 2023 11:06:14 +0900 Subject: [PATCH 099/133] rename docs --> _static --- baselines/fedmeta/README.md | 4 ++-- .../{docs => _static}/femnist_result_graph.png | Bin .../{docs => _static}/shakespeare_result_graph.png | Bin baselines/fedmeta/fedmeta/conf/data/femnist.yaml | 3 +-- 4 files changed, 3 insertions(+), 4 deletions(-) rename baselines/fedmeta/{docs => _static}/femnist_result_graph.png (100%) rename baselines/fedmeta/{docs => _static}/shakespeare_result_graph.png (100%) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index faebf0ebab88..73ba98f18767 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -119,10 +119,10 @@ If you proceed with all of the above experiments, You can get a graph of your ex **Femnist dataset experiment results** -![](docs/femnist_result_graph.png) +![](_static/femnist_result_graph.png) **Shakespeare dataset experiment results** -![](docs/shakespeare_result_graph.png) +![](_static/shakespeare_result_graph.png) diff --git a/baselines/fedmeta/docs/femnist_result_graph.png b/baselines/fedmeta/_static/femnist_result_graph.png similarity index 100% rename from baselines/fedmeta/docs/femnist_result_graph.png rename to baselines/fedmeta/_static/femnist_result_graph.png diff --git a/baselines/fedmeta/docs/shakespeare_result_graph.png b/baselines/fedmeta/_static/shakespeare_result_graph.png similarity index 100% rename from baselines/fedmeta/docs/shakespeare_result_graph.png rename to baselines/fedmeta/_static/shakespeare_result_graph.png diff --git a/baselines/fedmeta/fedmeta/conf/data/femnist.yaml b/baselines/fedmeta/fedmeta/conf/data/femnist.yaml index 463e3de613bf..d43f8dabb43f 100644 --- a/baselines/fedmeta/fedmeta/conf/data/femnist.yaml +++ b/baselines/fedmeta/fedmeta/conf/data/femnist.yaml @@ -10,8 +10,7 @@ client_resources: num_cpus: 4 num_gpus: 0.25 -#num_rounds: 2000 -num_rounds: 3 +num_rounds: 2000 data: femnist support_ratio: 0.2 batch_size: 10 From ff1b58cd926d0e02fb6561567ee188ae25e7a660 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 4 Oct 2023 11:34:40 +0900 Subject: [PATCH 100/133] Add grid line plot --- baselines/fedmeta/README.md | 13 ++------ .../fedmeta/_static/femnist_result_graph.png | Bin 151963 -> 157388 bytes .../_static/shakespeare_result_graph.png | Bin 108181 -> 112802 bytes .../femnist/graph_params/result_graph.png | Bin 151963 -> 157388 bytes .../shakespeare/graph_params/result_graph.png | Bin 117804 -> 112802 bytes baselines/fedmeta/fedmeta/utils.py | 28 ++++++++++++++++-- 6 files changed, 28 insertions(+), 13 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 73ba98f18767..c30bb09a5fc9 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -116,13 +116,6 @@ python -m fedmeta.main algo=fedmeta_meta_sgd data=shakespeare path=../leaf/data/ ## Expected Results If you proceed with all of the above experiments, You can get a graph of your experiment results as shown below along that `./femnist or shakespeare/graph_params/result_graph.png`. -**Femnist dataset experiment results** - - -![](_static/femnist_result_graph.png) - - -**Shakespeare dataset experiment results** - - -![](_static/shakespeare_result_graph.png) +| FEMNIST | SHAKESPEARE | +|:-------------------------------------------:|:----------------------------------------------------:| +| ![](_static/femnist_result_graph.png) | ![](_static/shakespeare_result_graph.png) | diff --git a/baselines/fedmeta/_static/femnist_result_graph.png b/baselines/fedmeta/_static/femnist_result_graph.png index d03f43dcfa848246c4bedcf07a6a0deeb51aa990..c66618ebfc40350c220c3f205d4970ddd4d3766c 100644 GIT binary patch literal 157388 zcmb@tWn5KT_cpvXo00|zX#}N1kPZP!kyNA`k#3}=y9Mc#MjE6Wqy$On+L9tjD-G}5 z=iK-IdEO82`+RxM&vQJ$X00{X9P=92xW+ZYRh8v%u_&-02*Q0PFZ}|7P+B1fwF(m* ze22U^6$ShyE=%SimY!% z>3-W&y3YhwsM~D}m@iP@8w6uw71QO=%wAIYL7DxzP~#7O6`a4@iFL~Q)$2d!fcT|@u(L}qEOVS`9Cl60xQZX6Nl{q*(?3%B$ zj`T`a9~>B9-qS~3f(YZErI?~w&r}2_CzBcYUcWurUom`jz{td8dbB=xaC#aV7zk&5 z^(lyuL6(l2d;GJqh6d@IZ)w3gbyi*Djm&o 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z0%Mm1_OPo-?LPKy?FTbc2HngB4BMA(Mq*yP*MBqW1{fbmwCwB-9A}sj4`}+;5|}TK pnoS$nE}yO>VVifa|HpTew&Lqs^{;;w+ezWI)!xyr@Kdk2zX3k?W^(`l diff --git a/baselines/fedmeta/fedmeta/utils.py b/baselines/fedmeta/fedmeta/utils.py index f8ab08ba66c6..592f1aae3742 100644 --- a/baselines/fedmeta/fedmeta/utils.py +++ b/baselines/fedmeta/fedmeta/utils.py @@ -132,10 +132,32 @@ def plot_from_pkl(directory="."): data = pickle.load(f) all_data[file] = data - plt.figure(figsize=(14, 6)) + # plt.figure(figsize=(14, 6)) + # + # # Acc graph + # plt.subplot(1, 2, 1) + # for file, data in all_data.items(): + # accuracies = [acc for _, acc in data["accuracy"]['accuracy']] + # legend_ = file[:-4] if file.endswith('.pkl') else file + # plt.plot(accuracies, label=legend_, color=color_mapping.get(file, "black")) + # plt.title("Accuracy") + # plt.legend() + # + # # Loss graph + # plt.subplot(1, 2, 2) + # for file, data in all_data.items(): + # loss = [loss for _, loss in data["loss"]] + # legend_ = file[:-4] if file.endswith('.pkl') else file + # plt.plot(loss, label=legend_, color=color_mapping.get(file, "black")) + # plt.title("Loss") + # plt.legend() + # + # plt.tight_layout() + + plt.figure(figsize=(7, 12)) # figsize 변경 # Acc graph - plt.subplot(1, 2, 1) + plt.subplot(2, 1, 1) # 변경: 첫 번째 인자를 2로, 두 번째 인자를 1로 설정 for file, data in all_data.items(): accuracies = [acc for _, acc in data["accuracy"]['accuracy']] legend_ = file[:-4] if file.endswith('.pkl') else file @@ -144,7 +166,7 @@ def plot_from_pkl(directory="."): plt.legend() # Loss graph - plt.subplot(1, 2, 2) + plt.subplot(2, 1, 2) # 변경: 첫 번째 인자를 2로, 두 번째 인자를 1로 설정 for file, data in all_data.items(): loss = [loss for _, loss in data["loss"]] legend_ = file[:-4] if file.endswith('.pkl') else file From c841b296d8e36fd691865990305b910c00fabb04 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 4 Oct 2023 12:46:02 +0900 Subject: [PATCH 101/133] Update README.md --- baselines/fedmeta/README.md | 28 +++++++++++++++++++++++----- 1 file changed, 23 insertions(+), 5 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index c30bb09a5fc9..1d09bafbd51d 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -39,10 +39,10 @@ dataset: [FEMNIST, SHAKESPEARE] **Shakespeare Dataset Issue** : In the FedMeta paper experiment, the Shakespeare dataset had 1126 users. However, due to a current bug, the number of users has decreased to 660 users. Therefore, we have only maintained the total number of data. -| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | -|:-----------:|:----------:| :---: |:--------:|:------------------------------------------------------------:|----------------------| -| FEMNIST | 1,068 | 235,683 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | -| SHAKESPEARE | 550 -> 110 | 625,127 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | +| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | +|:-----------:|:-------------------:|:----------------:|:--------:|:------------------------------------------------------------:|----------------------| +| FEMNIST | About
    1050 | About
    240,000 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | +| SHAKESPEARE | About
    550 -> 110 | About
    620,000 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | **The original specifications of the Leaf dataset can be found in the Leaf paper(_"LEAF: A Benchmark for Federated Settings"_).** @@ -75,13 +75,31 @@ Poetry shell ****Download Dataset**** : Go [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf) and Use the command below! You can download dataset (FEMNIST and SHAKESPEARE). ```bash +# clone LEAF repo +git clone https://github.com/TalwalkarLab/leaf.git +# prepare the Leaf GitHub libraries. +pip3 install numpy +pip3 install pillow + +# navigate to data directory and then the dataset +cd leaf/data/femnist #FEMNIST dataset Download command for these experiments -./preprocess.sh -s niid --iu 1068 --sf 0.3 -k 0 -t sample +./preprocess.sh -s niid --sf 0.3 -k 0 -t sample +# navigate to data directory and then the dataset +cd leaf/data/shakespeare #SHAKESEPEARE dataset Download command for these experiments ./preprocess.sh -s niid --sf 0.16 -k 0 -t sample ``` +*Run `./preprocess.sh` with a choice of the following tags* +* `-s` := 'iid' to sample in an i.i.d. manner, or 'niid' to sample in a non-i.i.d. manner; more information on i.i.d. versus non-i.i.d. is included in the 'Notes' section +* `--sf` := fraction of data to sample, written as a decimal; default is 0.1 +* `-k` := minimum number of samples per user +* `-t` := 'user' to partition users into train-test groups, or 'sample' to partition each user's samples into train-test groups + +More detailed tag information can be found on Leaf GitHub. + ****Start experiments**** : ```bash # FedAvg + Femnist Dataset From 268e1b821cdd14276784e67834f6c405a5ee9c7b Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 4 Oct 2023 12:48:05 +0900 Subject: [PATCH 102/133] fix grid line in README.md --- baselines/fedmeta/README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 1d09bafbd51d..b0726b24292b 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -39,21 +39,21 @@ dataset: [FEMNIST, SHAKESPEARE] **Shakespeare Dataset Issue** : In the FedMeta paper experiment, the Shakespeare dataset had 1126 users. However, due to a current bug, the number of users has decreased to 660 users. Therefore, we have only maintained the total number of data. -| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | -|:-----------:|:-------------------:|:----------------:|:--------:|:------------------------------------------------------------:|----------------------| -| FEMNIST | About
    1050 | About
    240,000 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | -| SHAKESPEARE | About
    550 -> 110 | About
    620,000 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2, Qry : 0.8 | +| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | +|:-----------:|:-------------------:|:----------------:|:--------:|:------------------------------------------------------------:|:----------------------:| +| FEMNIST | About
    1050 | About
    240,000 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | +| SHAKESPEARE | About
    550 -> 110 | About
    620,000 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | **The original specifications of the Leaf dataset can be found in the Leaf paper(_"LEAF: A Benchmark for Federated Settings"_).** ****Training Hyperparameters:**** : The following table shows the main hyperparameters for this baseline with their default value (i.e. the value used if you run `python main.py algo=? data=?` directly) -| Algorithm | Dataset | Clients per Round | Number of Rounds | Batch Size | Optimizer | Learning Rate(α, β) | Client Resources | Gradient Step | -|:-----------------:|:--------------:|:-----------------:|:----------------:|:----------:|:---------:|:-------------------:|--------------------------------------|:-------------:| -| FedAvg | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | 0.0001
    0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | -| FedAvg(Meta) | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | 0.0001
    0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } | - | -| FedMeta(MAML) | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | (0.001, 0.0001)
    (0.1, 0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 5
    1 | -| FedMeta(Meta-SGD) | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | (0.001, 0.0001)
    (0.1, 0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } | 5
    1 | +| Algorithm | Dataset | Clients per Round | Number of Rounds | Batch Size | Optimizer | Learning Rate(α, β) | Client Resources | Gradient Step | +|:-----------------:|:--------------:|:-----------------:|:----------------:|:----------:|:---------:|:-------------------:|:---------------------------------------:|:-------------:| +| FedAvg | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | 0.0001
    0.001 | {'num_cpus': 4.0,
    'num_gpus': 0.25 } | - | +| FedAvg(Meta) | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | 0.0001
    0.001 | {'num_cpus': 4.0,
    'num_gpus': 0.25 } | - | +| FedMeta(MAML) | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | (0.001, 0.0001)
    (0.1, 0.01) | {'num_cpus': 4.0,
    'num_gpus': 1.0 } | 5
    1 | +| FedMeta(Meta-SGD) | FEMNIST
    SHAKESPEARE | 4 | 2000
    400 | 10 | Adam | (0.001, 0.0001)
    (0.1, 0.01) | {'num_cpus': 4.0,
    'num_gpus': 1.0 } | 5
    1 | ## Environment Setup From c6697e87567e2a44bf4b99c9a07c7a05899b0303 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 4 Oct 2023 12:51:23 +0900 Subject: [PATCH 103/133] test README.md grid line --- baselines/fedmeta/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index b0726b24292b..b4756c99713e 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -39,10 +39,10 @@ dataset: [FEMNIST, SHAKESPEARE] **Shakespeare Dataset Issue** : In the FedMeta paper experiment, the Shakespeare dataset had 1126 users. However, due to a current bug, the number of users has decreased to 660 users. Therefore, we have only maintained the total number of data. -| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | -|:-----------:|:-------------------:|:----------------:|:--------:|:------------------------------------------------------------:|:----------------------:| -| FEMNIST | About
    1050 | About
    240,000 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | -| SHAKESPEARE | About
    550 -> 110 | About
    620,000 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | +| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | +|:-----------:|:-------------------:|:----------------:|:--------:|:----------------------------------------------------------------:|:----------------------:| +| FEMNIST | About
    1050 | About
    240,000 | 62 | Train Clients : 0.8,
    Valid Clients : 0.1,
    Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | +| SHAKESPEARE | About
    550 -> 110 | About
    620,000 | 80 | Train Clients : 0.8,
    Valid Clients : 0.1,
    Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | **The original specifications of the Leaf dataset can be found in the Leaf paper(_"LEAF: A Benchmark for Federated Settings"_).** From 7860b5015d683bebad89a9e84bf69e5c2b226bd0 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Wed, 4 Oct 2023 12:54:30 +0900 Subject: [PATCH 104/133] fixed feedback --- baselines/fedmeta/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index b4756c99713e..348a724cfead 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -39,10 +39,10 @@ dataset: [FEMNIST, SHAKESPEARE] **Shakespeare Dataset Issue** : In the FedMeta paper experiment, the Shakespeare dataset had 1126 users. However, due to a current bug, the number of users has decreased to 660 users. Therefore, we have only maintained the total number of data. -| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | -|:-----------:|:-------------------:|:----------------:|:--------:|:----------------------------------------------------------------:|:----------------------:| -| FEMNIST | About
    1050 | About
    240,000 | 62 | Train Clients : 0.8,
    Valid Clients : 0.1,
    Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | -| SHAKESPEARE | About
    550 -> 110 | About
    620,000 | 80 | Train Clients : 0.8,
    Valid Clients : 0.1,
    Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | +| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | +|:-----------:|:-------------------:|:----------------:|:--------:|:---------------------------------------------------------------:|:----------------------:| +| FEMNIST | About
    1050 | About
    240,000 | 62 | Train Clients : 0.8
    Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | +| SHAKESPEARE | About
    550 -> 110 | About
    620,000 | 80 | Train Clients : 0.8
    Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | **The original specifications of the Leaf dataset can be found in the Leaf paper(_"LEAF: A Benchmark for Federated Settings"_).** From d831cb92202665cfaaa97b95001b2786a0ae614b Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 5 Oct 2023 13:37:37 +0900 Subject: [PATCH 105/133] fix annotation --- baselines/fedmeta/README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 348a724cfead..fe64c053146a 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -68,7 +68,10 @@ poetry env use 3.10.6 # install the base Poetry environment poetry install -Poetry shell +#if you have issue "Failed to unlock the collection!" +export PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring +#poetry start +poetry shell ``` ## Running the Experiments From a082665ecc1918385a49b4d876487b8e36052d99 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 5 Oct 2023 13:42:37 +0900 Subject: [PATCH 106/133] fix annotation --- baselines/fedmeta/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index fe64c053146a..cf9ce59dfb6c 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -106,16 +106,16 @@ More detailed tag information can be found on Leaf GitHub. ****Start experiments**** : ```bash # FedAvg + Femnist Dataset -python -m fedmeta.main algo=fedavg data=femnist path=(your leaf dataset path)/leaf/data/shakespeare/data +python -m fedmeta.main algo=fedavg data=femnist path=(your leaf dataset path)/leaf/data/femnist/data # FedAvg(Meta) + Femnist Dataset -python -m fedmeta.main algo=fedavg_meta data=femnist path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedavg_meta data=femnist path=../leaf/data/femnist/data # FedMeta(MAML) + Femnist Dataset -python -m fedmeta.main algo=fedmeta_maml data=femnist path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedmeta_maml data=femnist path=../leaf/data/femnist/data # FedMeta(Meta-SGD) + Femnist Dataset -python -m fedmeta.main algo=fedmeta_meta_sgd data=femnist path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedmeta_meta_sgd data=femnist path=../leaf/data/femnist/data From 89baf06196a69e5dc29942ccea411c10208ec131 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 5 Oct 2023 14:45:25 +0900 Subject: [PATCH 107/133] update ReadME.md - remove torch - remove torchvision - add poetry add torch torchvision --- baselines/fedmeta/README.md | 19 ++++++++----------- baselines/fedmeta/pyproject.toml | 2 -- 2 files changed, 8 insertions(+), 13 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index cf9ce59dfb6c..7c930bba3ce9 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -67,10 +67,7 @@ pyenv local 3.10.6 poetry env use 3.10.6 # install the base Poetry environment -poetry install -#if you have issue "Failed to unlock the collection!" -export PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring -#poetry start +poetry add torch torchvision poetry shell ``` @@ -109,27 +106,27 @@ More detailed tag information can be found on Leaf GitHub. python -m fedmeta.main algo=fedavg data=femnist path=(your leaf dataset path)/leaf/data/femnist/data # FedAvg(Meta) + Femnist Dataset -python -m fedmeta.main algo=fedavg_meta data=femnist path=../leaf/data/femnist/data +python -m fedmeta.main algo=fedavg_meta data=femnist path=./leaf/data/femnist/data # FedMeta(MAML) + Femnist Dataset -python -m fedmeta.main algo=fedmeta_maml data=femnist path=../leaf/data/femnist/data +python -m fedmeta.main algo=fedmeta_maml data=femnist path=./leaf/data/femnist/data # FedMeta(Meta-SGD) + Femnist Dataset -python -m fedmeta.main algo=fedmeta_meta_sgd data=femnist path=../leaf/data/femnist/data +python -m fedmeta.main algo=fedmeta_meta_sgd data=femnist path=./leaf/data/femnist/data #FedAvg + Shakespeare Dataset -python -m fedmeta.main algo=fedavg data=shakespeare path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedavg data=shakespeare path=./leaf/data/shakespeare/data #FedAvg(Meta) + Shakespeare Dataset -python -m fedmeta.main algo=fedavg_meta data=shakespeare path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedavg_meta data=shakespeare path=./leaf/data/shakespeare/data #FedMeta(MAML) + Shakespeare Dataset -python -m fedmeta.main algo=fedmeta_maml data=shakespeare path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedmeta_maml data=shakespeare path=./leaf/data/shakespeare/data #FedMeta(Meta-SGD) + Shakespeare Dataset -python -m fedmeta.main algo=fedmeta_meta_sgd data=shakespeare path=../leaf/data/shakespeare/data +python -m fedmeta.main algo=fedmeta_meta_sgd data=shakespeare path=./leaf/data/shakespeare/data ``` diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml index d40378cf9519..686a6577d0fd 100644 --- a/baselines/fedmeta/pyproject.toml +++ b/baselines/fedmeta/pyproject.toml @@ -41,8 +41,6 @@ python = ">=3.10.0, <3.11.0" flwr = { extras = ["simulation"], version = "1.5.0" } hydra-core = "1.3.2" # don't change this matplotlib = "3.7.1" -torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl"} -torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.14.1%2Bcu116-cp310-cp310-linux_x86_64.whl"} scikit-learn = "^1.3.1" [tool.poetry.dev-dependencies] From d4e31124cbae12b90bd3498cbf2382931fc48d53 Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Sat, 7 Oct 2023 13:27:40 +0900 Subject: [PATCH 108/133] Update baselines/fedmeta/pyproject.toml Co-authored-by: Javier --- baselines/fedmeta/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml index 686a6577d0fd..60ddbebb40ab 100644 --- a/baselines/fedmeta/pyproject.toml +++ b/baselines/fedmeta/pyproject.toml @@ -41,7 +41,7 @@ python = ">=3.10.0, <3.11.0" flwr = { extras = ["simulation"], version = "1.5.0" } hydra-core = "1.3.2" # don't change this matplotlib = "3.7.1" -scikit-learn = "^1.3.1" +scikit-learn = "1.3.1" [tool.poetry.dev-dependencies] isort = "==5.11.5" From bf7460ddda4ddebc717be940a2abf8089988da44 Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Sat, 7 Oct 2023 13:27:53 +0900 Subject: [PATCH 109/133] Update baselines/fedmeta/pyproject.toml Co-authored-by: Javier --- baselines/fedmeta/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml index 60ddbebb40ab..12faec181ece 100644 --- a/baselines/fedmeta/pyproject.toml +++ b/baselines/fedmeta/pyproject.toml @@ -47,7 +47,7 @@ scikit-learn = "1.3.1" isort = "==5.11.5" black = "==23.1.0" docformatter = "==1.5.1" -mypy = "==0.961" +mypy = "==1.4.1" pylint = "==2.8.2" flake8 = "==3.9.2" pytest = "==6.2.4" From 0fcea37eb5d0a6dbd52e1cb53c748c199dae1752 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sat, 7 Oct 2023 13:46:37 +0900 Subject: [PATCH 110/133] Update pyproject.toml Update README.md --- baselines/fedmeta/README.md | 8 ++++---- baselines/fedmeta/pyproject.toml | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 7c930bba3ce9..e2076ec149b7 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -39,10 +39,10 @@ dataset: [FEMNIST, SHAKESPEARE] **Shakespeare Dataset Issue** : In the FedMeta paper experiment, the Shakespeare dataset had 1126 users. However, due to a current bug, the number of users has decreased to 660 users. Therefore, we have only maintained the total number of data. -| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | -|:-----------:|:-------------------:|:----------------:|:--------:|:---------------------------------------------------------------:|:----------------------:| -| FEMNIST | About
