make FedJob2 default FedJob

holgerroth · Apr 18, 2024 · 7a81dc6 · 7a81dc6
1 parent bf7ae5b
commit 7a81dc6
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Showing 10 changed files with 73 additions and 315 deletions.
diff --git a/examples/hello-world/python_jobs/pt/client_api_pt.py b/examples/hello-world/python_jobs/pt/client_api_pt.py
@@ -14,7 +14,7 @@
 
 from code.net import Net
 
-from nvflare import FedJob, ControllerApp, ExecutorApp
+from nvflare import FedJob
 from nvflare.app_common.workflows.fedavg import FedAvg
 from nvflare.app_opt.pt.in_process_client_api_executor import PTInProcessClientAPIExecutor
 
@@ -24,24 +24,20 @@
     num_rounds = 2
     train_script = "code/cifar10_fl.py"
 
-    job = FedJob(name="cifar10_fedavg")
+    job = FedJob(name="cifar10_fedavg", init_model=Net(), external_scripts=[train_script])  # TODO: use load/save model in FedAvg
 
     controller = FedAvg(
         min_clients=n_clients,
         num_rounds=num_rounds,
     )
-    server_app = ControllerApp(init_model=Net())  # TODO: use load/save model in FedAvg
-    server_app.add_controller(controller)
-    job.to(server_app, "server")
+    job.to(controller, "server")
 
     for i in range(n_clients):
-        executor = PTInProcessClientAPIExecutor(
+        executor = PTInProcessClientAPIExecutor(  # TODO: ScriptExecutor()
             task_script_path=train_script,
             task_script_args=""  # f"--batch_size 32 --data_path /tmp/data/site-{i}"
         )
-        client_app = ExecutorApp(external_scripts=[train_script])
-        client_app.add_executor(executor)
-        job.to(client_app, f"site-{i}", gpu=0)
+        job.to(executor, f"site-{i}", gpu=0)
 
     job.export_job("/tmp/nvflare/jobs/job_config")
     job.simulator_run("/tmp/nvflare/jobs/workdir")
diff --git a/examples/hello-world/python_jobs/pt/client_api_pt2.py b/examples/hello-world/python_jobs/pt/client_api_pt2.py
diff --git a/examples/hello-world/python_jobs/pt/client_api_pt_dp_filter.py b/examples/hello-world/python_jobs/pt/client_api_pt_dp_filter.py
@@ -14,7 +14,7 @@
 
 from code.net import Net
 
-from nvflare import FedJob, ControllerApp, ExecutorApp
+from nvflare import FedJob, FilterType
 from nvflare.app_common.workflows.fedavg import FedAvg
 from nvflare.app_opt.pt.in_process_client_api_executor import PTInProcessClientAPIExecutor
 from nvflare.app_common.filters.percentile_privacy import PercentilePrivacy
@@ -25,28 +25,24 @@
     num_rounds = 2
     train_script = "code/cifar10_fl.py"
 
-    job = FedJob(name="cifar10_fedavg_privacy")
+    job = FedJob(name="cifar10_fedavg_privacy", init_model=Net(), external_scripts=[train_script])
 
     controller = FedAvg(
         min_clients=n_clients,
         num_rounds=num_rounds,
     )
-    server_app = ControllerApp(init_model=Net())
-    server_app.add_controller(controller)
-    job.to(server_app, "server")
+    job.to(controller, "server")
 
     for i in range(n_clients):
         executor = PTInProcessClientAPIExecutor(
             task_script_path=train_script,
             task_script_args=""
         )
-        client_app = ExecutorApp(external_scripts=[train_script])
-        client_app.add_executor(executor=executor, tasks=["train"])
+        job.to(executor, f"site-{i}", tasks=["train"], gpu=0)
 
