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Move plugins to new
dask_cuda.plugins module
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,122 @@ | ||
| import importlib | ||
| import os | ||
|
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| from distributed import WorkerPlugin | ||
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| from .utils import get_rmm_log_file_name, parse_device_memory_limit | ||
|
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| class CPUAffinity(WorkerPlugin): | ||
| def __init__(self, cores): | ||
| self.cores = cores | ||
|
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| def setup(self, worker=None): | ||
| os.sched_setaffinity(0, self.cores) | ||
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| class RMMSetup(WorkerPlugin): | ||
| def __init__( | ||
| self, | ||
| initial_pool_size, | ||
| maximum_pool_size, | ||
| managed_memory, | ||
| async_alloc, | ||
| release_threshold, | ||
| log_directory, | ||
| track_allocations, | ||
| ): | ||
| if initial_pool_size is None and maximum_pool_size is not None: | ||
| raise ValueError( | ||
| "`rmm_maximum_pool_size` was specified without specifying " | ||
| "`rmm_pool_size`.`rmm_pool_size` must be specified to use RMM pool." | ||
| ) | ||
| if async_alloc is True: | ||
| if managed_memory is True: | ||
| raise ValueError( | ||
| "`rmm_managed_memory` is incompatible with the `rmm_async`." | ||
| ) | ||
| if async_alloc is False and release_threshold is not None: | ||
| raise ValueError("`rmm_release_threshold` requires `rmm_async`.") | ||
|
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| self.initial_pool_size = initial_pool_size | ||
| self.maximum_pool_size = maximum_pool_size | ||
| self.managed_memory = managed_memory | ||
| self.async_alloc = async_alloc | ||
| self.release_threshold = release_threshold | ||
| self.logging = log_directory is not None | ||
| self.log_directory = log_directory | ||
| self.rmm_track_allocations = track_allocations | ||
|
|
||
| def setup(self, worker=None): | ||
| if self.initial_pool_size is not None: | ||
| self.initial_pool_size = parse_device_memory_limit( | ||
| self.initial_pool_size, alignment_size=256 | ||
| ) | ||
|
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| if self.async_alloc: | ||
| import rmm | ||
|
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| if self.release_threshold is not None: | ||
| self.release_threshold = parse_device_memory_limit( | ||
| self.release_threshold, alignment_size=256 | ||
| ) | ||
|
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| mr = rmm.mr.CudaAsyncMemoryResource( | ||
| initial_pool_size=self.initial_pool_size, | ||
| release_threshold=self.release_threshold, | ||
| ) | ||
|
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||
| if self.maximum_pool_size is not None: | ||
| self.maximum_pool_size = parse_device_memory_limit( | ||
| self.maximum_pool_size, alignment_size=256 | ||
| ) | ||
| mr = rmm.mr.LimitingResourceAdaptor( | ||
| mr, allocation_limit=self.maximum_pool_size | ||
| ) | ||
|
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| rmm.mr.set_current_device_resource(mr) | ||
| if self.logging: | ||
| rmm.enable_logging( | ||
| log_file_name=get_rmm_log_file_name( | ||
| worker, self.logging, self.log_directory | ||
| ) | ||
| ) | ||
| elif self.initial_pool_size is not None or self.managed_memory: | ||
| import rmm | ||
|
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| pool_allocator = False if self.initial_pool_size is None else True | ||
|
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| if self.initial_pool_size is not None: | ||
| if self.maximum_pool_size is not None: | ||
| self.maximum_pool_size = parse_device_memory_limit( | ||
| self.maximum_pool_size, alignment_size=256 | ||
| ) | ||
|
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||
| rmm.reinitialize( | ||
| pool_allocator=pool_allocator, | ||
| managed_memory=self.managed_memory, | ||
| initial_pool_size=self.initial_pool_size, | ||
| maximum_pool_size=self.maximum_pool_size, | ||
| logging=self.logging, | ||
| log_file_name=get_rmm_log_file_name( | ||
| worker, self.logging, self.log_directory | ||
| ), | ||
| ) | ||
| if self.rmm_track_allocations: | ||
| import rmm | ||
|
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| mr = rmm.mr.get_current_device_resource() | ||
| rmm.mr.set_current_device_resource(rmm.mr.TrackingResourceAdaptor(mr)) | ||
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| class PreImport(WorkerPlugin): | ||
| def __init__(self, libraries): | ||
| if libraries is None: | ||
| libraries = [] | ||
| elif isinstance(libraries, str): | ||
| libraries = libraries.split(",") | ||
| self.libraries = libraries | ||
|
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||
| def setup(self, worker=None): | ||
| for l in self.libraries: | ||
| importlib.import_module(l) |
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