    1050 | About
    240,000 | 62 | Train Clients : 0.8
    Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | -| SHAKESPEARE | About
    550 -> 110 | About
    620,000 | 80 | Train Clients : 0.8
    Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | +| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | +|:-----------:|:--------:|:--------:|:--------:|:---------------------------------------------------------------:|:----------------------:| +| FEMNIST | 1109 | 245,337 | 62 | Train Clients : 0.8
    Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | +| SHAKESPEARE | 138 | 646,697 | 80 | Train Clients : 0.8
    Valid Clients : 0.1, Test Clients : 0.1 | Sup : 0.2
    Qry : 0.8 | **The original specifications of the Leaf dataset can be found in the Leaf paper(_"LEAF: A Benchmark for Federated Settings"_).** diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml index 12faec181ece..6a9f74a8a298 100644 --- a/baselines/fedmeta/pyproject.toml +++ b/baselines/fedmeta/pyproject.toml @@ -3,11 +3,11 @@ requires = ["poetry-core>=1.4.0"] build-backend = "poetry.masonry.api" [tool.poetry] -name = "FedMeta" # <----- Ensure it matches the name of your baseline directory containing all the source code +name = "fedmeta" version = "1.0.0" -description = "Flower Baselines" +description = "Implementation of FedMeta (Fei Chen et al. 2018)" license = "Apache-2.0" -authors = ["The Flower Authors "] +authors = ["Jinsoo Kim ", "Kangyoon Lee "] readme = "README.md" homepage = "https://flower.dev" repository = "https://github.com/adap/flower" From 06733dd0103b959fd1c734ec0c87b287340b9067 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Sat, 7 Oct 2023 13:54:41 +0900 Subject: [PATCH 111/133] Add grid line in graph --- .../femnist/graph_params/result_graph.png | Bin 157388 -> 151054 bytes .../shakespeare/graph_params/result_graph.png | Bin 112802 -> 123180 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/baselines/fedmeta/fedmeta/femnist/graph_params/result_graph.png b/baselines/fedmeta/fedmeta/femnist/graph_params/result_graph.png index c66618ebfc40350c220c3f205d4970ddd4d3766c..935643b46f900da9f451635e9a5b4fce5d13c8eb 100644 GIT binary patch literal 151054 zcmc$`WmJ`I`#uOrDBU0+jUe6Kij;(;(jg$--K`+q-5}j1jnWO0o9^z;x%TtCzjx-t zf7Y6rHM8c!UTk;V`@Zk%jN?3xv4mK9n4i=^cJe9UN@z`B_*j z|N8-EYdaGbuFBeCaF7=^QlIT%U@-Kd57?g~1*R~tFfh{Z#8g~T_tRWcHLS9LcqW}0NN<~U3H=EKlFI(zkwuA=bEWJ#~H_9>NzG1z=M7HkzJGX?h zyS2d<`vm*?q{>I(k)GebTedugw>%D$nwp32&K{c=>7}Eo;o*Em$uXb9`!V2Nbdmb} z^BlZnBaG#z4t*W^T!IR|{6B9jhrEBT^Y1s{I`G0UoB#WDA_^CT|NKLpQ~VhDKL;-R z8nBG_&tb?hwZ@)zF#LN=@`mjHmw$lZ=6WsSW1)(}-qe8sh6ME7tumvjrGL%%LoaW@ 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""" criterion = torch.nn.CrossEntropyLoss() - + total_loss = 0.0 if algo == "fedavg_meta": optimizer = torch.optim.Adam( net.parameters(), lr=learning_rate, weight_decay=0.001 ) net.train() + optimizer.zero_grad() if data == "femnist": for images, labels in trainloader: images, labels = images.to(device), labels.to(device) loss = criterion(net(images), labels) - optimizer.zero_grad() loss.backward() - optimizer.step() - # total_loss += loss * labels.size(0) - # total_loss = total_loss / len(trainloader.dataset) - # optimizer.zero_grad() - # total_loss.backward() - # optimizer.step() + total_loss += loss * labels.size(0) + optimizer.step() elif data == "shakespeare": for images, labels in trainloader: @@ -332,6 +328,7 @@ def _train_meta_one_epoch( num_adaptation_steps = gradient_step train_net = deepcopy(net) alpha = [alpha.to(device) for alpha in alpha] + train_net.train() for _ in range(num_adaptation_steps): loss_sum = 0.0 sup_num_sample = [] diff --git 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znA^w;HWom@Jc=Ni;8Z#%|J$4+5ot0hl~EZ5Km7}^pXo7r9aF-M&1q5_3 z3(fTpry&sE2m@3QeZR1^$$CgV=%i=_!Vz=GakIG$P`Nf+ll}kdxBss Tuple[NDArrays, torch.nn.ParameterList]: """Update model parameters for FedMeta(Meta-SGD). @@ -80,11 +80,11 @@ def fedmeta_update_meta_sgd( def fedmeta_update_maml( - net: torch.nn.Module, - beta: float, - weights_results: NDArrays, - gradients_aggregated: NDArrays, - weight_decay: float, + net: torch.nn.Module, + beta: float, + weights_results: NDArrays, + gradients_aggregated: NDArrays, + weight_decay: float, ) -> NDArrays: """Update model parameters for FedMeta(Meta-SGD). @@ -109,7 +109,9 @@ def fedmeta_update_maml( params_dict = zip(net.state_dict().keys(), weights_results) state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) net.load_state_dict(state_dict, strict=True) - optimizer = torch.optim.Adam(list(net.parameters()), lr=beta, weight_decay=weight_decay) + optimizer = torch.optim.Adam( + list(net.parameters()), lr=beta, weight_decay=weight_decay + ) for params, grad_ins in zip(net.parameters(), gradients_aggregated): params.grad = torch.tensor(grad_ins).to(params.dtype) optimizer.step() @@ -164,7 +166,7 @@ def __init__(self, alpha, beta, data, algo, **kwargs): ) def configure_fit( - self, server_round: int, parameters: Parameters, client_manager: ClientManager + self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, FitIns]]: """Configure the next round of training.""" config = {"alpha": self.alpha, "algo": self.algo, "data": self.data} @@ -181,14 +183,14 @@ def configure_fit( num_clients=sample_size, min_num_clients=min_num_clients, server_round=server_round, - step='evaluate' + step="fit", ) # Return client/config pairs return [(client, fit_ins) for client in clients] def configure_evaluate( - self, server_round: int, parameters: Parameters, client_manager: ClientManager + self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, EvaluateIns]]: """Configure the next round of evaluation.""" # Do not configure federated evaluation if fraction eval is 0. @@ -210,17 +212,17 @@ def configure_evaluate( num_clients=sample_size, min_num_clients=min_num_clients, server_round=server_round, - step='evaluate' + step="evaluate", ) # Return client/config pairs return [(client, evaluate_ins) for client in clients] def aggregate_fit( - self, - server_round: int, - results: List[Tuple[ClientProxy, FitRes]], - failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]], + self, + server_round: int, + results: List[Tuple[ClientProxy, FitRes]], + failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]], ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]: """Aggregate fit results using weighted average.""" if not results: @@ -237,9 +239,9 @@ def aggregate_fit( parameters_aggregated = aggregate(weights_results) if self.data == "femnist": - weight_decay = 0.001 + weight_decay_ = 0.001 else: - weight_decay = 0.0001 + weight_decay_ = 0.0001 # Gradient Average and Update Parameter for FedMeta(MAML) if self.algo == "fedmeta_maml": @@ -249,14 +251,18 @@ def aggregate_fit( ] gradients_aggregated = aggregate(grads_results) weights_prime = fedmeta_update_maml( - self.net, self.beta, weights_results[0][0], gradients_aggregated, weight_decay + self.net, + self.beta, + weights_results[0][0], + gradients_aggregated, + weight_decay_, ) parameters_aggregated = weights_prime # Gradient Average and Update Parameter for FedMeta(Meta-SGD) elif self.algo == "fedmeta_meta_sgd": - grads_results = [ - (fit_res.metrics["grads"], fit_res.num_examples) # type: ignore + grads_results: List[Tuple[NDArrays, int]] = [ # type: ignore + (fit_res.metrics["grads"], fit_res.num_examples) for _, fit_res in results ] gradients_aggregated = aggregate(grads_results) @@ -266,7 +272,7 @@ def aggregate_fit( self.beta, weights_results[0][0], gradients_aggregated, - weight_decay, + weight_decay_, ) self.alpha = update_alpha parameters_aggregated = weights_prime @@ -282,10 +288,10 @@ def aggregate_fit( return ndarrays_to_parameters(parameters_aggregated), metrics_aggregated def aggregate_evaluate( - self, - server_round: int, - results: List[Tuple[ClientProxy, EvaluateRes]], - failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]], + self, + server_round: int, + results: List[Tuple[ClientProxy, EvaluateRes]], + failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]], ) -> Tuple[Optional[float], Dict[str, Scalar]]: """Aggregate evaluation losses using weighted average.""" if not results: From 0a4327d41c4393b2845bc42300967f89ff8207bd Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 12 Oct 2023 15:30:07 +0900 Subject: [PATCH 119/133] for pull origin/fedmeta --- baselines/fedmeta/fedmeta/client.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/baselines/fedmeta/fedmeta/client.py b/baselines/fedmeta/fedmeta/client.py index 6f7fea5d8a1b..c0750cb6f2ee 100644 --- a/baselines/fedmeta/fedmeta/client.py +++ b/baselines/fedmeta/fedmeta/client.py @@ -20,15 +20,15 @@ class FlowerClient(fl.client.NumPyClient): # pylint: disable=too-many-arguments def __init__( - self, - net: torch.nn.Module, - trainloaders: DataLoader, - valloaders: DataLoader, - cid: str, - device: torch.device, - num_epochs: int, - learning_rate: float, - gradient_step: int, + self, + net: torch.nn.Module, + trainloaders: DataLoader, + valloaders: DataLoader, + cid: str, + device: torch.device, + num_epochs: int, + learning_rate: float, + gradient_step: int, ): self.net = net self.trainloaders = trainloaders From a99da4e5cb950a0ec326c6b77d1d5fb9533ee2fc Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Thu, 12 Oct 2023 15:37:49 +0900 Subject: [PATCH 120/133] Update baselines/fedmeta/pyproject.toml Co-authored-by: Javier --- baselines/fedmeta/pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml index 5defe4b5f98d..2f7802d07234 100644 --- a/baselines/fedmeta/pyproject.toml +++ b/baselines/fedmeta/pyproject.toml @@ -42,6 +42,9 @@ flwr = { extras = ["simulation"], version = "1.5.0" } hydra-core = "1.3.2" # don't change this matplotlib = "3.7.1" scikit-learn = "1.3.1" +torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"} +torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"} + [tool.poetry.dev-dependencies] isort = "==5.11.5" From 9077f3fc02912219eae6af42c74674be0ad5a648 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 12 Oct 2023 15:39:04 +0900 Subject: [PATCH 121/133] Update README.md --- baselines/fedmeta/README.md | 1 + baselines/fedmeta/fedmeta/models.py | 1 + 2 files changed, 2 insertions(+) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 6f12e48385f0..84cb08487737 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -67,6 +67,7 @@ pyenv local 3.10.6 poetry env use 3.10.6 # install the base Poetry environment +poetry install poetry shell ``` diff --git a/baselines/fedmeta/fedmeta/models.py b/baselines/fedmeta/fedmeta/models.py index 3c1b543deb40..15cea691de45 100644 --- a/baselines/fedmeta/fedmeta/models.py +++ b/baselines/fedmeta/fedmeta/models.py @@ -223,6 +223,7 @@ def test( loss = criterion(net(images), labels) loss.backward() total_loss += loss * labels.size(0) + total_loss = total_loss / len(trainloader.dataset) optimizer.step() elif data == "shakespeare": From 5d24c330e5d82996194dff0a818eae33ff6329f4 Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Thu, 12 Oct 2023 16:03:32 +0900 Subject: [PATCH 122/133] Update baselines/fedmeta/fedmeta/main.py Co-authored-by: Javier --- baselines/fedmeta/fedmeta/main.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/baselines/fedmeta/fedmeta/main.py b/baselines/fedmeta/fedmeta/main.py index bce21fca1fa6..ea00ed1099f1 100644 --- a/baselines/fedmeta/fedmeta/main.py +++ b/baselines/fedmeta/fedmeta/main.py @@ -84,10 +84,7 @@ def main(cfg: DictConfig) -> None: print("................") print(history) - output_path = ( - HydraConfig.get().runtime.cwd + "/fedmeta/" + cfg.data.data + "/graph_params" - ) - os.makedirs(output_path, exist_ok=True) + output_path = HydraConfig.get().runtime.output_dir data_params = { "algo": cfg.algo.algo, From d584ba38fd326219504a52d6863412c64e090077 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Thu, 12 Oct 2023 13:34:46 +0000 Subject: [PATCH 123/133] align with main --- datasets/flwr_datasets/__init__.py | 7 +- datasets/pyproject.toml | 1 + examples/flower-in-30-minutes/tutorial.ipynb | 751 ------------------ .../simulation/ray_transport/ray_actor.py | 2 +- .../ray_transport/ray_client_proxy.py | 6 +- 5 files changed, 9 insertions(+), 758 deletions(-) diff --git a/datasets/flwr_datasets/__init__.py b/datasets/flwr_datasets/__init__.py index 1a48df5e45f0..0149a53b7b0a 100644 --- a/datasets/flwr_datasets/__init__.py +++ b/datasets/flwr_datasets/__init__.py @@ -1,4 +1,4 @@ -# Copyright 2023 Adap GmbH. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -13,3 +13,8 @@ # limitations under the License. # ============================================================================== """Flower Datasets main package.""" + + +from .federated_dataset import FederatedDataset + +__all__ = ["FederatedDataset"] diff --git a/datasets/pyproject.toml b/datasets/pyproject.toml index aa35ddca4fce..954441e5d2e4 100644 --- a/datasets/pyproject.toml +++ b/datasets/pyproject.toml @@ -66,6 +66,7 @@ docformatter = "==1.7.1" mypy = "==1.4.0" pylint = "==2.13.9" flake8 = "==3.9.2" +parameterized = "==0.9.0" pytest = "==7.1.2" pytest-watch = "==4.2.0" ruff = "==0.0.277" diff --git a/examples/flower-in-30-minutes/tutorial.ipynb b/examples/flower-in-30-minutes/tutorial.ipynb index 4951298e93ce..336ec4c19644 100644 --- a/examples/flower-in-30-minutes/tutorial.ipynb +++ b/examples/flower-in-30-minutes/tutorial.ipynb @@ -3,13 +3,7 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "P-iD0bgbXDdC" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Welcome to the 30 minutes Flower federated learning tutorial!\n", "\n", @@ -29,11 +23,7 @@ "## Complementary Content\n", "\n", "But before do so, let me point you to a few video tutorials in the [Flower Youtube channel](https://www.youtube.com/@flowerlabs) that you might want to check out after this tutorial. We post new videos fairly regularly with new content:\n", -<<<<<<< HEAD - "* **[VIDEO]** quickstart-tensorflow: [15-min video on how to start with Flower + Tensorflow/Keras](https://www.youtube.com/watch?v=jOmmuzMIQ4c)\n", -======= "* **[VIDEO]** quickstart-tensorflow: [15-min video on how to start with Flower + Tensorflow/Keras](https://www.youtube.com/watch?v=FGTc2TQq7VM)\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "* **[VIDEO]** quickstart-pytorch: [20-min video on how to start with Flower + PyTorch](https://www.youtube.com/watch?v=jOmmuzMIQ4c)\n", "* **[VIDEO]** Flower simulation mini-series: [9 line-by-line video tutorials](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)" ] @@ -41,13 +31,7 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "jfy1EuX7Xzfg" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "# Environment Setup\n", "\n", @@ -62,39 +46,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 1, - "metadata": { - "id": "Gc_GOyNXXB35" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/adap/flower.git@main\n", - " Cloning https://github.com/adap/flower.git (to revision main) to /private/var/folders/yr/0jrp7k711jzcxcdxgh6y1jh80000gn/T/pip-req-build-9uwmcdwe\n", - " Running command git clone --filter=blob:none --quiet https://github.com/adap/flower.git /private/var/folders/yr/0jrp7k711jzcxcdxgh6y1jh80000gn/T/pip-req-build-9uwmcdwe\n", - " Resolved https://github.com/adap/flower.git to commit 351dae247c2d69680acdca6d449a4d62dfb5baf8\n", - " Installing build dependencies ... \u001B[?25ldone\n", - "\u001B[?25h Getting requirements to build wheel ... \u001B[?25ldone\n", - "\u001B[?25h Preparing metadata (pyproject.toml) ... \u001B[?25ldone\n", - "\u001B[?25hRequirement already satisfied: cryptography<42.0.0,>=41.0.2 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (41.0.3)\n", - "Requirement already satisfied: grpcio!=1.52.0,<2.0.0,>=1.48.2 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (1.57.0)\n", - "Requirement already satisfied: iterators<0.0.3,>=0.0.2 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (0.0.2)\n", - "Requirement already satisfied: numpy<2.0.0,>=1.21.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (1.24.3)\n", - "Requirement already satisfied: protobuf<4.0.0,>=3.19.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (3.20.3)\n", - "Requirement already satisfied: pycryptodome<4.0.0,>=3.18.0 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from flwr==1.5.0) (3.18.0)\n", - "Requirement already satisfied: cffi>=1.12 in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from cryptography<42.0.0,>=41.0.2->flwr==1.5.0) (1.15.1)\n", - "Requirement already satisfied: pycparser in /Users/javier/miniconda3/envs/agustin_test/lib/python3.10/site-packages (from cffi>=1.12->cryptography<42.0.0,>=41.0.2->flwr==1.5.0) (2.21)\n" - ] - } - ], -======= "execution_count": null, "metadata": {}, "outputs": [], ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "# depending on your shell, you might need to add `\\` before `[` and `]`.\n", "!pip install -q flwr[simulation]" @@ -103,13 +57,7 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "y58HdudsYWQP" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "We will be using the _simulation_ model in Flower, which allows you to run a large number of clients without the overheads of manually managing devices. This is achieved via the `Virtual Client Engine`, the core component that runs [FL Simulations](https://flower.dev/docs/framework/how-to-run-simulations.html) with Flower. With simulation, you can dynamically scale your experiments whether you run the code on your laptop, a machine with a single GPU, a server with multiple GPUs os even on a cluster with multiple servers. The `Virtual Client Engine` handles everything transparently and it allows you to specify how many resources (e.g. CPU cores, GPU VRAM) should be assigned to each virtual client." ] @@ -117,13 +65,7 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "2rkzo1M9a0io" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "## Install your ML framework\n", "\n", @@ -139,10 +81,6 @@ "colab": { "base_uri": "https://localhost:8080/" }, -<<<<<<< HEAD - "id": "lqrJYPbZZ8aM", -======= ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "outputId": "7192138a-8c87-4d9a-f726-af1038ad264c" }, "outputs": [], @@ -155,77 +93,21 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "4UTuRurVeLDF" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "We are going to install some other dependencies you are likely familiar with. We'll use these to make plots." ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 2, -======= "execution_count": null, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, -<<<<<<< HEAD - "id": "ybSlTUVXeT3u", - "outputId": "58b7af77-609f-4118-bd5b-5629a4b5a296" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting matplotlib\n", - " Obtaining dependency information for matplotlib from https://files.pythonhosted.org/packages/8d/22/719f4fff33b13b0708711fb52ca3fc44617a26728e0e023358288d5197ae/matplotlib-3.7.2-cp310-cp310-macosx_11_0_arm64.whl.metadata\n", - " Downloading matplotlib-3.7.2-cp310-cp310-macosx_11_0_arm64.whl.metadata (5.6 kB)\n", - "Collecting contourpy>=1.0.1 (from matplotlib)\n", - " Obtaining dependency information for contourpy>=1.0.1 from https://files.pythonhosted.org/packages/15/c4/aae3954fce0e22362cc55430d1a395bf0be5a22b40fce63edda9eb6ea339/contourpy-1.1.0-cp310-cp310-macosx_11_0_arm64.whl.metadata\n", - 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"\u001B[?25hInstalling collected packages: pyparsing, kiwisolver, fonttools, cycler, contourpy, matplotlib\n", - "Successfully installed contourpy-1.1.0 cycler-0.11.0 fonttools-4.42.1 kiwisolver-1.4.5 matplotlib-3.7.2 pyparsing-3.0.9\n" - ] - } - ], -======= "outputId": "58b7af77-609f-4118-bd5b-5629a4b5a296" }, "outputs": [], ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "!pip install matplotlib" ] @@ -233,13 +115,7 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "mpmcL_STdjIo" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "# Centralised training: the old way of doing ML" ] @@ -247,13 +123,7 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "gvw2TZjSec6C" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Let's begin by creating a simple (but complete) training loop as it is commonly done in centralised setups. Starting our tutorial in this way will allow us to very clearly identify which parts of a typical ML pipeline are common to both centralised and federated training and which ones are poles a part.\n", "\n", @@ -267,15 +137,8 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 3, - "metadata": { - "id": "p9aFBjd1cpHs" - }, -======= "execution_count": null, "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "outputs": [], "source": [ "# we naturally first need to import torch and torchvision\n", @@ -285,17 +148,10 @@ "from torchvision.datasets import MNIST\n", "\n", "\n", -<<<<<<< HEAD - "def get_mnist(data_path: str = './data'):\n", - " '''This function downloads the MNIST dataset into the `data_path`\n", - " directory if it is not there already. WE construct the train/test\n", - " split by converting the images into tensors and normalising them'''\n", -======= "def get_mnist(data_path: str = \"./data\"):\n", " \"\"\"This function downloads the MNIST dataset into the `data_path`\n", " directory if it is not there already. WE construct the train/test\n", " split by converting the images into tensors and normalising them\"\"\"\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "\n", " # transformation to convert images to tensors and apply normalisation\n", " tr = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])\n", @@ -310,28 +166,15 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "vKoQfqgYgwg0" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Let's run the code above and do some visualisations to understand better the data we are working with !" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 4, - "metadata": { - "id": "pS8sL2hDgvZN" - }, -======= "execution_count": null, "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "outputs": [], "source": [ "trainset, testset = get_mnist()" @@ -340,57 +183,21 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "iNA-6AcYhYVM" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "We can have a quick overview of our datasets by just typing the object on the command line. For instance, below you can see that the `trainset` has 60k training examples and will use the transformation rule we defined above in `get_mnist()`." ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 5, -======= "execution_count": null, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, -<<<<<<< HEAD - "id": "pWsIHsq-g4nX", - "outputId": "f10b649f-3cee-4e86-c7ff-94bd1fd3e082" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Dataset MNIST\n", - " Number of datapoints: 60000\n", - " Root location: ./data\n", - " Split: Train\n", - " StandardTransform\n", - "Transform: Compose(\n", - " ToTensor()\n", - " Normalize(mean=(0.1307,), std=(0.3081,))\n", - " )" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], -======= "outputId": "f10b649f-3cee-4e86-c7ff-94bd1fd3e082" }, "outputs": [], ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "trainset" ] @@ -398,138 +205,58 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "NY9bWlaMhweq" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Let's create a more insightful visualisation. First let's see the distribution over the labels by constructing a histogram. Then, let's visualise some training examples !" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 6, -======= "execution_count": null, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 490 }, -<<<<<<< HEAD - "id": "DCTjwpizikwy", - "outputId": "c8d0f4c0-60cd-4c58-bc91-3b061dae8046" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Class labels distribution for MNIST')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "

    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], -======= "outputId": "c8d0f4c0-60cd-4c58-bc91-3b061dae8046" }, "outputs": [], ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "import matplotlib.pyplot as plt\n", "\n", "\n", "# construct histogram\n", "all_labels = trainset.targets\n", -<<<<<<< HEAD - "num_possible_labels = len(set(all_labels.numpy().tolist())) # this counts unique labels (so it should be = 10)\n", -======= "num_possible_labels = len(\n", " set(all_labels.numpy().tolist())\n", ") # this counts unique labels (so it should be = 10)\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "plt.hist(all_labels, bins=num_possible_labels)\n", "\n", "# plot formatting\n", "plt.xticks(range(num_possible_labels))\n", "plt.grid()\n", -<<<<<<< HEAD - "plt.xlabel('Label')\n", - "plt.ylabel('Number of images')\n", - "plt.title('Class labels distribution for MNIST')" -======= "plt.xlabel(\"Label\")\n", "plt.ylabel(\"Number of images\")\n", "plt.title(\"Class labels distribution for MNIST\")" ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 ] }, { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "K7-K0bKamho7" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Let's visualise 32 images from the dataset\n" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 7, - "metadata": { - "id": "ExGypiVsjMUv" - }, -======= "execution_count": null, "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "outputs": [], "source": [ "import random\n", "import numpy as np\n", "\n", -<<<<<<< HEAD - "def visualise_n_random_examples(trainset_, n: int, verbose: bool = True):\n", - " # take n examples at random\n", - " idx =list(range(len(trainset_.data)))\n", - " random.shuffle(idx)\n", - " idx = idx[:n]\n", - " if verbose:\n", - " print(f\"will display images with idx: {idx}\")\n", - "\n", - "\n", - " # construct canvas\n", - " num_cols = 8\n", - " num_rows = int(np.ceil(len(idx)/num_cols))\n", - " fig, axs = plt.subplots(figsize=(16, num_rows*2), nrows=num_rows, ncols=num_cols)\n", - "\n", - " # display images on canvas\n", - " for c_i, i in enumerate(idx):\n", - " axs.flat[c_i].imshow(trainset_.data[i], cmap='gray')" -======= "\n", "def visualise_n_random_examples(trainset_, n: int, verbose: bool = True):\n", " # take n examples at random\n", @@ -547,49 +274,19 @@ " # display images on canvas\n", " for c_i, i in enumerate(idx):\n", " axs.flat[c_i].imshow(trainset_.data[i], cmap=\"gray\")" ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 8, -======= "execution_count": null, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 715 }, -<<<<<<< HEAD - "id": "xA2s8vqkmkga", - "outputId": "4e0988a8-388d-4acf-882b-089e4ea887bf" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "will display images with idx: [59717, 34422, 26054, 1199, 3182, 18665, 27924, 45921, 19494, 40038, 31891, 22197, 14705, 4590, 46747, 15779, 2575, 32582, 47065, 19149, 41838, 24098, 28738, 39203, 35935, 55347, 16343, 40626, 31743, 34183, 18890, 47438]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], -======= "outputId": "4e0988a8-388d-4acf-882b-089e4ea887bf" }, "outputs": [], ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "# it is likely that the plot this function will generate looks familiar to other plots you might have generated before\n", "# or you might have encountered in other tutorials. So far, we aren't doing anything new, Federated Learning will start soon!\n", @@ -599,13 +296,7 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "PmGyjFEFhVwd" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "# A CNN architecture\n", "\n", @@ -615,24 +306,14 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 9, - "metadata": { - "id": "Nr4jR6tspOh4" - }, -======= "execution_count": null, "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "outputs": [], "source": [ "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", -<<<<<<< HEAD -======= "\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "class Net(nn.Module):\n", " def __init__(self, num_classes: int) -> None:\n", " super(Net, self).__init__()\n", @@ -656,46 +337,21 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "5TJrrCBlpZOp" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Similarly to what we did with the dataset you could inspect the model in various ways. We can, for instance, count the number of model parameters." ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 10, -======= "execution_count": null, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, -<<<<<<< HEAD - "id": "zdVK9c4hpYaC", - "outputId": "67d01ab4-cdd9-4661-8f01-eaa9aabf786d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num_parameters = 44426\n" - ] - } - ], -======= "outputId": "67d01ab4-cdd9-4661-8f01-eaa9aabf786d" }, "outputs": [], ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "model = Net(num_classes=10)\n", "num_parameters = sum(value.numel() for value in model.state_dict().values())\n", @@ -705,13 +361,7 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "XAXzw1dlp_oO" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "## The Training Loop\n", "\n", @@ -725,15 +375,8 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 12, - "metadata": { - "id": "DRhz5bcfpw06" - }, -======= "execution_count": null, "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "outputs": [], "source": [ "def train(net, trainloader, optimizer, epochs):\n", @@ -748,10 +391,7 @@ " optimizer.step()\n", " return net\n", "\n", -<<<<<<< HEAD -======= "\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "def test(net, testloader):\n", " \"\"\"Validate the network on the entire test set.\"\"\"\n", " criterion = torch.nn.CrossEntropyLoss()\n", @@ -767,11 +407,7 @@ " return loss, accuracy\n", "\n", "\n", -<<<<<<< HEAD - "def run_centralised(epochs: int, lr: float, momentum: float=0.9):\n", -======= "def run_centralised(epochs: int, lr: float, momentum: float = 0.9):\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 " \"\"\"A minimal (but complete) training loop\"\"\"\n", "\n", " # instantiate the model\n", @@ -797,47 +433,21 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "76Q0UnqiukYT" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Let's run this for 5 epochs (you'll see it reaching close to 99% accuracy -- as expected from a centralised setup with the MNIST dataset)" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 13, -======= "execution_count": null, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, -<<<<<<< HEAD - "id": "xgJ6mdNSqzpI", - "outputId": "e8d9b429-178d-4924-e82f-4d4e52863788" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss = 2.4290689574118005\n", - "accuracy = 0.9894\n" - ] - } - ], -======= "outputId": "e8d9b429-178d-4924-e82f-4d4e52863788" }, "outputs": [], ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "run_centralised(epochs=5, lr=0.01)" ] @@ -845,13 +455,7 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "pyz2gQaluw-5" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "The above centralised formulation has worked just fine for some applications and to showcase the potential of AI in a variety of scenarios. However, as was discussed earlier in the session, centralised training is unsuitable for a larger range of settings were information cannot be collected in order to build a unified (centralised) dataset.\n", "\n", @@ -869,13 +473,7 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "-Jv-H2HBzbPA" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "## One Client, One Data Partition\n", "\n", @@ -884,38 +482,20 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 14, - "metadata": { - "id": "Lgc5C6yltJCv" - }, -======= "execution_count": null, "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "outputs": [], "source": [ "from torch.utils.data import random_split\n", "\n", -<<<<<<< HEAD - "def prepare_dataset(num_partitions: int,\n", - " batch_size: int,\n", - " val_ratio: float = 0.1):\n", - "\n", -======= "\n", "def prepare_dataset(num_partitions: int, batch_size: int, val_ratio: float = 0.1):\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 " \"\"\"This function partitions the training set into N disjoint\n", " subsets, each will become the local dataset of a client. This\n", " function also subsequently partitions each traininset partition\n", " into train and validation. The test set is left intact and will\n", " be used by the central server to asses the performance of the\n", -<<<<<<< HEAD - " global model. \"\"\"\n", -======= " global model.\"\"\"\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "\n", " # get the MNIST dataset\n", " trainset, testset = get_mnist()\n", @@ -925,13 +505,9 @@ "\n", " partition_len = [num_images] * num_partitions\n", "\n", -<<<<<<< HEAD - " trainsets = random_split(trainset, partition_len, torch.Generator().manual_seed(2023))\n", -======= " trainsets = random_split(\n", " trainset, partition_len, torch.Generator().manual_seed(2023)\n", " )\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "\n", " # create dataloaders with train+val support\n", " trainloaders = []\n", @@ -941,12 +517,6 @@ " num_val = int(val_ratio * num_total)\n", " num_train = num_total - num_val\n", "\n", -<<<<<<< HEAD - " for_train, for_val = random_split(trainset_, [num_train, num_val], torch.Generator().manual_seed(2023))\n", - "\n", - " trainloaders.append(DataLoader(for_train, batch_size=batch_size, shuffle=True, num_workers=2))\n", - " valloaders.append(DataLoader(for_val, batch_size=batch_size, shuffle=False, num_workers=2))\n", -======= " for_train, for_val = random_split(\n", " trainset_, [num_train, num_val], torch.Generator().manual_seed(2023)\n", " )\n", @@ -957,7 +527,6 @@ " valloaders.append(\n", " DataLoader(for_val, batch_size=batch_size, shuffle=False, num_workers=2)\n", " )\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "\n", " # create dataloader for the test set\n", " testloader = DataLoader(testset, batch_size=128)\n", @@ -968,66 +537,19 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "9sXWjalq-G39" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Let's create 100 partitions and extract some statistics from one partition\n" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 15, -======= "execution_count": null, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 508 }, -<<<<<<< HEAD - "id": "I0LbJhrC0evC", - "outputId": "0f53ca81-cb55-46ef-c8e0-4e19a4f060b2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "number of images: 540\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Class labels distribution for MNIST')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "trainloaders, valloaders, testloader = prepare_dataset(num_partitions=100,\n", - " batch_size=32)\n", -======= "outputId": "0f53ca81-cb55-46ef-c8e0-4e19a4f060b2" }, "outputs": [], @@ -1035,7 +557,6 @@ "trainloaders, valloaders, testloader = prepare_dataset(\n", " num_partitions=100, batch_size=32\n", ")\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "\n", "# first partition\n", "train_partition = trainloaders[0].dataset\n", @@ -1048,27 +569,15 @@ "plt.hist(train_partition.dataset.dataset.targets[partition_indices], bins=10)\n", "plt.grid()\n", "plt.xticks(range(10))\n", -<<<<<<< HEAD - "plt.xlabel('Label')\n", - "plt.ylabel('Number of images')\n", - "plt.title('Class labels distribution for MNIST')" -======= "plt.xlabel(\"Label\")\n", "plt.ylabel(\"Number of images\")\n", "plt.title(\"Class labels distribution for MNIST\")" ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 ] }, { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "me-cNRBO_-fa" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "As you can see, the histogram of this partition is a bit different from the one we obtained at the beginning where we took the entire dataset into consideration. Because our data partitions are artificially constructed by sampling the MNIST dataset in an IID fashion, our Federated Learning example will not face sever _data heterogeneity_ issues (which is a fairly [active research topic](https://arxiv.org/abs/1912.04977)).\n", "\n", @@ -1093,25 +602,9 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 16, - "metadata": { - "id": "GckcVE2hH5UV" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-28 20:57:17,516\tINFO util.py:159 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n" - ] - } - ], -======= "execution_count": null, "metadata": {}, "outputs": [], ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "import flwr as fl" ] @@ -1119,28 +612,15 @@ { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "w3zwIYgVH5wU" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Now let's define our Flower Client class:" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 17, - "metadata": { - "id": "uXdiNmCE_90y" - }, -======= "execution_count": null, "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "outputs": [], "source": [ "from collections import OrderedDict\n", @@ -1149,16 +629,9 @@ "import torch\n", "from flwr.common import NDArrays, Scalar\n", "\n", -<<<<<<< HEAD - "class FlowerClient(fl.client.NumPyClient):\n", - " def __init__(self,\n", - " trainloader,\n", - " vallodaer) -> None:\n", -======= "\n", "class FlowerClient(fl.client.NumPyClient):\n", " def __init__(self, trainloader, vallodaer) -> None:\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 " super().__init__()\n", "\n", " self.trainloader = trainloader\n", @@ -1201,29 +674,17 @@ " local validation set. Then return performance metrics.\"\"\"\n", "\n", " self.set_parameters(parameters)\n", -<<<<<<< HEAD - " loss, accuracy = test(self.model, self.valloader) # <-------------------------- calls the `test` function, just what we did in the centralised setting (but this time using the client's local validation set)\n", - " # send statistics back to the server\n", - " return float(loss), len(self.valloader), {'accuracy': accuracy}" -======= " loss, accuracy = test(\n", " self.model, self.valloader\n", " ) # <-------------------------- calls the `test` function, just what we did in the centralised setting (but this time using the client's local validation set)\n", " # send statistics back to the server\n", " return float(loss), len(self.valloader), {\"accuracy\": accuracy}" ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 ] }, { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "d5Ku-Z_1Jkvz" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Spend a few minutes to inspect the `FlowerClient` class above. Please ask questions if there is something unclear !\n", "\n", @@ -1249,15 +710,8 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 18, - "metadata": { - "id": "gUmUpH5t-Urn" - }, -======= "execution_count": null, "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "outputs": [], "source": [ "def get_evalulate_fn(testloader):\n", @@ -1265,10 +719,7 @@ " function (i.e. `evaluate_fn`) will be executed by the strategy\n", " at the end of each round to evaluate the stat of the global\n", " model.\"\"\"\n", -<<<<<<< HEAD -======= "\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 " def evaluate_fn(server_round: int, parameters, config):\n", " \"\"\"This function is executed by the strategy it will instantiate\n", " a model and replace its parameters with those from the global model.