-        # add privacy filter.  # TODO: is there a better way to handle task names?
-        client_app.add_task_result_filter(["train"], PercentilePrivacy(percentile=10, gamma=0.01))
-
-        job.to(client_app, f"site-{i}", gpu=0)
+        # add privacy filter.  # TODO: is there a better way to handle task names and filter type?
+        pp_filter = PercentilePrivacy(percentile=10, gamma=0.01)
+        job.to(pp_filter, f"site-{i}", tasks=["train"], filter_type=FilterType.TASK_RESULT)
 
     job.export_job("/tmp/nvflare/jobs/job_config")
     job.simulator_run("/tmp/nvflare/jobs/workdir")
diff --git a/examples/hello-world/python_jobs/pt/client_api_pt_dp_filter2.py b/examples/hello-world/python_jobs/pt/client_api_pt_dp_filter2.py
diff --git a/examples/hello-world/python_jobs/pt/client_api_pt_swarm.py b/examples/hello-world/python_jobs/pt/client_api_pt_swarm.py
@@ -14,7 +14,7 @@
 
 from code.net import Net
 
-from nvflare import FedJob2
+from nvflare import FedJob
 from nvflare.apis.dxo import DataKind
 from nvflare.app_opt.pt.in_process_client_api_executor import PTInProcessClientAPIExecutor
 from nvflare.app_common.ccwf import SwarmServerController, CrossSiteEvalServerController, SwarmClientController, CrossSiteEvalClientController
@@ -32,7 +32,7 @@
     num_rounds = 3
     train_script = "code/cifar10_fl.py"
 
-    job = FedJob2(name="cifar10_swarm", init_model=Net(), external_scripts=[train_script])  # TODO: use load/save model in FedAvg
+    job = FedJob(name="cifar10_swarm", init_model=Net(), external_scripts=[train_script])  # TODO: use load/save model in FedAvg
 
     controller = SwarmServerController(
         num_rounds=num_rounds,

diff --git a/examples/hello-world/python_jobs/pt/model_learner_xsite_val.py b/examples/hello-world/python_jobs/pt/model_learner_xsite_val.py
@@ -21,19 +21,20 @@
 from pt.utils.cifar10_data_splitter import Cifar10DataSplitter
 from pt.utils.cifar10_data_utils import load_cifar10_data
 
-from nvflare import FedJob, ControllerApp, ExecutorApp
+from nvflare import FedJob
 from nvflare.app_common.workflows.fedavg import FedAvg
 from nvflare.app_common.workflows.cross_site_model_eval import CrossSiteModelEval
 from nvflare.app_common.executors.model_learner_executor import ModelLearnerExecutor
 
+
 if __name__ == "__main__":
     n_clients = 2
     num_rounds = 2
     aggregation_epochs = 4
     alpha = 0.1
     train_split_root = f"/tmp/cifar10_splits/clients{n_clients}_alpha{alpha}"  # avoid overwriting results
 
-    job = FedJob(name="cifar10_fedavg")
+    job = FedJob(name="cifar10_fedavg", init_model=ModerateCNN())
 
     ctrl1 = FedAvg(
         min_clients=n_clients,
@@ -48,11 +49,9 @@
         alpha=alpha,
     )
 
-    server_app = ControllerApp(init_model=ModerateCNN())
-    server_app.add_controller(ctrl1)
-    server_app.add_controller(ctrl2)
-    server_app.add_component(data_splitter)
-    job.to(server_app, "server")
+    job.to(ctrl1, "server")
+    job.to(ctrl2, "server")
+    job.to(data_splitter, "server")
 
     for i in range(n_clients):
         learner = CIFAR10ModelLearner(
@@ -63,10 +62,7 @@
         executor = ModelLearnerExecutor(
             learner_id=learner  # TODO: change more places that use id to directly accept objects
         )
-        client_app = ExecutorApp()
-        client_app.add_executor(executor=executor, tasks=["train", "submit_model", "validate"])
-
-        job.to(client_app, f"site-{i+1}", gpu=0)  # data splitter assumes client names start from 1
+        job.to(executor, f"site-{i+1}", gpu=0)  # data splitter assumes client names start from 1
 
     job.export_job("/tmp/nvflare/jobs/job_config")
     job.simulator_run("/tmp/nvflare/jobs/workdir")
diff --git a/examples/hello-world/python_jobs/pt/model_learner_xsite_val2.py b/examples/hello-world/python_jobs/pt/model_learner_xsite_val2.py
diff --git a/nvflare/__init__.py b/nvflare/__init__.py
@@ -19,5 +19,4 @@
 # https://github.com/microsoft/pylance-release/issues/856
 
 from nvflare.private.fed.app.simulator.simulator_runner import SimulatorRunner as SimulatorRunner
-from nvflare.fed_job import FedJob, ControllerApp, ExecutorApp
-from nvflare.fed_job2 import FedJob2, FilterType
+from nvflare.fed_job import FedJob