chore: add cancel cp option to camelyon benchmark

benchmark/camelyon/benchmarks.py

@@ -63,6 +63,7 @@ def fed_avg(params: dict, train_folder: Path, test_folder: Path):
         model=model,
         mode=exp_params["mode"],
         cp_name=exp_params["cp_name"],
+        cancel_cp=exp_params["cancel_cp"],
         torch_gpu=exp_params["torch_gpu"],
     )
 

benchmark/camelyon/common/utils.py

@@ -75,6 +75,7 @@ def parse_params() -> dict:
         default=Path(__file__).resolve().parents[1] / "data",
         help="Path to the data",
     )
+    parser.add_argument("--cancel-cp", type=bool, default=False, help="Remote only: cancel the CP after registration")
     parser.add_argument("--torch-gpu", action="store_true", help="Use PyTorch with GPU/CUDA support")
     parser.add_argument(
         "--cp-name",
@@ -95,6 +96,7 @@ def parse_params() -> dict:
     params["nb_train_data_samples"] = args.nb_train_data_samples
     params["nb_test_data_samples"] = args.nb_test_data_samples
     params["data_path"] = args.data_path
+    params["cancel_cp"] = args.cancel_cp
     params["torch_gpu"] = args.torch_gpu
     params["cp_name"] = args.cp_name
 

benchmark/camelyon/workflows.py

@@ -4,6 +4,7 @@
 from typing import Optional
 
 import numpy as np
+import substra
 import torch
 import weldon_fedavg
 from common import benchmark_metrics
@@ -42,6 +43,7 @@ def substrafl_fed_avg(
     credentials_path: Path,
     asset_keys_path: Path,
     cp_name: Optional[str],
+    cancel_cp: bool = False,
     torch_gpu: bool = False,
 ) -> benchmark_metrics.BenchmarkResults:
     """Execute Weldon algorithm for a fed avg strategy with substrafl API.
@@ -65,6 +67,7 @@ def substrafl_fed_avg(
         asset_keys_path (Path): Remote only: path to asset key file. If un existent, it will be created.
             Otherwise, all present keys in this fill will be reused per Substra in remote mode.
         cp_name ben): (Optional[str]): Compute Plan name to display
+        cancel_cp (bool): if set to True, the CP will be canceled as soon as it's registered. Only work for remote mode.
         torch_gpu (bool): Use GPU default index for pytorch
     Returns:
         dict: Results of the experiment.
@@ -140,6 +143,9 @@ def accuracy(data_from_opener, predictions):
         name=cp_name,
     )
 
+    if cancel_cp and clients[0].backend_mode == substra.BackendType.REMOTE:
+        clients[0].cancel_compute_plan(key=compute_plan.key)
+
     clients[0].wait_compute_plan(key=compute_plan.key, raise_on_failure=True)
 
     performances = clients[1].get_performances(key=compute_plan.key)