\n", @@ -1283,44 +734,27 @@ " model.load_state_dict(state_dict, strict=True)\n", "\n", " # call test\n", -<<<<<<< HEAD - " loss, accuracy = test(model, testloader) # <-------------------------- calls the `test` function, just what we did in the centralised setting\n", -======= " loss, accuracy = test(\n", " model, testloader\n", " ) # <-------------------------- calls the `test` function, just what we did in the centralised setting\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 " return loss, {\"accuracy\": accuracy}\n", "\n", " return evaluate_fn\n", "\n", "\n", "# now we can define the strategy\n", -<<<<<<< HEAD - "strategy = fl.server.strategy.FedAvg(fraction_fit=0.1, # let's sample 10% of the client each round to do local training\n", - " fraction_evaluate=0.1, # after each round, let's sample 20% of the clients to asses how well the global model is doing\n", - " min_available_clients=100, # total number of clients available in the experiment\n", - " evaluate_fn=get_evalulate_fn(testloader)) # a callback to a function that the strategy can execute to evaluate the state of the global model on a centralised dataset\n" -======= "strategy = fl.server.strategy.FedAvg(\n", " fraction_fit=0.1, # let's sample 10% of the client each round to do local training\n", " fraction_evaluate=0.1, # after each round, let's sample 20% of the clients to asses how well the global model is doing\n", " min_available_clients=100, # total number of clients available in the experiment\n", " evaluate_fn=get_evalulate_fn(testloader),\n", ") # a callback to a function that the strategy can execute to evaluate the state of the global model on a centralised dataset" ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 ] }, { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "4UV_kBVGRbQT" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "So far we have:\n", "* created the dataset partitions (one for each client)\n", @@ -1332,27 +766,14 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 19, - "metadata": { - "id": "frPHAxUg-3Ev" - }, -======= "execution_count": null, "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "outputs": [], "source": [ "def generate_client_fn(trainloaders, valloaders):\n", " def client_fn(cid: str):\n", " \"\"\"Returns a FlowerClient containing the cid-th data partition\"\"\"\n", "\n", -<<<<<<< HEAD - " return FlowerClient(trainloader=trainloaders[int(cid)],\n", - " vallodaer=valloaders[int(cid)])\n", - " return client_fn\n", - "\n", -======= " return FlowerClient(\n", " trainloader=trainloaders[int(cid)], vallodaer=valloaders[int(cid)]\n", " )\n", @@ -1360,124 +781,24 @@ " return client_fn\n", "\n", "\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "client_fn_callback = generate_client_fn(trainloaders, valloaders)" ] }, { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "uJ0swdTqSyuA" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Now we are ready to launch the FL experiment using Flower simulation:" ] }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 21, -======= "execution_count": null, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, -<<<<<<< HEAD - "id": "VpXEG9cxR9vu", - "outputId": "9ad8dcea-8004-4c6e-a025-e168da636c88" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO flwr 2023-08-28 20:59:31,625 | app.py:175 | Starting Flower simulation, config: ServerConfig(num_rounds=10, round_timeout=None)\n", - "2023-08-28 20:59:34,903\tINFO worker.py:1621 -- Started a local Ray instance.\n", - "INFO flwr 2023-08-28 20:59:35,673 | app.py:210 | Flower VCE: Ray initialized with resources: {'object_store_memory': 2147483648.0, 'CPU': 10.0, 'node:__internal_head__': 1.0, 'node:127.0.0.1': 1.0, 'memory': 19064990925.0}\n", - "INFO flwr 2023-08-28 20:59:35,673 | app.py:218 | No `client_resources` specified. Using minimal resources for clients.\n", - "INFO flwr 2023-08-28 20:59:35,674 | app.py:224 | Flower VCE: Resources for each Virtual Client: {'num_cpus': 1, 'num_gpus': 0.0}\n", - "INFO flwr 2023-08-28 20:59:35,682 | app.py:270 | Flower VCE: Creating VirtualClientEngineActorPool with 10 actors\n", - "INFO flwr 2023-08-28 20:59:35,682 | server.py:89 | Initializing global parameters\n", - "INFO flwr 2023-08-28 20:59:35,683 | server.py:276 | Requesting initial parameters from one random client\n", - "INFO flwr 2023-08-28 20:59:40,091 | server.py:280 | Received initial parameters from one random client\n", - "INFO flwr 2023-08-28 20:59:40,092 | server.py:91 | Evaluating initial parameters\n", - "INFO flwr 2023-08-28 20:59:40,780 | server.py:94 | initial parameters (loss, other metrics): 182.0281903743744, {'accuracy': 0.1114}\n", - "INFO flwr 2023-08-28 20:59:40,780 | server.py:104 | FL starting\n", - "DEBUG flwr 2023-08-28 20:59:40,781 | server.py:222 | fit_round 1: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 20:59:47,133 | server.py:236 | fit_round 1 received 10 results and 0 failures\n", - "WARNING flwr 2023-08-28 20:59:47,141 | fedavg.py:242 | No fit_metrics_aggregation_fn provided\n", - "INFO flwr 2023-08-28 20:59:47,821 | server.py:125 | fit progress: (1, 181.06436610221863, {'accuracy': 0.1341}, 7.040863708942197)\n", - "DEBUG flwr 2023-08-28 20:59:47,822 | server.py:173 | evaluate_round 1: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 20:59:52,362 | server.py:187 | evaluate_round 1 received 10 results and 0 failures\n", - "WARNING flwr 2023-08-28 20:59:52,363 | fedavg.py:273 | No evaluate_metrics_aggregation_fn provided\n", - "DEBUG flwr 2023-08-28 20:59:52,363 | server.py:222 | fit_round 2: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 20:59:56,935 | server.py:236 | fit_round 2 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 20:59:57,627 | server.py:125 | fit progress: (2, 179.6406238079071, {'accuracy': 0.2844}, 16.846019334043376)\n", - "DEBUG flwr 2023-08-28 20:59:57,627 | server.py:173 | evaluate_round 2: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:02,146 | server.py:187 | evaluate_round 2 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:02,146 | server.py:222 | fit_round 3: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:06,692 | server.py:236 | fit_round 3 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:07,369 | server.py:125 | fit progress: (3, 176.3769176006317, {'accuracy': 0.5013}, 26.587926791980863)\n", - "DEBUG flwr 2023-08-28 21:00:07,369 | server.py:173 | evaluate_round 3: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:11,854 | server.py:187 | evaluate_round 3 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:11,855 | server.py:222 | fit_round 4: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:16,728 | server.py:236 | fit_round 4 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:17,414 | server.py:125 | fit progress: (4, 165.48094844818115, {'accuracy': 0.4989}, 36.63336270896252)\n", - "DEBUG flwr 2023-08-28 21:00:17,415 | server.py:173 | evaluate_round 4: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:22,117 | server.py:187 | evaluate_round 4 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:22,118 | server.py:222 | fit_round 5: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:26,776 | server.py:236 | fit_round 5 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:27,456 | server.py:125 | fit progress: (5, 115.77451705932617, {'accuracy': 0.6265}, 46.67501679202542)\n", - "DEBUG flwr 2023-08-28 21:00:27,457 | server.py:173 | evaluate_round 5: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:31,981 | server.py:187 | evaluate_round 5 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:31,982 | server.py:222 | fit_round 6: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:36,573 | server.py:236 | fit_round 6 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:37,266 | server.py:125 | fit progress: (6, 51.16007122397423, {'accuracy': 0.8018}, 56.484427334042266)\n", - "DEBUG flwr 2023-08-28 21:00:37,266 | server.py:173 | evaluate_round 6: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:41,796 | server.py:187 | evaluate_round 6 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:41,797 | server.py:222 | fit_round 7: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:46,326 | server.py:236 | fit_round 7 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:47,010 | server.py:125 | fit progress: (7, 46.40866267681122, {'accuracy': 0.8081}, 66.22883979196195)\n", - "DEBUG flwr 2023-08-28 21:00:47,011 | server.py:173 | evaluate_round 7: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:51,524 | server.py:187 | evaluate_round 7 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:00:51,525 | server.py:222 | fit_round 8: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:00:56,240 | server.py:236 | fit_round 8 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:00:56,982 | server.py:125 | fit progress: (8, 33.36455833911896, {'accuracy': 0.8698}, 76.20065708400216)\n", - "DEBUG flwr 2023-08-28 21:00:56,983 | server.py:173 | evaluate_round 8: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:01:01,527 | server.py:187 | evaluate_round 8 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:01:01,527 | server.py:222 | fit_round 9: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:01:06,174 | server.py:236 | fit_round 9 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:01:06,874 | server.py:125 | fit progress: (9, 28.229523852467537, {'accuracy': 0.9001}, 86.09250316699035)\n", - "DEBUG flwr 2023-08-28 21:01:06,874 | server.py:173 | evaluate_round 9: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:01:11,251 | server.py:187 | evaluate_round 9 received 10 results and 0 failures\n", - "DEBUG flwr 2023-08-28 21:01:11,251 | server.py:222 | fit_round 10: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:01:15,794 | server.py:236 | fit_round 10 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:01:16,470 | server.py:125 | fit progress: (10, 22.725839115679264, {'accuracy': 0.9168}, 95.68810208397917)\n", - "DEBUG flwr 2023-08-28 21:01:16,470 | server.py:173 | evaluate_round 10: strategy sampled 10 clients (out of 100)\n", - "DEBUG flwr 2023-08-28 21:01:20,808 | server.py:187 | evaluate_round 10 received 10 results and 0 failures\n", - "INFO flwr 2023-08-28 21:01:20,809 | server.py:153 | FL finished in 100.02693225000985\n", - "INFO flwr 2023-08-28 21:01:20,809 | app.py:225 | app_fit: losses_distributed [(1, 4.5842246294021605), (2, 4.546195244789123), (3, 4.46350576877594), (4, 4.165321779251099), (5, 2.8972655892372132), (6, 1.3353233098983766), (7, 1.2181178748607635), (8, 0.9146452054381371), (9, 0.8028807744383812), (10, 0.4898006349802017)]\n", - "INFO flwr 2023-08-28 21:01:20,809 | app.py:226 | app_fit: metrics_distributed_fit {}\n", - "INFO flwr 2023-08-28 21:01:20,810 | app.py:227 | app_fit: metrics_distributed {}\n", - "INFO flwr 2023-08-28 21:01:20,810 | app.py:228 | app_fit: losses_centralized [(0, 182.0281903743744), (1, 181.06436610221863), (2, 179.6406238079071), (3, 176.3769176006317), (4, 165.48094844818115), (5, 115.77451705932617), (6, 51.16007122397423), (7, 46.40866267681122), (8, 33.36455833911896), (9, 28.229523852467537), (10, 22.725839115679264)]\n", - "INFO flwr 2023-08-28 21:01:20,810 | app.py:229 | app_fit: metrics_centralized {'accuracy': [(0, 0.1114), (1, 0.1341), (2, 0.2844), (3, 0.5013), (4, 0.4989), (5, 0.6265), (6, 0.8018), (7, 0.8081), (8, 0.8698), (9, 0.9001), (10, 0.9168)]}\n" - ] - } - ], - "source": [ - "history = fl.simulation.start_simulation(\n", - " client_fn=client_fn_callback, # a callback to construct a client\n", - " num_clients=100, # total number of clients in the experiment\n", - " config=fl.server.ServerConfig(num_rounds=10), # let's run for 10 rounds\n", - " strategy=strategy, # the strategy that will orchestrate the whole FL pipeline\n", -======= "outputId": "9ad8dcea-8004-4c6e-a025-e168da636c88" }, "outputs": [], @@ -1487,20 +808,13 @@ " num_clients=100, # total number of clients in the experiment\n", " config=fl.server.ServerConfig(num_rounds=10), # let's run for 10 rounds\n", " strategy=strategy, # the strategy that will orchestrate the whole FL pipeline\n", ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 ")" ] }, { "attachments": {}, "cell_type": "markdown", -<<<<<<< HEAD - "metadata": { - "id": "2hLLbDCEUat7" - }, -======= "metadata": {}, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "source": [ "Doing 10 rounds should take less than 2 minutes on a CPU-only Colab instance <-- Flower Simulation is fast! 🚀\n", "\n", @@ -1509,61 +823,12 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 22, -======= "execution_count": null, ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 508 }, -<<<<<<< HEAD - "id": "EQ8GnlFVTJkF", - "outputId": "d8eab106-cee9-4266-9082-0944882cdba8" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "history.metrics_centralized = {'accuracy': [(0, 0.1114), (1, 0.1341), (2, 0.2844), (3, 0.5013), (4, 0.4989), (5, 0.6265), (6, 0.8018), (7, 0.8081), (8, 0.8698), (9, 0.9001), (10, 0.9168)]}\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'MNIST - IID - 100 clients with 10 clients per round')" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(f\"{history.metrics_centralized = }\")\n", - "\n", - "global_accuracy_centralised = history.metrics_centralized['accuracy']\n", - "round = [data[0] for data in global_accuracy_centralised]\n", - "acc = [100.0*data[1] for data in global_accuracy_centralised]\n", - "plt.plot(round, acc)\n", - "plt.grid()\n", - "plt.ylabel('Accuracy (%)')\n", - "plt.xlabel('Round')\n", - "plt.title('MNIST - IID - 100 clients with 10 clients per round')" -======= "outputId": "d8eab106-cee9-4266-9082-0944882cdba8" }, "outputs": [], @@ -1578,7 +843,6 @@ "plt.ylabel(\"Accuracy (%)\")\n", "plt.xlabel(\"Round\")\n", "plt.title(\"MNIST - IID - 100 clients with 10 clients per round\")" ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 ] }, { @@ -1620,21 +884,6 @@ "kernelspec": { "display_name": "Python 3", "name": "python3" -<<<<<<< HEAD - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" -======= ->>>>>>> ab7f77584b7caf37e98752016249b0cbfc6abd79 } }, "nbformat": 4, diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py index 4be1b6f229af..63323f51368a 100644 --- a/src/py/flwr/simulation/ray_transport/ray_actor.py +++ b/src/py/flwr/simulation/ray_transport/ray_actor.py @@ -1,4 +1,4 @@ -# Copyright 2023 Flower Labs. All Rights Reserved. +# Copyright 2023 Flower Labs GmbH. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py index 7b222ef3b901..0365cce073b4 100644 --- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py +++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py @@ -17,7 +17,7 @@ import traceback from logging import ERROR -from typing import Callable, Dict, Optional, Union, cast +from typing import Dict, Optional, cast import ray @@ -36,10 +36,6 @@ JobFn, VirtualClientEngineActorPool, ) -ClientFn = Callable[[str], ClientLike] -ClientRes = Union[ - common.GetPropertiesRes, common.GetParametersRes, common.FitRes, common.EvaluateRes -] class RayClientProxy(ClientProxy): From 4532326c0deda6163f002c9fb370bf4f6aeae0cf Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Thu, 12 Oct 2023 23:24:48 +0900 Subject: [PATCH 124/133] Update baselines/fedmeta/README.md Co-authored-by: Javier --- baselines/fedmeta/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 84cb08487737..fc91de5875e6 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -7,7 +7,7 @@ dataset: [FEMNIST, SHAKESPEARE] # FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication -****Paper:**** : [arxiv.org/abs/1802.07876](https://arxiv.org/abs/1802.07876) +**Paper:** [arxiv.org/abs/1802.07876](https://arxiv.org/abs/1802.07876) ****Authors:**** :Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, Xiuqiang He From bd865c330e0d4e04809bcb8d1934cc2e4438eccf Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 12 Oct 2023 23:30:45 +0900 Subject: [PATCH 125/133] fixed README.md --- baselines/fedmeta/README.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index fc91de5875e6..a0abaecdd9f7 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -9,35 +9,35 @@ dataset: [FEMNIST, SHAKESPEARE] **Paper:** [arxiv.org/abs/1802.07876](https://arxiv.org/abs/1802.07876) -****Authors:**** :Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, Xiuqiang He +**Authors:** Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, Xiuqiang He -****Abstract:**** :Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta-learning is a natural choice to handle these issues, and propose a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches. We conduct an extensive empirical evaluation on LEAF datasets and a real-world production dataset, and demonstrate that FedMeta achieves a reduction in required communication cost by 2.82-4.33 times with faster convergence, and an increase in accuracy by 3.23%-14.84% as compared to Federated Averaging (FedAvg) which is a leading optimization algorithm in federated learning. Moreover, FedMeta preserves user privacy since only the parameterized algorithm is transmitted between mobile devices and central servers, and no raw data is collected onto the servers. +**Abstract:** Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta-learning is a natural choice to handle these issues, and propose a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches. We conduct an extensive empirical evaluation on LEAF datasets and a real-world production dataset, and demonstrate that FedMeta achieves a reduction in required communication cost by 2.82-4.33 times with faster convergence, and an increase in accuracy by 3.23%-14.84% as compared to Federated Averaging (FedAvg) which is a leading optimization algorithm in federated learning. Moreover, FedMeta preserves user privacy since only the parameterized algorithm is transmitted between mobile devices and central servers, and no raw data is collected onto the servers. ## About this baseline -****What’s implemented:**** : We reimplemented the experiments from the paper 'FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication' by Fei Chen (2018). which proposed the FedMeta(MAML & Meta-SGD) algorithm. Specifically, we replicate the results from Table 2 and Figure 2 of the paper. +**What’s implemented:** We reimplemented the experiments from the paper 'FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication' by Fei Chen (2018). which proposed the FedMeta(MAML & Meta-SGD) algorithm. Specifically, we replicate the results from Table 2 and Figure 2 of the paper. -****Datasets:**** : FEMNIST and SHAKESPEARE from Leaf Federated Learning Dataset +**Datasets:** FEMNIST and SHAKESPEARE from Leaf Federated Learning Dataset -****Hardware Setup:**** : These experiments were run on a machine with 16 CPU threads and 1 GPU(GeForce RTX 2080 Ti). **However, the FedMeta experiment using the Shakespeare dataset required more computing power (more than 4 GPUs).** Out of Memory errors may occur with some clients, but federated learning can continue to operate. +**Hardware Setup:** These experiments were run on a machine with 16 CPU threads and 1 GPU(GeForce RTX 2080 Ti). **However, the FedMeta experiment using the Shakespeare dataset required more computing power (more than 4 GPUs).** Out of Memory errors may occur with some clients, but federated learning can continue to operate. -****Contributors:**** : **Jinsoo Kim and Kangyoon Lee** +**Contributors:** Jinsoo Kim and Kangyoon Lee ## Experimental Setup -****Task:**** : A comparison task of four algorithms(FedAvg, FedAvg(Meta), FedMeta(MAML), FedMeta(Meta-SGD)) in the categories of Image Classification and next-word prediction. +**Task:** A comparison task of four algorithms(FedAvg, FedAvg(Meta), FedMeta(MAML), FedMeta(Meta-SGD)) in the categories of Image Classification and next-word prediction. -****Model:**** :This directory implements two models: +**Model:** This directory implements two models: * A two-layer CNN network as used in the FedMeta paper for Femnist (see `models/CNN_Network`). * A StackedLSTM model used in the FedMeta paper for Shakespeare (see `models/StackedLSTM`). **You can see more detail in Apendix.A of the paper** -****Dataset:**** : This baseline includes the FEMNIST dataset and SHAKESPEARE. For data partitioning and sampling per client, we use the Leaf GitHub([LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf)). The data and client specifications used in this experiment are listed in the table below (Table 1 in the paper). +**Dataset:** This baseline includes the FEMNIST dataset and SHAKESPEARE. For data partitioning and sampling per client, we use the Leaf GitHub([LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf)). The data and client specifications used in this experiment are listed in the table below (Table 1 in the paper). -**Shakespeare Dataset Issue** : In the FedMeta paper experiment, the Shakespeare dataset had 1126 users. However, due to a current bug, the number of users has decreased to 660 users. Therefore, we have only maintained the total number of data. +**Shakespeare Dataset Issue:** In the FedMeta paper experiment, the Shakespeare dataset had 1126 users. However, due to a current bug, the number of users has decreased to 660 users. Therefore, we have only maintained the total number of data. | Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset | |:-----------:|:--------:|:--------:|:--------:|:---------------------------------------------------------------:|:----------------------:| @@ -46,7 +46,7 @@ dataset: [FEMNIST, SHAKESPEARE] **The original specifications of the Leaf dataset can be found in the Leaf paper(_"LEAF: A Benchmark for Federated Settings"_).** -****Training Hyperparameters:**** : The following table shows the main hyperparameters for this baseline with their default value (i.e. the value used if you run `python main.py algo=? data=?` directly) +**Training Hyperparameters:** The following table shows the main hyperparameters for this baseline with their default value (i.e. the value used if you run `python main.py algo=? data=?` directly) | Algorithm | Dataset | Clients per Round | Number of Rounds | Batch Size | Optimizer | Learning Rate(α, β) | Client Resources | Gradient Step | |:-----------------:|:--------------:|:-----------------:|:----------------:|:----------:|:---------:|:-------------------:|:---------------------------------------:|:-------------:| @@ -73,7 +73,7 @@ poetry shell ## Running the Experiments -****Download Dataset**** : Go [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf) and Use the command below! You can download dataset (FEMNIST and SHAKESPEARE). +**Download Dataset** Go [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf) and Use the command below! You can download dataset (FEMNIST and SHAKESPEARE). ```bash # clone LEAF repo git clone https://github.com/TalwalkarLab/leaf.git @@ -100,7 +100,7 @@ cd leaf/data/shakespeare More detailed tag information can be found on Leaf GitHub. -****Start experiments**** : +****Start experiments**** ```bash # FedAvg + Femnist Dataset python -m fedmeta.main algo=fedavg data=femnist path=(your leaf dataset path)/leaf/data/femnist/data From e144deae23f906f55f02f4566df2cbf6cdeedfbe Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 12 Oct 2023 23:33:12 +0900 Subject: [PATCH 126/133] fixed README.md --- baselines/fedmeta/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index a0abaecdd9f7..d8b3e6792d3b 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -73,7 +73,7 @@ poetry shell ## Running the Experiments -**Download Dataset** Go [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf) and Use the command below! You can download dataset (FEMNIST and SHAKESPEARE). +**Download Dataset:** Go [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf) and Use the command below! You can download dataset (FEMNIST and SHAKESPEARE). ```bash # clone LEAF repo git clone https://github.com/TalwalkarLab/leaf.git From df5fc0d62ebafd3989aedd4b493552b643c05816 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Thu, 12 Oct 2023 15:42:43 +0000 Subject: [PATCH 127/133] formatted --- baselines/fedmeta/fedmeta/client.py | 21 +++--- .../fedmeta/fedmeta/dataset_preparation.py | 2 +- .../fedmeta/fedmeta/fedmeta_client_manager.py | 14 ++-- baselines/fedmeta/fedmeta/main.py | 5 +- baselines/fedmeta/fedmeta/models.py | 73 ++++++++++--------- baselines/fedmeta/fedmeta/strategy.py | 2 +- 6 files changed, 57 insertions(+), 60 deletions(-) diff --git a/baselines/fedmeta/fedmeta/client.py b/baselines/fedmeta/fedmeta/client.py index 01a878ec2a2e..fb09773eebed 100644 --- a/baselines/fedmeta/fedmeta/client.py +++ b/baselines/fedmeta/fedmeta/client.py @@ -50,7 +50,7 @@ def set_parameters(self, parameters: NDArrays) -> None: self.net.load_state_dict(state_dict, strict=True) def fit( # type: ignore - self, parameters: NDArrays, config: Dict[str, Scalar] + self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[NDArrays, int, Dict]: """Implement distributed fit function for a given client.""" self.set_parameters(parameters) @@ -62,7 +62,7 @@ def fit( # type: ignore ) # FedAvg & FedAvg(Meta) train basic Learning - if algo in ('fedavg', 'fedavg_meta'): + if algo in ("fedavg", "fedavg_meta"): loss = train( self.net, self.trainloaders["sup"][self.cid], @@ -73,7 +73,7 @@ def fit( # type: ignore return self.get_parameters({}), total_len, {"loss": loss} # FedMeta(MAML) & FedMeta(Meta-SGD) train inner and outer loop - if algo in ('fedmeta_maml', 'fedmeta_meta_sgd'): + if algo in ("fedmeta_maml", "fedmeta_meta_sgd"): alpha = config["alpha"] loss, grads = train_meta( # type: ignore self.net, @@ -87,7 +87,7 @@ def fit( # type: ignore raise ValueError("Unsupported algorithm") def evaluate( # type: ignore - self, parameters: NDArrays, config: Dict[str, Scalar] + self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[float, int, Dict]: """Implement distributed evaluation for a given client.""" self.set_parameters(parameters) @@ -128,12 +128,12 @@ def evaluate( # type: ignore # pylint: disable=too-many-arguments def gen_client_fn( - num_epochs: int, - trainloaders: List[DataLoader], - valloaders: List[DataLoader], - learning_rate: float, - model: DictConfig, - gradient_step: int, + num_epochs: int, + trainloaders: List[DataLoader], + valloaders: List[DataLoader], + learning_rate: float, + model: DictConfig, + gradient_step: int, ) -> Callable[[str], FlowerClient]: """Generate the client function that creates the Flower Clients. @@ -165,7 +165,6 @@ def gen_client_fn( def client_fn(cid: str) -> FlowerClient: """Create a Flower client representing a single organization.""" - # Load model torch.manual_seed(42) torch.cuda.manual_seed_all(42) diff --git a/baselines/fedmeta/fedmeta/dataset_preparation.py b/baselines/fedmeta/fedmeta/dataset_preparation.py index 9c99cddddc5a..c139cdf86d69 100644 --- a/baselines/fedmeta/fedmeta/dataset_preparation.py +++ b/baselines/fedmeta/fedmeta/dataset_preparation.py @@ -167,7 +167,7 @@ def _partition_data( sup_x, qry_x, sup_y, qry_y = support_query_split( all_x, all_y, support_ratio ) - except Exception: # pylint: disable=broad-except + except Exception: # pylint: disable=broad-except continue elif data_type == "shakespeare": diff --git a/baselines/fedmeta/fedmeta/fedmeta_client_manager.py b/baselines/fedmeta/fedmeta/fedmeta_client_manager.py index 1b3a38c5b2d5..098922b92215 100644 --- a/baselines/fedmeta/fedmeta/fedmeta_client_manager.py +++ b/baselines/fedmeta/fedmeta/fedmeta_client_manager.py @@ -28,12 +28,12 @@ def __init__(self, valid_client, **kwargs): # pylint: disable=too-many-arguments def sample( # pylint: disable=arguments-differ - self, - num_clients: int, - min_num_clients: Optional[int] = None, - criterion: Optional[Criterion] = None, - server_round: Optional[int] = None, - step: Optional[str] = None + self, + num_clients: int, + min_num_clients: Optional[int] = None, + criterion: Optional[Criterion] = None, + server_round: Optional[int] = None, + step: Optional[str] = None, ) -> List[ClientProxy]: """Sample a number of Flower ClientProxy instances.""" # Block until at least num_clients are connected. @@ -42,7 +42,7 @@ def sample( # pylint: disable=arguments-differ self.wait_for(min_num_clients) # Sample clients which meet the criterion - if step == 'evaluate': + if step == "evaluate": available_cids = [str(result) for result in range(0, self.valid_client)] else: available_cids = list(self.clients) diff --git a/baselines/fedmeta/fedmeta/main.py b/baselines/fedmeta/fedmeta/main.py index ea00ed1099f1..e43ad94a3089 100644 --- a/baselines/fedmeta/fedmeta/main.py +++ b/baselines/fedmeta/fedmeta/main.py @@ -4,7 +4,6 @@ model is going to be evaluated, etc. At the end, this script saves the results. """ -import os import flwr as fl import hydra @@ -35,9 +34,7 @@ def main(cfg: DictConfig) -> None: print(OmegaConf.to_yaml(cfg)) # partition dataset and get dataloaders - trainloaders, valloaders, _ = load_datasets( - config=cfg.data, path=cfg.path - ) + trainloaders, valloaders, _ = load_datasets(config=cfg.data, path=cfg.path) # prepare function that will be used to spawn each client client_fn = client.gen_client_fn( diff --git a/baselines/fedmeta/fedmeta/models.py b/baselines/fedmeta/fedmeta/models.py index 15cea691de45..f065ae6c372b 100644 --- a/baselines/fedmeta/fedmeta/models.py +++ b/baselines/fedmeta/fedmeta/models.py @@ -95,11 +95,11 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: # pylint: disable=too-many-arguments def train( - net: nn.Module, - trainloader: DataLoader, - device: torch.device, - epochs: int, - learning_rate: float, + net: nn.Module, + trainloader: DataLoader, + device: torch.device, + epochs: int, + learning_rate: float, ) -> Tuple[float]: """Train the network on the training set. @@ -134,11 +134,11 @@ def train( def _train_one_epoch( - net: nn.Module, - trainloader: DataLoader, - device: torch.device, - criterion: torch.nn.CrossEntropyLoss, - optimizer: torch.optim.Adam, + net: nn.Module, + trainloader: DataLoader, + device: torch.device, + criterion: torch.nn.CrossEntropyLoss, + optimizer: torch.optim.Adam, ) -> nn.Module: """Train for one epoch. @@ -177,13 +177,13 @@ def _train_one_epoch( # pylint: disable=too-many-locals def test( - net: nn.Module, - trainloader: DataLoader, - testloader: DataLoader, - device: torch.device, - algo: str, - data: str, - learning_rate: float, + net: nn.Module, + trainloader: DataLoader, + testloader: DataLoader, + device: torch.device, + algo: str, + data: str, + learning_rate: float, ) -> Tuple[float, float]: """Evaluate the network on the entire test set. @@ -252,12 +252,12 @@ def test( def train_meta( - net: nn.Module, - supportloader: DataLoader, - queryloader: DataLoader, - alpha: torch.nn.ParameterList, - device: torch.device, - gradient_step: int, + net: nn.Module, + supportloader: DataLoader, + queryloader: DataLoader, + alpha: torch.nn.ParameterList, + device: torch.device, + gradient_step: int, ) -> Tuple[float, List]: """Train the network on the training set. @@ -290,15 +290,16 @@ def train_meta( ) return loss, grads + # pylint: disable=too-many-locals def _train_meta_one_epoch( - net: nn.Module, - supportloader: DataLoader, - queryloader: DataLoader, - alpha: torch.nn.ParameterList, - criterion: torch.nn.CrossEntropyLoss, - device: torch.device, - gradient_step: int, + net: nn.Module, + supportloader: DataLoader, + queryloader: DataLoader, + alpha: torch.nn.ParameterList, + criterion: torch.nn.CrossEntropyLoss, + device: torch.device, + gradient_step: int, ) -> Tuple[float, List]: """Train for one epoch. @@ -373,12 +374,12 @@ def _train_meta_one_epoch( def test_meta( - net: nn.Module, - supportloader: DataLoader, - queryloader: DataLoader, - alpha: torch.nn.ParameterList, - device: torch.device, - gradient_step: int, + net: nn.Module, + supportloader: DataLoader, + queryloader: DataLoader, + alpha: torch.nn.ParameterList, + device: torch.device, + gradient_step: int, ) -> Tuple[float, float]: """Evaluate the network on the entire test set. diff --git a/baselines/fedmeta/fedmeta/strategy.py b/baselines/fedmeta/fedmeta/strategy.py index d974ede0e119..7947938116e9 100644 --- a/baselines/fedmeta/fedmeta/strategy.py +++ b/baselines/fedmeta/fedmeta/strategy.py @@ -261,7 +261,7 @@ def aggregate_fit( # Gradient Average and Update Parameter for FedMeta(Meta-SGD) elif self.algo == "fedmeta_meta_sgd": - grads_results: List[Tuple[NDArrays, int]] = [ # type: ignore + grads_results: List[Tuple[NDArrays, int]] = [ # type: ignore (fit_res.metrics["grads"], fit_res.num_examples) for _, fit_res in results ] From f52dd48d51a16ab8d667119a56babd08573d9110 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Thu, 12 Oct 2023 15:58:03 +0000 Subject: [PATCH 128/133] attempt at fix --- .../tutorial-series-get-started-with-flower-pytorch.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb index 41c9254e9d69..a43afe33d648 100644 --- a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb +++ b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb @@ -40,7 +40,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -q flwr[simulation] torch torchvision matplotlib" + "!pip install -q flwr[simulation] torch torchvision matplotlib " ] }, { From e19180854405e4dba65782ac0ac66c1bb4b5be1e Mon Sep 17 00:00:00 2001 From: jafermarq Date: Thu, 12 Oct 2023 15:58:37 +0000 Subject: [PATCH 129/133] fix? --- .../tutorial-series-get-started-with-flower-pytorch.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb index a43afe33d648..41c9254e9d69 100644 --- a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb +++ b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb @@ -40,7 +40,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -q flwr[simulation] torch torchvision matplotlib " + "!pip install -q flwr[simulation] torch torchvision matplotlib" ] }, { From e3ed392653192c3ccc206cadb6cdfcb935e09b54 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Thu, 12 Oct 2023 16:04:57 +0000 Subject: [PATCH 130/133] changed permissions, does it fix? --- doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb | 0 doc/source/tutorial-series-what-is-federated-learning.ipynb | 0 2 files changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb mode change 100644 => 100755 doc/source/tutorial-series-what-is-federated-learning.ipynb diff --git a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb old mode 100644 new mode 100755 diff --git a/doc/source/tutorial-series-what-is-federated-learning.ipynb b/doc/source/tutorial-series-what-is-federated-learning.ipynb old mode 100644 new mode 100755 From 48d7cb95870421066a65fb99d754dfdc402cf4e3 Mon Sep 17 00:00:00 2001 From: jafermarq Date: Thu, 12 Oct 2023 16:51:20 +0000 Subject: [PATCH 131/133] now fixing pillow to 9.5, minior edit to readme --- baselines/fedmeta/README.md | 3 --- baselines/fedmeta/pyproject.toml | 1 + 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index d8b3e6792d3b..03133e4cbef4 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -77,9 +77,6 @@ poetry shell ```bash # clone LEAF repo git clone https://github.com/TalwalkarLab/leaf.git -# prepare the Leaf GitHub libraries. -pip3 install numpy -pip3 install pillow # navigate to data directory and then the dataset cd leaf/data/femnist diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml index 2f7802d07234..cbaa9bb5d110 100644 --- a/baselines/fedmeta/pyproject.toml +++ b/baselines/fedmeta/pyproject.toml @@ -44,6 +44,7 @@ matplotlib = "3.7.1" scikit-learn = "1.3.1" torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"} torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"} +pillow = "9.5.0" # needed <10.0.0 for LEAF repo scripts [tool.poetry.dev-dependencies] From 79b4ce3f0bf50fc0b735a78153b3cebfe79737ff Mon Sep 17 00:00:00 2001 From: jafermarq Date: Thu, 12 Oct 2023 23:42:50 +0000 Subject: [PATCH 132/133] in changelog --- doc/source/ref-changelog.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md index 05ad1a64f1a2..d567d9366c6b 100644 --- a/doc/source/ref-changelog.md +++ b/doc/source/ref-changelog.md @@ -20,6 +20,8 @@ - FedProx ([#2210](https://github.com/adap/flower/pull/2210), [#2286](https://github.com/adap/flower/pull/2286)) + - FedMeta [#2438](https://github.com/adap/flower/pull/2438) + - Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290)) - **Update Flower Examples** ([#2384](https://github.com/adap/flower/pull/2384)), ([#2425](https://github.com/adap/flower/pull/2425)) From e206895d482b9b553b42edf79dfe65c8583ecbec Mon Sep 17 00:00:00 2001 From: jafermarq Date: Mon, 16 Oct 2023 08:31:36 +0000 Subject: [PATCH 133/133] updated readme based on slack discussion --- baselines/fedmeta/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index 03133e4cbef4..a1ed982f8bf2 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -20,7 +20,7 @@ dataset: [FEMNIST, SHAKESPEARE] **Datasets:** FEMNIST and SHAKESPEARE from Leaf Federated Learning Dataset -**Hardware Setup:** These experiments were run on a machine with 16 CPU threads and 1 GPU(GeForce RTX 2080 Ti). **However, the FedMeta experiment using the Shakespeare dataset required more computing power (more than 4 GPUs).** Out of Memory errors may occur with some clients, but federated learning can continue to operate. +**Hardware Setup:** These experiments were run on a machine with 16 CPU threads and 1 GPU(GeForce RTX 2080 Ti). **FedMeta experiment using the Shakespeare dataset required more computing power.** Out of Memory errors may occur with some clients, but federated learning can continue to operate. On a GPU with more VRAM (A6000 with 48GB) no clients failed. **Contributors:** Jinsoo Kim and Kangyoon Lee

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zKV4$Fs7nEaha^-~qIG5g@?Zeqoo(`y!rkkzgy4G+dLU%HDRLsDw0P_PCgkIuBA7?+ zx-6L;Oyp}2gGtNm;0%m@O5TkNuWbiD3D7b#-$~)qNd~Cnr_od#f?z&__;6c1(u!y0 z1#yzz)pZ|K|D<;H^gMp~@-t|v*mr&35>*6|J5woz+UWye5CZ`VAQ9F|3p*7lX=!{} zcb$`UvhI>{yR@8te>+$8=oA4Hd-8E?Og}?8pyustg(f(UpOWqFr=g*taOabq zDebvxCw?%3q^1fPJqDER*JEGQa_0)Q;bsz#wY5|1bN#dc)%doA#8XBw3t_04N4&K2 z-1CZx3bLA&VF^~1z&eon^nsyg9W)-HDsJ4^{dC$qFg%AQ`r`<82JVx!Mso!hD{m@<+-f4&2PJ`LRaqjW%WtXbKY@CS5dUKJG!S~|Md zAmEOG*-$J@7-~l z{(b0~qGY$+7Dmg$A^-yES);czi=6A1y`|hjs-inykJPEZ{1=`XZH#jo)x|u9LnN&MIva16g* zj;aWOp~ihaLAG}*G34?afOA`3zka;{bnCy(0jO|WBQO;udGz)r%e_=WX7H0oHPRZx6_#uyNZ%(I(XyAc=k2H1fm#SNZ|6k Date: Sat, 7 Oct 2023 14:23:15 +0900 Subject: [PATCH 113/133] update utill.py - add grid line --- baselines/fedmeta/fedmeta/utils.py | 49 +++++++++--------------------- 1 file changed, 15 insertions(+), 34 deletions(-) diff --git a/baselines/fedmeta/fedmeta/utils.py b/baselines/fedmeta/fedmeta/utils.py index 592f1aae3742..0da04e826d95 100644 --- a/baselines/fedmeta/fedmeta/utils.py +++ b/baselines/fedmeta/fedmeta/utils.py @@ -117,10 +117,10 @@ def plot_from_pkl(directory="."): """ color_mapping = { - "fedavg.pkl": "green", - "fedavg_meta.pkl": "blue", - "fedmeta_maml.pkl": "orange", - "fedmeta_meta_sgd.pkl": "red", + "fedavg.pkl": "#66CC00", + "fedavg_meta.pkl": "#3333CC", + "fedmeta_maml.pkl": "#FFCC00", + "fedmeta_meta_sgd.pkl": "#CC0000", } pkl_files = [f for f in os.listdir(directory) if f.endswith('.pkl')] @@ -132,47 +132,28 @@ def plot_from_pkl(directory="."): data = pickle.load(f) all_data[file] = data - # plt.figure(figsize=(14, 6)) - # - # # Acc graph - # plt.subplot(1, 2, 1) - # for file, data in all_data.items(): - # accuracies = [acc for _, acc in data["accuracy"]['accuracy']] - # legend_ = file[:-4] if file.endswith('.pkl') else file - # plt.plot(accuracies, label=legend_, color=color_mapping.get(file, "black")) - # plt.title("Accuracy") - # plt.legend() - # - # # Loss graph - # plt.subplot(1, 2, 2) - # for file, data in all_data.items(): - # loss = [loss for _, loss in data["loss"]] - # legend_ = file[:-4] if file.endswith('.pkl') else file - # plt.plot(loss, label=legend_, color=color_mapping.get(file, "black")) - # plt.title("Loss") - # plt.legend() - # - # plt.tight_layout() - - plt.figure(figsize=(7, 12)) # figsize 변경 + plt.figure(figsize=(7, 12)) # Acc graph - plt.subplot(2, 1, 1) # 변경: 첫 번째 인자를 2로, 두 번째 인자를 1로 설정 - for file, data in all_data.items(): + plt.subplot(2, 1, 1) + for file in sorted(all_data.keys()): + data = all_data[file] accuracies = [acc for _, acc in data["accuracy"]['accuracy']] legend_ = file[:-4] if file.endswith('.pkl') else file - plt.plot(accuracies, label=legend_, color=color_mapping.get(file, "black")) + plt.plot(accuracies, label=legend_, color=color_mapping.get(file, "black"), linewidth=3) plt.title("Accuracy") + plt.grid(True) plt.legend() - # Loss graph - plt.subplot(2, 1, 2) # 변경: 첫 번째 인자를 2로, 두 번째 인자를 1로 설정 - for file, data in all_data.items(): + plt.subplot(2, 1, 2) + for file in sorted(all_data.keys()): + data = all_data[file] loss = [loss for _, loss in data["loss"]] legend_ = file[:-4] if file.endswith('.pkl') else file - plt.plot(loss, label=legend_, color=color_mapping.get(file, "black")) + plt.plot(loss, label=legend_, color=color_mapping.get(file, "black"), linewidth=3) plt.title("Loss") plt.legend() + plt.grid(True) plt.tight_layout() From 3ccb9260780b843efa4e0bc9ff89aef6c6b7fdf2 Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Mon, 9 Oct 2023 23:05:08 +0900 Subject: [PATCH 114/133] Fixed code for "./dev/format-baseline.sh fedmeta" and "./dev/test-baseline.sh fedmeta" --- baselines/fedmeta/fedmeta/client.py | 119 +++++---- .../fedmeta/fedmeta/conf/data/femnist.yaml | 2 +- baselines/fedmeta/fedmeta/dataset.py | 251 ++++++++++++------ .../fedmeta/fedmeta/dataset_preparation.py | 130 +++++---- .../fedmeta/fedmeta/fedmeta_client_manager.py | 74 +++--- baselines/fedmeta/fedmeta/main.py | 27 +- baselines/fedmeta/fedmeta/models.py | 202 +++++++------- baselines/fedmeta/fedmeta/server.py | 1 + baselines/fedmeta/fedmeta/strategy.py | 167 ++++++------ baselines/fedmeta/fedmeta/utils.py | 97 ++++--- 10 files changed, 571 insertions(+), 499 deletions(-) diff --git a/baselines/fedmeta/fedmeta/client.py b/baselines/fedmeta/fedmeta/client.py index 806583665d42..bd77e04b6a72 100644 --- a/baselines/fedmeta/fedmeta/client.py +++ b/baselines/fedmeta/fedmeta/client.py @@ -1,35 +1,35 @@ -"""Define your client class and a function to construct such clients. -""" +"""Define your client class and a function to construct such clients.""" from collections import OrderedDict from typing import Callable, Dict, List, Tuple -from omegaconf import DictConfig -from hydra.utils import instantiate import flwr as fl -from flwr.common.typing import NDArrays, Scalar - import torch import torch.nn +from flwr.common.typing import NDArrays, Scalar +from hydra.utils import instantiate +from omegaconf import DictConfig from torch.utils.data import DataLoader -from fedmeta.models import train, test, train_meta, test_meta +from fedmeta.models import test, test_meta, train, train_meta + +# pylint: disable=too-many-instance-attributes +class FlowerClient(fl.client.NumPyClient): + """Standard Flower client for Local training.""" -class FlowerClient( - fl.client.NumPyClient -): + # pylint: disable=too-many-arguments def __init__( - self, - net: torch.nn.Module, - trainloaders: DataLoader, - valloaders: DataLoader, - cid: str, - device: torch.device, - num_epochs: int, - learning_rate: float, - gradient_step: int - ) -> object: + self, + net: torch.nn.Module, + trainloaders: DataLoader, + valloaders: DataLoader, + cid: str, + device: torch.device, + num_epochs: int, + learning_rate: float, + gradient_step: int, + ): self.net = net self.trainloaders = trainloaders self.valloaders = valloaders @@ -40,96 +40,102 @@ def __init__( self.gradient_step = gradient_step def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays: - """Returns the parameters of the current net.""" + """Return the parameters of the current net.""" return [val.cpu().numpy() for _, val in self.net.state_dict().items()] def set_parameters(self, parameters: NDArrays) -> None: - """Changes the parameters of the model using the given ones.""" + """Change the parameters of the model using the given ones.""" params_dict = zip(self.net.state_dict().keys(), parameters) state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict}) self.net.load_state_dict(state_dict, strict=True) - def fit( - self, parameters: NDArrays, config: Dict[str, Scalar] + def fit( # type: ignore + self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[NDArrays, int, Dict]: - """Implements distributed fit function for a given client.""" + """Implement distributed fit function for a given client.""" self.set_parameters(parameters) algo = config["algo"] # Total number of data for Weighted Avg and Grad - total_len = len(self.trainloaders['qry'][self.cid].dataset) + len(self.trainloaders['sup'][self.cid].dataset) + total_len = len(self.trainloaders["qry"][self.cid].dataset) + len( + self.trainloaders["sup"][self.cid].dataset + ) # FedAvg & FedAvg(Meta) train basic Learning - if algo == 'fedavg' or algo == 'fedavg_meta': + if algo in ('fedavg', 'fedavg_meta'): loss = train( self.net, - self.trainloaders['sup'][self.cid], - self.trainloaders['qry'][self.cid], + self.trainloaders["sup"][self.cid], self.device, epochs=self.num_epochs, - learning_rate=self.learning_rate + learning_rate=self.learning_rate, ) return self.get_parameters({}), total_len, {"loss": loss} # FedMeta(MAML) & FedMeta(Meta-SGD) train inner and outer loop - elif algo == 'fedmeta_maml' or algo == 'fedmeta_meta_sgd': + if algo in ('fedmeta_maml', 'fedmeta_meta_sgd'): alpha = config["alpha"] - loss, grads = train_meta( + loss, grads = train_meta( # type: ignore self.net, - self.trainloaders['sup'][self.cid], - self.trainloaders['qry'][self.cid], + self.trainloaders["sup"][self.cid], + self.trainloaders["qry"][self.cid], alpha, self.device, self.gradient_step, ) return self.get_parameters({}), total_len, {"loss": loss, "grads": grads} + raise ValueError("Unsupported algorithm") - def evaluate( - self, parameters: NDArrays, config: Dict[str, Scalar] + def evaluate( # type: ignore + self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[float, int, Dict]: - """Implements distributed evaluation for a given client.""" + """Implement distributed evaluation for a given client.""" self.set_parameters(parameters) + algo = config["algo"] # Total number of data for Weighted Avg and Grad - total_len = len(self.valloaders['qry'][self.cid].dataset) + len(self.valloaders['sup'][self.cid].dataset) + total_len = len(self.valloaders["qry"][self.cid].dataset) + len( + self.valloaders["sup"][self.cid].dataset + ) # FedAvg & FedAvg(Meta) train basic Learning - if config["algo"] == 'fedavg' or config["algo"] == 'fedavg_meta': + if algo in ("fedavg", "fedavg_meta"): loss, accuracy = test( self.net, - self.valloaders['sup'][self.cid], - self.valloaders['qry'][self.cid], + self.valloaders["sup"][self.cid], + self.valloaders["qry"][self.cid], self.device, - config["algo"], - config["data"], + algo=str(config["algo"]), + data=str(config["data"]), learning_rate=self.learning_rate, ) return float(loss), total_len, {"correct": accuracy, "loss": loss} # FedMeta(MAML) & FedMeta(Meta-SGD) train inner and outer loop - elif config["algo"] == 'fedmeta_maml' or config["algo"] == 'fedmeta_meta_sgd': + if algo in ("fedmeta_maml", "fedmeta_meta_sgd"): alpha = config["alpha"] loss, accuracy = test_meta( self.net, - self.valloaders['sup'][self.cid], - self.valloaders['qry'][self.cid], + self.valloaders["sup"][self.cid], + self.valloaders["qry"][self.cid], alpha, self.device, self.gradient_step, ) return float(loss), total_len, {"correct": float(accuracy), "loss": loss} + raise ValueError("Unsupported algorithm") +# pylint: disable=too-many-arguments def gen_client_fn( - num_epochs: int, - trainloaders: List[DataLoader], - valloaders: List[DataLoader], - learning_rate: float, - model: DictConfig, - gradient_step: int, + num_epochs: int, + trainloaders: List[DataLoader], + valloaders: List[DataLoader], + learning_rate: float, + model: DictConfig, + gradient_step: int, ) -> Callable[[str], FlowerClient]: - - """Generates the client function that creates the Flower Clients. + """Generate the client function that creates the Flower Clients. Parameters ---------- @@ -159,8 +165,7 @@ def gen_client_fn( def client_fn(cid: str) -> FlowerClient: """Create a Flower client representing a single organization.""" - - print(f'cid : {cid}') + print(f"cid : {cid}") # Load model torch.manual_seed(42) @@ -176,7 +181,7 @@ def client_fn(cid: str) -> FlowerClient: device, num_epochs, learning_rate, - gradient_step + gradient_step, ) return client_fn diff --git a/baselines/fedmeta/fedmeta/conf/data/femnist.yaml b/baselines/fedmeta/fedmeta/conf/data/femnist.yaml index d43f8dabb43f..7f24eed1a951 100644 --- a/baselines/fedmeta/fedmeta/conf/data/femnist.yaml +++ b/baselines/fedmeta/fedmeta/conf/data/femnist.yaml @@ -4,7 +4,7 @@ # a similar configuration structure and hence be easy to customise) model: - _target_: fedmeta.models.CNN_network # model config + _target_: fedmeta.models.FemnistNetwork # model config client_resources: num_cpus: 4 diff --git a/baselines/fedmeta/fedmeta/dataset.py b/baselines/fedmeta/fedmeta/dataset.py index db11e2324f09..25ff5201bfdb 100644 --- a/baselines/fedmeta/fedmeta/dataset.py +++ b/baselines/fedmeta/fedmeta/dataset.py @@ -9,145 +9,226 @@ defined here of course. """ -from omegaconf import DictConfig -from typing import Tuple +from typing import Dict, List, Tuple import numpy as np import torch import torchvision.transforms as transforms +from omegaconf import DictConfig from torch.utils.data import DataLoader, Dataset -from fedmeta.dataset_preparation import _partition_data, split_train_validation_test_clients -from fedmeta.utils import word_to_indices, letter_to_vec +from fedmeta.dataset_preparation import ( + _partition_data, + split_train_validation_test_clients, +) +from fedmeta.utils import letter_to_vec, word_to_indices class ShakespeareDataset(Dataset): - def __init__(self, data): - """ - [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf) + """ + [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf). - We imported the preprocessing method for the Shakespeare dataset from GitHub. + We imported the preprocessing method for the Shakespeare dataset from GitHub. - word_to_indices : returns a list of character indices - sentences_to_indices : returns one-hot vector with given size and value 1 at given index - letter_to_vec : returns one-hot representation of given letter + word_to_indices : returns a list of character indices + sentences_to_indices: converts an index to a one-hot vector of a given size. + letter_to_vec : returns one-hot representation of given letter - """ - sentence, label = data['x'], data['y'] + """ + + def __init__(self, data): + sentence, label = data["x"], data["y"] sentences_to_indices = [word_to_indices(word) for word in sentence] sentences_to_indices = np.array(sentences_to_indices) self.sentences_to_indices = np.array(sentences_to_indices, dtype=np.int64) - self.labels = np.array([letter_to_vec(letter) for letter in label], dtype=np.int64) + self.labels = np.array( + [letter_to_vec(letter) for letter in label], dtype=np.int64 + ) def __len__(self): + """Return the number of labels present in the dataset. + + Returns + ------- + int: The total number of labels. + """ return len(self.labels) def __getitem__(self, index): + """Retrieve the data and its corresponding label at a given index. + + Args: + index (int): The index of the data item to fetch. + + Returns + ------- + tuple: (data tensor, label tensor) + """ data, target = self.sentences_to_indices[index], self.labels[index] return torch.tensor(data), torch.tensor(target) class FemnistDataset(Dataset): - def __init__(self, dataset, transform): - """ - Using FemnistDataset for CNN_network() + """ + [LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf). - """ - self.x = dataset['x'] - self.y = dataset['y'] + We imported the preprocessing method for the Femnist dataset from GitHub. + """ + + def __init__(self, dataset, transform): + self.x = dataset["x"] + self.y = dataset["y"] self.transform = transform def __getitem__(self, index): - input_data = np.array(self.x[index]).reshape(28, 28, 1) + """Retrieve the input data and its corresponding label at a given index. + + Args: + index (int): The index of the data item to fetch. + + Returns + ------- + tuple: + - input_data (torch.Tensor): Reshaped and optionally transformed data. + - target_data (int or torch.Tensor): Label for the input data. + """ + input_data = np.array(self.x[index]).reshape(28, 28) if self.transform: input_data = self.transform(input_data) target_data = self.y[index] return input_data.to(torch.float32), target_data def __len__(self): + """Return the number of labels present in the dataset. + + Returns + ------- + int: The total number of labels. + """ return len(self.y) def load_datasets( - config: DictConfig, - path: str, + config: DictConfig, + path: str, ) -> Tuple[DataLoader, DataLoader, DataLoader]: + """Create the dataloaders to be fed into the model. + + Parameters + ---------- + config: DictConfig + data: float + Used data type + batch_size : int + The size of the batches to be fed into the model, + by default 10 + support_ratio : float + The ratio of Support set for each client.(between 0 and 1) + by default 0.2 + path : str + The path where the leaf dataset was downloaded + + Returns + ------- + Tuple[DataLoader, DataLoader, DataLoader] """ - Creates the dataloaders to be fed into the model. - - Parameters - ---------- - config: DictConfig - Parameterises the dataset partitioning process - batch_size : int - The size of the batches to be fed into the model, - by default 10 - support_ratio : float - The ratio of Support set for each client.(between 0 and 1) - by default 0.2 - path : str - The path where the leaf dataset was downloaded - - Returns - ------- - Tuple[DataLoader, DataLoader, DataLoader] - The DataLoader for training, the DataLoader for validation, the DataLoader for testing. - - """ - dataset = _partition_data( - data_type=config.data, - dir_path=path, - support_ratio=config.support_ratio + data_type=config.data, dir_path=path, support_ratio=config.support_ratio ) # Client list : 0.8, 0.1, 0.1 - clients_list = split_train_validation_test_clients( - dataset[0]['users'] - ) + clients_list = split_train_validation_test_clients(dataset[0]["users"]) - trainloaders = {'sup': [], 'qry': []} - valloaders = {'sup': [], 'qry': []} - testloaders = {'sup': [], 'qry': []} + trainloaders: Dict[str, List[DataLoader]] = {"sup": [], "qry": []} + valloaders: Dict[str, List[DataLoader]] = {"sup": [], "qry": []} + testloaders: Dict[str, List[DataLoader]] = {"sup": [], "qry": []} data_type = config.data - if data_type == 'femnist': + if data_type == "femnist": transform = transforms.Compose([transforms.ToTensor()]) for user in clients_list[0]: - trainloaders['sup'].append( - DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=config.batch_size, - shuffle=True)) - trainloaders['qry'].append( - DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=config.batch_size)) + trainloaders["sup"].append( + DataLoader( + FemnistDataset(dataset[0]["user_data"][user], transform), + batch_size=config.batch_size, + shuffle=True, + ) + ) + trainloaders["qry"].append( + DataLoader( + FemnistDataset(dataset[1]["user_data"][user], transform), + batch_size=config.batch_size, + ) + ) for user in clients_list[1]: - valloaders['sup'].append( - DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=config.batch_size)) - valloaders['qry'].append( - DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=config.batch_size)) + valloaders["sup"].append( + DataLoader( + FemnistDataset(dataset[0]["user_data"][user], transform), + batch_size=config.batch_size, + ) + ) + valloaders["qry"].append( + DataLoader( + FemnistDataset(dataset[1]["user_data"][user], transform), + batch_size=config.batch_size, + ) + ) for user in clients_list[2]: - testloaders['sup'].append( - DataLoader(FemnistDataset(dataset[0]['user_data'][user], transform), batch_size=config.batch_size)) - testloaders['qry'].append( - DataLoader(FemnistDataset(dataset[1]['user_data'][user], transform), batch_size=config.batch_size)) - - elif data_type == 'shakespeare': + testloaders["sup"].append( + DataLoader( + FemnistDataset(dataset[0]["user_data"][user], transform), + batch_size=config.batch_size, + ) + ) + testloaders["qry"].append( + DataLoader( + FemnistDataset(dataset[1]["user_data"][user], transform), + batch_size=config.batch_size, + ) + ) + + elif data_type == "shakespeare": for user in clients_list[0]: - trainloaders['sup'].append( - DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, - shuffle=True)) - trainloaders['qry'].append( - DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=config.batch_size)) + trainloaders["sup"].append( + DataLoader( + ShakespeareDataset(dataset[0]["user_data"][user]), + batch_size=config.batch_size, + shuffle=True, + ) + ) + trainloaders["qry"].append( + DataLoader( + ShakespeareDataset(dataset[1]["user_data"][user]), + batch_size=config.batch_size, + ) + ) for user in clients_list[1]: - valloaders['sup'].append( - DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, - shuffle=True)) - valloaders['qry'].append( - DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=config.batch_size)) + valloaders["sup"].append( + DataLoader( + ShakespeareDataset(dataset[0]["user_data"][user]), + batch_size=config.batch_size, + shuffle=True, + ) + ) + valloaders["qry"].append( + DataLoader( + ShakespeareDataset(dataset[1]["user_data"][user]), + batch_size=config.batch_size, + ) + ) for user in clients_list[2]: - testloaders['sup'].append( - DataLoader(ShakespeareDataset(dataset[0]['user_data'][user]), batch_size=config.batch_size, - shuffle=True)) - testloaders['qry'].append( - DataLoader(ShakespeareDataset(dataset[1]['user_data'][user]), batch_size=config.batch_size)) + testloaders["sup"].append( + DataLoader( + ShakespeareDataset(dataset[0]["user_data"][user]), + batch_size=config.batch_size, + shuffle=True, + ) + ) + testloaders["qry"].append( + DataLoader( + ShakespeareDataset(dataset[1]["user_data"][user]), + batch_size=config.batch_size, + ) + ) return trainloaders, valloaders, testloaders diff --git a/baselines/fedmeta/fedmeta/dataset_preparation.py b/baselines/fedmeta/fedmeta/dataset_preparation.py index ac0c642a1dac..9c99cddddc5a 100644 --- a/baselines/fedmeta/fedmeta/dataset_preparation.py +++ b/baselines/fedmeta/fedmeta/dataset_preparation.py @@ -8,20 +8,18 @@ """ import json import os -from typing import List, Optional, Tuple, Dict, DefaultDict from collections import defaultdict +from typing import Any, DefaultDict, Dict, List, Tuple + import numpy as np from sklearn.model_selection import train_test_split -def _read_dataset( - path: str -) -> Tuple[List, DefaultDict, List]: - """ - Read (if necessary) and returns the leaf dataset. +def _read_dataset(path: str) -> Tuple[List, DefaultDict, List]: + """Read (if necessary) and returns the leaf dataset. Parameters - ---------- + ---------- path : str The path where the leaf dataset was downloaded @@ -29,35 +27,33 @@ def _read_dataset( ------- Tuple[user, data[x,y], num_total_data] The dataset for training and the dataset for testing. - """ users = [] - data = defaultdict(lambda: None) + data: DefaultDict[str, Any] = defaultdict(lambda: None) num_example = [] - files = [f for f in os.listdir(path) if f.endswith('.json')] + files = [f for f in os.listdir(path) if f.endswith(".json")] for file_name in files: - with open(f'{path}/{file_name}') as f: - dataset = json.load(f) - users.extend(dataset['users']) - data.update(dataset['user_data']) - num_example.extend(dataset['num_samples']) + with open(f"{path}/{file_name}") as file: + dataset = json.load(file) + users.extend(dataset["users"]) + data.update(dataset["user_data"]) + num_example.extend(dataset["num_samples"]) - users = list(sorted(data.keys())) + users = sorted(data.keys()) return users, data, num_example def support_query_split( - data: DefaultDict, - label: List, - support_ratio: float, + data, + label, + support_ratio: float, ) -> Tuple[List, List, List, List]: - """ - Separate support set and query set + """Separate support set and query set. Parameters - ---------- + ---------- data: DefaultDict, Raw all Datasets label: List, @@ -70,24 +66,23 @@ def support_query_split( ------- Tuple[List, List, List, List] Support set and query set classification of data and labels - """ - - x_train, x_test, y_train, y_test = train_test_split(data, label, train_size=support_ratio, stratify=label, random_state=42) + x_train, x_test, y_train, y_test = train_test_split( + data, label, train_size=support_ratio, stratify=label, random_state=42 + ) return x_train, x_test, y_train, y_test def split_train_validation_test_clients( - clients: List, - train_rate: Optional[float] = 0.8, - val_rate: Optional[float] = 0.1, + clients: List, + train_rate: float = 0.8, + val_rate: float = 0.1, ) -> Tuple[List[str], List[str], List[str]]: - """ - Classification of all clients into train clients, valid clients, and test clients + """Classification of all clients into train, valid, and test. Parameters - ---------- + ---------- clients: List, Full list of clients for the sampled leaf dataset. train_rate: float, optional @@ -101,32 +96,30 @@ def split_train_validation_test_clients( ------- Tuple[List, List, List] List of each train client, valid client, and test client - """ np.random.seed(42) train_rate = int(train_rate * len(clients)) val_rate = int(val_rate * len(clients)) - test_rate = len(clients) - train_rate - val_rate index = np.random.permutation(len(clients)) trans_numpy = np.asarray(clients) train_clients = trans_numpy[index[:train_rate]].tolist() - val_clients = trans_numpy[index[train_rate:train_rate + val_rate]].tolist() - test_clients = trans_numpy[index[train_rate + val_rate:]].tolist() + val_clients = trans_numpy[index[train_rate : train_rate + val_rate]].tolist() + test_clients = trans_numpy[index[train_rate + val_rate :]].tolist() return train_clients, val_clients, test_clients +# pylint: disable=too-many-locals def _partition_data( - data_type: str, - dir_path: str, - support_ratio: float, + data_type: str, + dir_path: str, + support_ratio: float, ) -> Tuple[Dict, Dict]: - """ - Classification of support sets and query sets by client + """Classification of support sets and query sets by client. Parameters - ---------- + ---------- data_type: str, The type of femnist for classification or shakespeare for regression dir_path: str, @@ -139,28 +132,29 @@ def _partition_data( ------- Tuple[Dict, Dict] Return support set and query set for total data - """ - train_path = f'{dir_path}/train' - test_path = f'{dir_path}/test' + train_path = f"{dir_path}/train" + test_path = f"{dir_path}/test" - train_users, train_data, train_num = _read_dataset(train_path) - test_users, test_data, test_num = _read_dataset(test_path) + train_users, train_data, _ = _read_dataset(train_path) + _, test_data, _ = _read_dataset(test_path) - all_dataset = {'users': [], 'user_data': {}, 'num_samples': []} - support_dataset = {'users': [], 'user_data': {}, 'num_samples': []} - query_dataset = {'users': [], 'user_data': {}, 'num_samples': []} + all_dataset: Dict[str, Any] = {"users": [], "user_data": {}, "num_samples": []} + support_dataset: Dict[str, Any] = {"users": [], "user_data": {}, "num_samples": []} + query_dataset: Dict[str, Any] = {"users": [], "user_data": {}, "num_samples": []} for user in train_users: - all_x = np.asarray(train_data[user]['x'] + test_data[user]['x']) - all_y = np.asarray(train_data[user]['y'] + test_data[user]['y']) + all_x = np.asarray(train_data[user]["x"] + test_data[user]["x"]) + all_y = np.asarray(train_data[user]["y"] + test_data[user]["y"]) - if data_type == 'femnist': + if data_type == "femnist": unique, counts = np.unique(all_y, return_counts=True) class_counts = dict(zip(unique, counts)) # Find classes with only one sample - classes_to_remove = [cls for cls, count in class_counts.items() if count == 1] + classes_to_remove = [ + cls for cls, count in class_counts.items() if count == 1 + ] # Filter out the samples of those classes mask = np.isin(all_y, classes_to_remove, invert=True) @@ -170,23 +164,27 @@ def _partition_data( # Client filtering for support set and query set classification try: - sup_x, qry_x, sup_y, qry_y = support_query_split(all_x, all_y, support_ratio) - except Exception as e: + sup_x, qry_x, sup_y, qry_y = support_query_split( + all_x, all_y, support_ratio + ) + except Exception: # pylint: disable=broad-except continue - elif data_type == 'shakespeare': - sup_x, qry_x, sup_y, qry_y = train_test_split(all_x, all_y, train_size=support_ratio, random_state=42) + elif data_type == "shakespeare": + sup_x, qry_x, sup_y, qry_y = train_test_split( + all_x, all_y, train_size=support_ratio, random_state=42 + ) - all_dataset['users'].append(user) - all_dataset['user_data'][user] = {'x': all_x.tolist(), 'y': all_y.tolist()} - all_dataset['num_samples'].append(len(all_y.tolist())) + all_dataset["users"].append(user) + all_dataset["user_data"][user] = {"x": all_x.tolist(), "y": all_y.tolist()} + all_dataset["num_samples"].append(len(all_y.tolist())) - support_dataset['users'].append(user) - support_dataset['user_data'][user] = {'x': sup_x, 'y': sup_y} - support_dataset['num_samples'].append(len(sup_y)) + support_dataset["users"].append(user) + support_dataset["user_data"][user] = {"x": sup_x, "y": sup_y} + support_dataset["num_samples"].append(len(sup_y)) - query_dataset['users'].append(user) - query_dataset['user_data'][user] = {'x': qry_x, 'y': qry_y} - query_dataset['num_samples'].append(len(qry_y)) + query_dataset["users"].append(user) + query_dataset["user_data"][user] = {"x": qry_x, "y": qry_y} + query_dataset["num_samples"].append(len(qry_y)) return support_dataset, query_dataset diff --git a/baselines/fedmeta/fedmeta/fedmeta_client_manager.py b/baselines/fedmeta/fedmeta/fedmeta_client_manager.py index f0e08aa2750b..1b3a38c5b2d5 100644 --- a/baselines/fedmeta/fedmeta/fedmeta_client_manager.py +++ b/baselines/fedmeta/fedmeta/fedmeta_client_manager.py @@ -1,68 +1,56 @@ -from flwr.server.client_manager import SimpleClientManager -from typing import List, Optional -from logging import INFO -from flwr.common.logger import log -from flwr.server.criterion import Criterion -from flwr.server.client_proxy import ClientProxy -import random - - -class evaluate_client_Criterion(Criterion): - def __init__(self, min_evaluate_clients): - self.min_evaluate_clients = min_evaluate_clients +"""Handles clients that are sampled every round. - """Criterion to select evaluate clients.""" - def select( - self, - valid_client: int - ) -> List: - """ - Clients to be used in evaluation should be sampled from the validation client list. - - Parameters - ---------- - valid_client : int - Length of validation client list +In a FedMeta experiment, there is a train and a test client. So we modified the manager +to sample from each list each round. +""" - Returns - ------- - Return client cid list +import random +from logging import INFO +from typing import List, Optional - """ - return [str(result) for result in range(0, valid_client)] +from flwr.common.logger import log +from flwr.server.client_manager import SimpleClientManager +from flwr.server.client_proxy import ClientProxy +from flwr.server.criterion import Criterion -class fedmeta_client_manager(SimpleClientManager): +class FedmetaClientManager(SimpleClientManager): + """In the fit phase, clients must be sampled from the training client list. - """ - In the fit phase, clients must be sampled from the training client list. And in the evaluate stage, clients must be sampled from the validation client list. - So we modify 'fedmeta_client_manager' to sample clients from [cid: List] for each list. - + So we modify 'fedmeta_client_manager' to sample clients from [cid: List] for each + list. """ def __init__(self, valid_client, **kwargs): super().__init__(**kwargs) self.valid_client = valid_client - def sample( - self, - num_clients: int, - server_round: Optional[int] = None, - min_num_clients: Optional[int] = None, - criterion: Optional[Criterion] = None, + # pylint: disable=too-many-arguments + def sample( # pylint: disable=arguments-differ + self, + num_clients: int, + min_num_clients: Optional[int] = None, + criterion: Optional[Criterion] = None, + server_round: Optional[int] = None, + step: Optional[str] = None ) -> List[ClientProxy]: """Sample a number of Flower ClientProxy instances.""" - # Block until at least num_clients are connected. if min_num_clients is None: min_num_clients = num_clients self.wait_for(min_num_clients) # Sample clients which meet the criterion - available_cids = list(self.clients) + if step == 'evaluate': + available_cids = [str(result) for result in range(0, self.valid_client)] + else: + available_cids = list(self.clients) + if criterion is not None: - available_cids = criterion.select(self.valid_client) + available_cids = [ + cid for cid in available_cids if criterion.select(self.clients[cid]) + ] if num_clients > len(available_cids): log( diff --git a/baselines/fedmeta/fedmeta/main.py b/baselines/fedmeta/fedmeta/main.py index 49f454b43bc9..bce21fca1fa6 100644 --- a/baselines/fedmeta/fedmeta/main.py +++ b/baselines/fedmeta/fedmeta/main.py @@ -4,19 +4,19 @@ model is going to be evaluated, etc. At the end, this script saves the results. """ -import hydra -from hydra.core.hydra_config import HydraConfig -from omegaconf import DictConfig, OmegaConf -from hydra.utils import instantiate import os import flwr as fl +import hydra +from hydra.core.hydra_config import HydraConfig +from hydra.utils import instantiate +from omegaconf import DictConfig, OmegaConf import fedmeta.client as client -from fedmeta.fedmeta_client_manager import fedmeta_client_manager from fedmeta.dataset import load_datasets +from fedmeta.fedmeta_client_manager import FedmetaClientManager from fedmeta.strategy import weighted_average -from fedmeta.utils import save_graph_params, plot_from_pkl +from fedmeta.utils import plot_from_pkl, save_graph_params @hydra.main(config_path="conf", config_name="config", version_base=None) @@ -30,13 +30,14 @@ def main(cfg: DictConfig) -> None: algo : FedAvg, FedAvg(Meta), FedMeta(MAML), FedMeta(Meta-SGD) data : Femnist, Shakespeare - """ # print config structured as YAML print(OmegaConf.to_yaml(cfg)) # partition dataset and get dataloaders - trainloaders, valloaders, testloaders= load_datasets(config=cfg.data, path=cfg.path) + trainloaders, valloaders, _ = load_datasets( + config=cfg.data, path=cfg.path + ) # prepare function that will be used to spawn each client client_fn = client.gen_client_fn( @@ -61,13 +62,13 @@ def main(cfg: DictConfig) -> None: # Start Simulation history = fl.simulation.start_simulation( client_fn=client_fn, - num_clients=len(trainloaders['sup']), + num_clients=len(trainloaders["sup"]), config=fl.server.ServerConfig(num_rounds=cfg.data.num_rounds), client_resources={ "num_cpus": cfg.data.client_resources.num_cpus, "num_gpus": cfg.data.client_resources.num_gpus, }, - client_manager=fedmeta_client_manager(valid_client=len(valloaders['qry'])), + client_manager=FedmetaClientManager(valid_client=len(valloaders["qry"])), strategy=strategy, ) @@ -83,7 +84,9 @@ def main(cfg: DictConfig) -> None: print("................") print(history) - output_path = HydraConfig.get().runtime.cwd + '/fedmeta/' + cfg.data.data + '/graph_params' + output_path = ( + HydraConfig.get().runtime.cwd + "/fedmeta/" + cfg.data.data + "/graph_params" + ) os.makedirs(output_path, exist_ok=True) data_params = { @@ -91,7 +94,7 @@ def main(cfg: DictConfig) -> None: "data": cfg.data.data, "loss": history.losses_distributed, "accuracy": history.metrics_distributed, - "path": output_path + "path": output_path, } save_graph_params(data_params) diff --git a/baselines/fedmeta/fedmeta/models.py b/baselines/fedmeta/fedmeta/models.py index 13183d891202..d84a9e0ce98c 100644 --- a/baselines/fedmeta/fedmeta/models.py +++ b/baselines/fedmeta/fedmeta/models.py @@ -6,35 +6,32 @@ the python code at all """ -from typing import Tuple, List +from copy import deepcopy +from typing import List, Tuple import torch import torch.nn as nn from torch.utils.data import DataLoader -from copy import deepcopy - class StackedLSTM(nn.Module): - """ - StackedLSTM architecture. + """StackedLSTM architecture. - As described in Fei Chen 2018 paper : + As described in Fei Chen 2018 paper : - [FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication] - (https://arxiv.org/abs/1802.07876) + [FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication] + (https://arxiv.org/abs/1802.07876) + """ - """ def __init__(self): - super(StackedLSTM, self).__init__() + super().__init__() self.embedding = nn.Embedding(80, 8) self.lstm = nn.LSTM(8, 256, num_layers=2, dropout=0.5, batch_first=True) - self.fc = nn.Linear(256, 80) + self.fully_ = nn.Linear(256, 80) def forward(self, text): - """ - Forward pass of the StackedLSTM. + """Forward pass of the StackedLSTM. Parameters ---------- @@ -45,31 +42,30 @@ def forward(self, text): ------- torch.Tensor The resulting Tensor after it has passed through the network - """ embedded = self.embedding(text) self.lstm.flatten_parameters() lstm_out, _ = self.lstm(embedded) - final_output = self.fc(lstm_out[:, -1, :]) + final_output = self.fully_(lstm_out[:, -1, :]) return final_output -class CNN_network(nn.Module): - """ - Convolutional Neural Network architecture. +class FemnistNetwork(nn.Module): + """Convolutional Neural Network architecture. As described in Fei Chen 2018 paper : [FedMeta: Federated Meta-Learning with Fast Convergence and Efficient Communication] (https://arxiv.org/abs/1802.07876) - """ def __init__(self) -> None: - super(CNN_network, self).__init__() + super().__init__() self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5, padding=2) self.maxpool1 = nn.MaxPool2d(kernel_size=(2, 2)) - self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2) + self.conv2 = nn.Conv2d( + in_channels=32, out_channels=64, kernel_size=5, padding=2 + ) self.maxpool2 = nn.MaxPool2d(kernel_size=(2, 2)) self.linear1 = nn.Linear(7 * 7 * 64, 2048) self.linear2 = nn.Linear(2048, 62) @@ -86,7 +82,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: ------- torch.Tensor The resulting Tensor after it has passed through the network - """ x = torch.relu(self.conv1(x)) x = self.maxpool1(x) @@ -98,16 +93,15 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return x +# pylint: disable=too-many-arguments def train( net: nn.Module, trainloader: DataLoader, - testloader: DataLoader, device: torch.device, epochs: int, learning_rate: float, ) -> Tuple[float]: - """ - Train the network on the training set. + """Train the network on the training set. Parameters ---------- @@ -135,9 +129,7 @@ def train( optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) net.train() for _ in range(epochs): - net, loss = _train_one_epoch( - net, trainloader, device, criterion, optimizer - ) + net, loss = _train_one_epoch(net, trainloader, device, criterion, optimizer) return loss @@ -169,7 +161,6 @@ def _train_one_epoch( The model that has been trained for one epoch. total_loss The Loss that has been trained for one epoch. - """ total_loss = 0.0 @@ -184,6 +175,7 @@ def _train_one_epoch( return net, total_loss +# pylint: disable=too-many-locals def test( net: nn.Module, trainloader: DataLoader, @@ -193,8 +185,7 @@ def test( data: str, learning_rate: float, ) -> Tuple[float, float]: - """ - Evaluate the network on the entire test set. + """Evaluate the network on the entire test set. Parameters ---------- @@ -217,25 +208,28 @@ def test( ------- Tuple[float, float] The loss and the accuracy of the input model on the given data. - """ criterion = torch.nn.CrossEntropyLoss() - if algo == 'fedavg_meta': - total_loss = 0.0 - optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.001) + if algo == "fedavg_meta": + optimizer = torch.optim.Adam( + net.parameters(), lr=learning_rate, weight_decay=0.001 + ) net.train() - if data == 'femnist': + if data == "femnist": for images, labels in trainloader: images, labels = images.to(device), labels.to(device) loss = criterion(net(images), labels) - total_loss += loss * labels.size(0) - total_loss = total_loss / len(trainloader.dataset) - optimizer.zero_grad() - total_loss.backward() - optimizer.step() + optimizer.zero_grad() + loss.backward() + optimizer.step() + # total_loss += loss * labels.size(0) + # total_loss = total_loss / len(trainloader.dataset) + # optimizer.zero_grad() + # total_loss.backward() + # optimizer.step() - elif data == 'shakespeare': + elif data == "shakespeare": for images, labels in trainloader: images, labels = images.to(device), labels.to(device) loss = criterion(net(images), labels) @@ -264,7 +258,7 @@ def train_meta( net: nn.Module, supportloader: DataLoader, queryloader: DataLoader, - alpha, + alpha: torch.nn.ParameterList, device: torch.device, gradient_step: int, ) -> Tuple[float, List]: @@ -272,28 +266,25 @@ def train_meta( Parameters ---------- - net : nn.Module - The neural network to train. - supportloader : DataLoader - The DataLoader containing the data to inner loop train the network on. - queryloader : DataLoader - The DataLoader containing the data to outer loop train the network on. - alpha : int - The learning rate for the optimizer. - device : torch.device - The device on which the model should be trained, either 'cpu' or 'cuda'. - gradient_step : int - The number of inner loop learning + net : nn.Module + The neural network to train. + supportloader : DataLoader + The DataLoader containing the data to inner loop train the network on. + queryloader : DataLoader + The DataLoader containing the data to outer loop train the network on. + alpha : torch.nn.ParameterList + The learning rate for the optimizer. + device : torch.device + The device on which the model should be trained, either 'cpu' or 'cuda'. + gradient_step : int + The number of inner loop learning Returns ------- - nn.Module - The model that has been trained for one meta epoch. - total_loss - The Loss that has been trained for one epoch. - grads - The gradients that has been trained for one epoch. - + total_loss + The Loss that has been trained for one epoch. + grads + The gradients that has been trained for one epoch. """ criterion = torch.nn.CrossEntropyLoss() for _ in range(1): @@ -302,7 +293,7 @@ def train_meta( ) return loss, grads - +# pylint: disable=too-many-locals def _train_meta_one_epoch( net: nn.Module, supportloader: DataLoader, @@ -311,36 +302,32 @@ def _train_meta_one_epoch( criterion: torch.nn.CrossEntropyLoss, device: torch.device, gradient_step: int, -) -> nn.Module: - """ - Train for one epoch. +) -> Tuple[float, List]: + """Train for one epoch. Parameters ---------- - net : nn.Module - The neural network to train. - supportloader : DataLoader - The DataLoader containing the data to inner loop train the network on. - queryloader : DataLoader - The DataLoader containing the data to outer loop train the network on. - alpha : torch.nn.ParameterList - The learning rate for the optimizer. - criterion : torch.nn.CrossEntropyLoss - The loss function to use for training - device : torch.device - The device on which the model should be trained, either 'cpu' or 'cuda'. - gradient_step : int - The number of inner loop learning + net : nn.Module + The neural network to train. + supportloader : DataLoader + The DataLoader containing the data to inner loop train the network on. + queryloader : DataLoader + The DataLoader containing the data to outer loop train the network on. + alpha : torch.nn.ParameterList + The learning rate for the optimizer. + criterion : torch.nn.CrossEntropyLoss + The loss function to use for training + device : torch.device + The device on which the model should be trained, either 'cpu' or 'cuda'. + gradient_step : int + The number of inner loop learning Returns ------- - nn.Module - The model that has been trained for one meta epoch. - total_loss - The Loss that has been trained for one epoch. - grads - The gradients that has been trained for one epoch. - + total_loss + The Loss that has been trained for one epoch. + grads + The gradients that has been trained for one epoch. """ num_adaptation_steps = gradient_step train_net = deepcopy(net) @@ -355,14 +342,16 @@ def _train_meta_one_epoch( loss_sum += loss * labels.size(0) sup_num_sample.append(labels.size(0)) sup_total_loss.append(loss * labels.size(0)) - grads = torch.autograd.grad(loss, list(train_net.parameters()), create_graph=True, retain_graph=True) + grads = torch.autograd.grad( + loss, list(train_net.parameters()), create_graph=True, retain_graph=True + ) - for p, g, a in zip(train_net.parameters(), grads, alpha): - p.data = p.data - a * g + for param, grad_, alphas in zip(train_net.parameters(), grads, alpha): + param.data = param.data - alphas * grad_ - for p in train_net.parameters(): - if p.grad is not None: - p.grad.zero_() + for param in train_net.parameters(): + if param.grad is not None: + param.grad.zero_() qry_total_loss = [] qry_num_sample = [] @@ -376,11 +365,11 @@ def _train_meta_one_epoch( loss_sum = loss_sum / sum(qry_num_sample) grads = torch.autograd.grad(loss_sum, list(train_net.parameters())) - for p in train_net.parameters(): - if p.grad is not None: - p.grad.zero_() + for param in train_net.parameters(): + if param.grad is not None: + param.grad.zero_() - grads = [g.cpu().numpy() for g in grads] + grads = [grad_.cpu().numpy() for grad_ in grads] loss = sum(sup_total_loss) / sum(sup_num_sample) return loss, grads @@ -393,8 +382,7 @@ def test_meta( device: torch.device, gradient_step: int, ) -> Tuple[float, float]: - """ - Evaluate the network on the entire test set. + """Evaluate the network on the entire test set. Parameters ---------- @@ -431,14 +419,16 @@ def test_meta( loss_sum += loss * labels.size(0) sup_num_sample.append(labels.size(0)) sup_total_loss.append(loss) - grads = torch.autograd.grad(loss, list(test_net.parameters()), create_graph=True, retain_graph=True) + grads = torch.autograd.grad( + loss, list(test_net.parameters()), create_graph=True, retain_graph=True + ) - for p, g, a in zip(test_net.parameters(), grads, alpha): - p.data -= a * g + for param, grad_, alphas in zip(test_net.parameters(), grads, alpha): + param.data -= alphas * grad_ - for p in test_net.parameters(): - if p.grad is not None: - p.grad.zero_() + for param in test_net.parameters(): + if param.grad is not None: + param.grad.zero_() test_net.eval() correct, total, loss = 0, 0, 0.0 @@ -454,5 +444,3 @@ def test_meta( loss = loss / total accuracy = correct / total return loss, accuracy - - diff --git a/baselines/fedmeta/fedmeta/server.py b/baselines/fedmeta/fedmeta/server.py index e69de29bb2d1..b24928de48b3 100644 --- a/baselines/fedmeta/fedmeta/server.py +++ b/baselines/fedmeta/fedmeta/server.py @@ -0,0 +1 @@ +"""Flower Server.""" diff --git a/baselines/fedmeta/fedmeta/strategy.py b/baselines/fedmeta/fedmeta/strategy.py index 22dc4df84dd6..a7bac9a15c67 100644 --- a/baselines/fedmeta/fedmeta/strategy.py +++ b/baselines/fedmeta/fedmeta/strategy.py @@ -3,47 +3,43 @@ Needed only when the strategy is not yet implemented in Flower or because you want to extend or modify the functionality of an existing strategy. """ -from typing import Dict, List, Optional, Tuple, Union -from logging import WARNING from collections import OrderedDict -import torch - - -from flwr.server.client_proxy import ClientProxy -from flwr.server.strategy import FedAvg -from flwr.server.strategy.aggregate import aggregate, weighted_loss_avg -from flwr.server.client_manager import ClientManager - -from fedmeta.models import CNN_network, StackedLSTM -from fedmeta.fedmeta_client_manager import evaluate_client_Criterion -from fedmeta.utils import update_ema - +from logging import WARNING +from typing import Dict, List, Optional, Tuple, Union -from flwr.common.logger import log +import torch from flwr.common import ( + EvaluateIns, + EvaluateRes, + FitIns, FitRes, + Metrics, + NDArrays, Parameters, Scalar, ndarrays_to_parameters, parameters_to_ndarrays, - EvaluateRes, - Metrics, - FitIns, - EvaluateIns, - NDArrays, ) +from flwr.common.logger import log +from flwr.server.client_manager import ClientManager +from flwr.server.client_proxy import ClientProxy +from flwr.server.strategy import FedAvg +from flwr.server.strategy.aggregate import aggregate, weighted_loss_avg + +from fedmeta.models import FemnistNetwork, StackedLSTM +from fedmeta.utils import update_ema +# pylint: disable=too-many-arguments def fedmeta_update_meta_sgd( - net : torch.nn.Module, - alpha : torch.nn.ParameterList, - beta : float, - weights_results : List[Tuple[NDArrays, int]], - gradients_aggregated : List[Tuple[NDArrays, int]], - WD : float, -) -> Tuple[List[Tuple[NDArrays, int]], torch.nn.ParameterList]: - """ - Update model parameters for FedMeta(Meta-SGD). + net: torch.nn.Module, + alpha: torch.nn.ParameterList, + beta: float, + weights_results: NDArrays, + gradients_aggregated: NDArrays, + weight_decay: float, +) -> Tuple[NDArrays, torch.nn.ParameterList]: + """Update model parameters for FedMeta(Meta-SGD). Parameters ---------- @@ -66,12 +62,13 @@ def fedmeta_update_meta_sgd( These are updated parameters. alpha : torch.nn.ParameterLis These are updated alpha. - """ params_dict = zip(net.state_dict().keys(), weights_results) state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) net.load_state_dict(state_dict, strict=True) - optimizer = torch.optim.Adam(list(net.parameters()) + list(alpha), lr=beta, weight_decay=WD) + optimizer = torch.optim.Adam( + list(net.parameters()) + list(alpha), lr=beta, weight_decay=weight_decay + ) for params, grad_ins, alphas in zip(net.parameters(), gradients_aggregated, alpha): params.grad = torch.tensor(grad_ins).to(params.dtype) alphas.grad = torch.tensor(grad_ins).to(params.dtype) @@ -83,14 +80,13 @@ def fedmeta_update_meta_sgd( def fedmeta_update_maml( - net : torch.nn.Module, - beta : float, - weights_results : List[Tuple[NDArrays, int]], - gradients_aggregated : List[Tuple[NDArrays, int]], - WD : float -) -> List[Tuple[NDArrays, int]]: - """ - Update model parameters for FedMeta(Meta-SGD). + net: torch.nn.Module, + beta: float, + weights_results: NDArrays, + gradients_aggregated: NDArrays, + weight_decay: float, +) -> NDArrays: + """Update model parameters for FedMeta(Meta-SGD). Parameters ---------- @@ -109,12 +105,11 @@ def fedmeta_update_maml( ------- weights_prime : List[Tuple[NDArrays, int]] These are updated parameters. - """ params_dict = zip(net.state_dict().keys(), weights_results) state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) net.load_state_dict(state_dict, strict=True) - optimizer = torch.optim.Adam(list(net.parameters()), lr=beta, weight_decay=WD) + optimizer = torch.optim.Adam(list(net.parameters()), lr=beta, weight_decay=weight_decay) for params, grad_ins in zip(net.parameters(), gradients_aggregated): params.grad = torch.tensor(grad_ins).to(params.dtype) optimizer.step() @@ -125,7 +120,7 @@ def fedmeta_update_maml( def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: - """Aggregation function for weighted average during evaluation. + """Aggregate using a weighted average during evaluation. Parameters ---------- @@ -138,18 +133,15 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics: The weighted average metric. """ # Multiply accuracy of each client by number of examples used - correct = [num_examples * m["correct"] for num_examples, m in metrics] + correct = [num_examples * float(m["correct"]) for num_examples, m in metrics] examples = [num_examples for num_examples, _ in metrics] # Aggregate and return custom metric (weighted average) - return {"accuracy": sum(correct) / sum(examples)} + return {"accuracy": float(sum(correct)) / float(sum(examples))} class FedMeta(FedAvg): - """ - FedMeta - The average of the gradient and the parameter update on the server through it. - """ + """FedMeta averages the gradient and server parameter update through it.""" def __init__(self, alpha, beta, data, algo, **kwargs): super().__init__(**kwargs) @@ -159,18 +151,23 @@ def __init__(self, alpha, beta, data, algo, **kwargs): self.ema_loss = None self.ema_acc = None - if self.data == 'femnist': - self.net = CNN_network() - elif self.data == 'shakespeare': + if self.data == "femnist": + self.net = FemnistNetwork() + elif self.data == "shakespeare": self.net = StackedLSTM() - self.alpha = torch.nn.ParameterList([torch.nn.Parameter(torch.full_like(p, alpha)) for p in self.net.parameters()]) + self.alpha = torch.nn.ParameterList( + [ + torch.nn.Parameter(torch.full_like(p, alpha)) + for p in self.net.parameters() + ] + ) def configure_fit( self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, FitIns]]: """Configure the next round of training.""" - config = {"alpha" : self.alpha, "algo": self.algo, "data": self.data} + config = {"alpha": self.alpha, "algo": self.algo, "data": self.data} if self.on_fit_config_fn is not None: # Custom fit config function provided config = self.on_fit_config_fn(server_round) @@ -180,10 +177,11 @@ def configure_fit( sample_size, min_num_clients = self.num_fit_clients( client_manager.num_available() ) - clients = client_manager.sample( + clients = client_manager.sample( # type: ignore num_clients=sample_size, min_num_clients=min_num_clients, - server_round=server_round + server_round=server_round, + step='evaluate' ) # Return client/config pairs @@ -198,7 +196,7 @@ def configure_evaluate( return [] # Parameters and config - config = {"alpha" : self.alpha, "algo": self.algo, "data":self.data} + config = {"alpha": self.alpha, "algo": self.algo, "data": self.data} if self.on_evaluate_config_fn is not None: # Custom evaluation config function provided config = self.on_evaluate_config_fn(server_round) @@ -208,11 +206,11 @@ def configure_evaluate( sample_size, min_num_clients = self.num_evaluation_clients( client_manager.num_available() ) - clients = client_manager.sample( + clients = client_manager.sample( # type: ignore num_clients=sample_size, - server_round=server_round, min_num_clients=min_num_clients, - criterion=evaluate_client_Criterion(self.min_evaluate_clients), + server_round=server_round, + step='evaluate' ) # Return client/config pairs @@ -232,37 +230,46 @@ def aggregate_fit( return None, {} # Convert results - weights_results = [ + weights_results: List[Tuple[NDArrays, int]] = [ (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples) for _, fit_res in results ] - parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results)) - if self.data == 'femnist': - WD = 0.001 + parameters_aggregated = aggregate(weights_results) + if self.data == "femnist": + weight_decay = 0.001 else: - WD = 0.0001 + weight_decay = 0.0001 # Gradient Average and Update Parameter for FedMeta(MAML) - if self.algo == 'fedmeta_maml': - grads_results = [ - (fit_res.metrics['grads'], fit_res.num_examples) + if self.algo == "fedmeta_maml": + grads_results: List[Tuple[NDArrays, int]] = [ + (fit_res.metrics["grads"], fit_res.num_examples) # type: ignore for _, fit_res in results ] gradients_aggregated = aggregate(grads_results) - weights_prime = fedmeta_update_maml(self.net, self.beta, weights_results[0][0], gradients_aggregated, WD) - parameters_aggregated = ndarrays_to_parameters(weights_prime) + weights_prime = fedmeta_update_maml( + self.net, self.beta, weights_results[0][0], gradients_aggregated, weight_decay + ) + parameters_aggregated = weights_prime # Gradient Average and Update Parameter for FedMeta(Meta-SGD) - elif self.algo == 'fedmeta_meta_sgd': + elif self.algo == "fedmeta_meta_sgd": grads_results = [ - (fit_res.metrics['grads'], fit_res.num_examples) + (fit_res.metrics["grads"], fit_res.num_examples) # type: ignore for _, fit_res in results ] gradients_aggregated = aggregate(grads_results) - weights_prime, update_alpha = fedmeta_update_meta_sgd(self.net, self.alpha, self.beta, weights_results[0][0], gradients_aggregated, WD) + weights_prime, update_alpha = fedmeta_update_meta_sgd( + self.net, + self.alpha, + self.beta, + weights_results[0][0], + gradients_aggregated, + weight_decay, + ) self.alpha = update_alpha - parameters_aggregated = ndarrays_to_parameters(weights_prime) + parameters_aggregated = weights_prime # Aggregate custom metrics if aggregation fn was provided metrics_aggregated = {} @@ -272,7 +279,7 @@ def aggregate_fit( elif server_round == 1: # Only log this warning once log(WARNING, "No fit_metrics_aggregation_fn provided") - return parameters_aggregated, metrics_aggregated + return ndarrays_to_parameters(parameters_aggregated), metrics_aggregated def aggregate_evaluate( self, @@ -295,8 +302,8 @@ def aggregate_evaluate( ] ) - if self.data == 'femnist': - smoothing_weight = 0.9 + if self.data == "femnist": + smoothing_weight = 0.95 else: smoothing_weight = 0.7 self.ema_loss = update_ema(self.ema_loss, loss_aggregated, smoothing_weight) @@ -307,8 +314,12 @@ def aggregate_evaluate( if self.evaluate_metrics_aggregation_fn: eval_metrics = [(res.num_examples, res.metrics) for _, res in results] metrics_aggregated = self.evaluate_metrics_aggregation_fn(eval_metrics) - self.ema_acc = update_ema(self.ema_acc, round(metrics_aggregated['accuracy'] * 100, 3), smoothing_weight) - metrics_aggregated['accuracy'] = self.ema_acc + self.ema_acc = update_ema( + self.ema_acc, + round(float(metrics_aggregated["accuracy"] * 100), 3), + smoothing_weight, + ) + metrics_aggregated["accuracy"] = self.ema_acc elif server_round == 1: # Only log this warning once log(WARNING, "No evaluate_metrics_aggregation_fn provided") diff --git a/baselines/fedmeta/fedmeta/utils.py b/baselines/fedmeta/fedmeta/utils.py index 0da04e826d95..b8e1dd95acab 100644 --- a/baselines/fedmeta/fedmeta/utils.py +++ b/baselines/fedmeta/fedmeta/utils.py @@ -5,67 +5,61 @@ results, plotting. """ -from typing import List, Dict -import pickle import os +import pickle +from typing import Dict, List + import matplotlib.pyplot as plt # Encoding list for the Shakespeare dataset -ALL_LETTERS = "\n !\"&'(),-.0123456789:;>?ABCDEFGHIJKLMNOPQRSTUVWXYZ[]abcdefghijklmnopqrstuvwxyz}" +ALL_LETTERS = ( + "\n !\"&'(),-.0123456789:;>?ABCDEFGHIJKLMNOPQRSTUVWXYZ[]abcdefghijklmnopqrstuvwxyz}" +) def _one_hot( - index: int, - size: int, + index: int, + size: int, ) -> List: - """ - returns one-hot vector with given size and value 1 at given index - - """ - + """Return one-hot vector with given size and value 1 at given index.""" vec = [0 for _ in range(size)] vec[int(index)] = 1 return vec def letter_to_vec( - letter: str, + letter: str, ) -> int: - """ - returns one-hot representation of given letter - - """ - + """Return one-hot representation of given letter.""" index = ALL_LETTERS.find(letter) return index def word_to_indices( - word: str, + word: str, ) -> List: - """ - returns a list of character indices - Args: - word: string + """Return a list of character indices. - Return: - indices: int list with length len(word) + Parameters + ---------- + word: string. + Returns + ------- + indices: int list with length len(word) """ - indices = [] - for c in word: - indices.append(ALL_LETTERS.find(c)) + for count in word: + indices.append(ALL_LETTERS.find(count)) return indices def update_ema( - prev_ema: float, - current_value: float, - smoothing_weight: float, + prev_ema: float, + current_value: float, + smoothing_weight: float, ) -> float: - """ - We use EMA to visually enhance the learning trend for each round. + """We use EMA to visually enhance the learning trend for each round. Parameters ---------- @@ -81,7 +75,6 @@ def update_ema( ------- EMA_Loss or EMA_ACC The weighted average metric. - """ if prev_ema is None: return current_value @@ -89,33 +82,30 @@ def update_ema( def save_graph_params(data_info: Dict): - """ - Save parameters to visualize experiment results (Loss, ACC) + """Save parameters to visualize experiment results (Loss, ACC). Parameters ---------- data_info : Dict This is a parameter dictionary of data from which the experiment was completed. """ - if os.path.exists(f"{data_info['path']}/{data_info['algo']}.pkl"): - raise ValueError(f"'{data_info['path']}/{data_info['algo']}.pkl' is already exists!") + raise ValueError( + f"'{data_info['path']}/{data_info['algo']}.pkl' is already exists!" + ) - with open(f"{data_info['path']}/{data_info['algo']}.pkl", 'wb') as f: - pickle.dump(data_info, f) + with open(f"{data_info['path']}/{data_info['algo']}.pkl", "wb") as file: + pickle.dump(data_info, file) def plot_from_pkl(directory="."): - """ - Visualization of algorithms for each data (FedAvg, FedAvg_Meta, FedMeta_MAML, FedMeta_Meta-SGD) + """Visualization of algorithms like 4 Algorithm for data. Parameters ---------- directory : str Graph params directory path for Femnist or Shakespeare - """ - color_mapping = { "fedavg.pkl": "#66CC00", "fedavg_meta.pkl": "#3333CC", @@ -123,13 +113,13 @@ def plot_from_pkl(directory="."): "fedmeta_meta_sgd.pkl": "#CC0000", } - pkl_files = [f for f in os.listdir(directory) if f.endswith('.pkl')] + pkl_files = [f for f in os.listdir(directory) if f.endswith(".pkl")] all_data = {} for file in pkl_files: - with open(os.path.join(directory, file), 'rb') as f: - data = pickle.load(f) + with open(os.path.join(directory, file), "rb") as file_: + data = pickle.load(file_) all_data[file] = data plt.figure(figsize=(7, 12)) @@ -138,9 +128,14 @@ def plot_from_pkl(directory="."): plt.subplot(2, 1, 1) for file in sorted(all_data.keys()): data = all_data[file] - accuracies = [acc for _, acc in data["accuracy"]['accuracy']] - legend_ = file[:-4] if file.endswith('.pkl') else file - plt.plot(accuracies, label=legend_, color=color_mapping.get(file, "black"), linewidth=3) + accuracies = [acc for _, acc in data["accuracy"]["accuracy"]] + legend_ = file[:-4] if file.endswith(".pkl") else file + plt.plot( + accuracies, + label=legend_, + color=color_mapping.get(file, "black"), + linewidth=3, + ) plt.title("Accuracy") plt.grid(True) plt.legend() @@ -149,8 +144,10 @@ def plot_from_pkl(directory="."): for file in sorted(all_data.keys()): data = all_data[file] loss = [loss for _, loss in data["loss"]] - legend_ = file[:-4] if file.endswith('.pkl') else file - plt.plot(loss, label=legend_, color=color_mapping.get(file, "black"), linewidth=3) + legend_ = file[:-4] if file.endswith(".pkl") else file + plt.plot( + loss, label=legend_, color=color_mapping.get(file, "black"), linewidth=3 + ) plt.title("Loss") plt.legend() plt.grid(True) From 0da42a9069d69e8faacb031e9cbc5718e38e9230 Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Tue, 10 Oct 2023 07:38:04 +0900 Subject: [PATCH 115/133] Update baselines/fedmeta/fedmeta/client.py Co-authored-by: Javier --- baselines/fedmeta/fedmeta/client.py | 1 - 1 file changed, 1 deletion(-) diff --git a/baselines/fedmeta/fedmeta/client.py b/baselines/fedmeta/fedmeta/client.py index bd77e04b6a72..e1485ae61719 100644 --- a/baselines/fedmeta/fedmeta/client.py +++ b/baselines/fedmeta/fedmeta/client.py @@ -165,7 +165,6 @@ def gen_client_fn( def client_fn(cid: str) -> FlowerClient: """Create a Flower client representing a single organization.""" - print(f"cid : {cid}") # Load model torch.manual_seed(42) From 2523ef1aa1921c04dfd46dc51569efe773f00851 Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Thu, 12 Oct 2023 11:57:55 +0900 Subject: [PATCH 116/133] Update baselines/fedmeta/pyproject.toml Co-authored-by: Javier --- baselines/fedmeta/pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml index 6a9f74a8a298..5defe4b5f98d 100644 --- a/baselines/fedmeta/pyproject.toml +++ b/baselines/fedmeta/pyproject.toml @@ -84,6 +84,9 @@ disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import good-names = "i,j,k,_,x,y,X,Y" signature-mutators="hydra.main.main" +[tool.pylint.typecheck] +generated-members="numpy.*, torch.*, tensorflow.*" + [[tool.mypy.overrides]] module = [ "importlib.metadata.*", From aa0030d567eb083e1dafcc2fb3cdbeaa66b89986 Mon Sep 17 00:00:00 2001 From: "JS.KIM" Date: Thu, 12 Oct 2023 12:07:04 +0900 Subject: [PATCH 117/133] Update baselines/fedmeta/README.md Co-authored-by: Javier --- baselines/fedmeta/README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/baselines/fedmeta/README.md b/baselines/fedmeta/README.md index e2076ec149b7..6f12e48385f0 100644 --- a/baselines/fedmeta/README.md +++ b/baselines/fedmeta/README.md @@ -67,7 +67,6 @@ pyenv local 3.10.6 poetry env use 3.10.6 # install the base Poetry environment -poetry add torch torchvision poetry shell ``` From 21887ba068c5966c26ceb9b2462eb034f23e0f1a Mon Sep 17 00:00:00 2001 From: Jinsoo Date: Thu, 12 Oct 2023 15:16:09 +0900 Subject: [PATCH 118/133] Update code --- baselines/fedmeta/fedmeta/client.py | 36 +++++----- .../femnist/graph_params/result_graph.png | Bin 151054 -> 0 bytes baselines/fedmeta/fedmeta/models.py | 13 ++-- .../shakespeare/graph_params/result_graph.png | Bin 123180 -> 0 bytes baselines/fedmeta/fedmeta/strategy.py | 66 ++++++++++-------- 5 files changed, 59 insertions(+), 56 deletions(-) delete mode 100644 baselines/fedmeta/fedmeta/femnist/graph_params/result_graph.png delete mode 100644 baselines/fedmeta/fedmeta/shakespeare/graph_params/result_graph.png diff --git a/baselines/fedmeta/fedmeta/client.py b/baselines/fedmeta/fedmeta/client.py index bd77e04b6a72..6f7fea5d8a1b 100644 --- a/baselines/fedmeta/fedmeta/client.py +++ b/baselines/fedmeta/fedmeta/client.py @@ -20,15 +20,15 @@ class FlowerClient(fl.client.NumPyClient): # pylint: disable=too-many-arguments def __init__( - self, - net: torch.nn.Module, - trainloaders: DataLoader, - valloaders: DataLoader, - cid: str, - device: torch.device, - num_epochs: int, - learning_rate: float, - gradient_step: int, + self, + net: torch.nn.Module, + trainloaders: DataLoader, + valloaders: DataLoader, + cid: str, + device: torch.device, + num_epochs: int, + learning_rate: float, + gradient_step: int, ): self.net = net self.trainloaders = trainloaders @@ -50,7 +50,7 @@ def set_parameters(self, parameters: NDArrays) -> None: self.net.load_state_dict(state_dict, strict=True) def fit( # type: ignore - self, parameters: NDArrays, config: Dict[str, Scalar] + self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[NDArrays, int, Dict]: """Implement distributed fit function for a given client.""" self.set_parameters(parameters) @@ -75,7 +75,7 @@ def fit( # type: ignore # FedMeta(MAML) & FedMeta(Meta-SGD) train inner and outer loop if algo in ('fedmeta_maml', 'fedmeta_meta_sgd'): alpha = config["alpha"] - loss, grads = train_meta( # type: ignore + loss, grads = train_meta( # type: ignore self.net, self.trainloaders["sup"][self.cid], self.trainloaders["qry"][self.cid], @@ -87,7 +87,7 @@ def fit( # type: ignore raise ValueError("Unsupported algorithm") def evaluate( # type: ignore - self, parameters: NDArrays, config: Dict[str, Scalar] + self, parameters: NDArrays, config: Dict[str, Scalar] ) -> Tuple[float, int, Dict]: """Implement distributed evaluation for a given client.""" self.set_parameters(parameters) @@ -128,12 +128,12 @@ def evaluate( # type: ignore # pylint: disable=too-many-arguments def gen_client_fn( - num_epochs: int, - trainloaders: List[DataLoader], - valloaders: List[DataLoader], - learning_rate: float, - model: DictConfig, - gradient_step: int, + num_epochs: int, + trainloaders: List[DataLoader], + valloaders: List[DataLoader], + learning_rate: float, + model: DictConfig, + gradient_step: int, ) -> Callable[[str], FlowerClient]: """Generate the client function that creates the Flower Clients. diff --git 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