hide ControllerApp/ExecutorApp

examples/hello-world/python_jobs/pt/client_api_pt_job2.py

@@ -0,0 +1,43 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  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.
+
+from code.net import Net
+
+from nvflare import FedJob2
+from nvflare.app_common.workflows.fedavg import FedAvg
+from nvflare.app_opt.pt.in_process_client_api_executor import PTInProcessClientAPIExecutor
+
+
+if __name__ == "__main__":
+    n_clients = 2
+    num_rounds = 2
+    train_script = "code/cifar10_fl.py"
+
+    job = FedJob2(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,
+    )
+    job.to(controller, "server")
+
+    for i in range(n_clients):
+        executor = PTInProcessClientAPIExecutor(
+            task_script_path=train_script,
+            task_script_args=""  # --batch_size 32 --data_path f'/tmp/data/site-{i}'
+        )
+        job.to(executor, f"site-{i}", gpu=0)
+
+    job.export_job("/tmp/nvflare/jobs/job_config")
+    job.simulator_run("/tmp/nvflare/jobs/workdir")

nvflare/__init__.py

@@ -20,3 +20,4 @@
 
 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

nvflare/fed_job2.py

@@ -0,0 +1,105 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  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.
+
+import os
+import uuid
+from typing import List, Any
+
+from nvflare.app_common.widgets.external_configurator import ExternalConfigurator
+from nvflare.app_common.widgets.intime_model_selector import IntimeModelSelector
+from nvflare.app_common.widgets.metric_relay import MetricRelay
+from nvflare.app_common.widgets.validation_json_generator import ValidationJsonGenerator
+from nvflare.app_opt.pt import PTFileModelPersistor
+from nvflare.app_opt.pt.file_model_locator import PTFileModelLocator
+from nvflare.app_opt.tracking.tb.tb_receiver import TBAnalyticsReceiver
+from nvflare.fuel.utils.constants import Mode
+from nvflare.fuel.utils.pipe.file_pipe import FilePipe
+from nvflare.job_config.fed_app_config import ClientAppConfig, FedAppConfig, ServerAppConfig
+from nvflare.job_config.fed_job_config import FedJobConfig
+from nvflare.apis.filter import Filter
+from .fed_job import FedApp, ControllerApp, ExecutorApp
+from nvflare.apis.impl.controller import Controller
+from nvflare.apis.executor import Executor
+
+
+class FedJob2:
+    def __init__(self, name="fed_job", min_clients=1, mandatory_clients=None, init_model=None, external_scripts: List = None) -> None:
+        self.job_name = name
+        self.gpus = []
+        self.clients = []
+        self.job: FedJobConfig = FedJobConfig(job_name=self.job_name, min_clients=min_clients, mandatory_clients=mandatory_clients)
+        self.init_model = init_model
+        self.external_scripts = external_scripts
+        self._deploy_map = {}
+
+    def to(self, obj: Any, target: str, tasks: List[str] = None, gpu: int = None):
+        if isinstance(obj, Controller):
+            if target not in self._deploy_map:
+                self._deploy_map[target] = ControllerApp(init_model=self.init_model)
+            self._deploy_map[target].add_controller(obj)
+        elif isinstance(obj, Executor):
+            if target not in self._deploy_map:
+                self._deploy_map[target] = ExecutorApp(external_scripts=self.external_scripts)
+                self.clients.append(target)
+                if gpu is not None:
+                    self.gpus.append(str(gpu))
+            self._deploy_map[target].add_executor(obj, tasks=tasks)
+        else:
+            if target not in self._deploy_map:
+                raise ValueError(f"{target} doesn't have a `Controller` or `Executor`. Deploy one first before adding components!")
+            self._deploy_map[target].add_component(obj)
+
+    def _deploy(self, app: FedApp, target: str):
+        if not isinstance(app, FedApp):
+            raise ValueError(f"App needs to be of type `FedApp` but was type {type(app)}")
+
+        client_server_config = app.get_app_config()
+        if isinstance(client_server_config, ClientAppConfig):
+            app_config = FedAppConfig(server_app=None, client_app=client_server_config)
+            app_name = f"app_client_{uuid.uuid4()}"
+        elif isinstance(client_server_config, ServerAppConfig):
+            app_config = FedAppConfig(server_app=client_server_config, client_app=None)
+            app_name = f"app_server_{uuid.uuid4()}"
+        else:
+            raise ValueError(
+                f"App needs to be of type `ClientAppConfig` or `ServerAppConfig` but was type {type(client_server_config)}"
+            )
+
+        self.job.add_fed_app(app_name, app_config)
+        self.job.set_site_app(target, app_name)
+
+    def _run_deploy(self):
+        for target in self._deploy_map:
+            self._deploy(self._deploy_map[target], target)
+
+    def export_job(self, job_root):
+        self._run_deploy()
+        self.job.generate_job_config(job_root)
+
+    def simulator_run(self, workspace, threads: int = None):
+        self._run_deploy()
+
+        n_clients = len(self.clients)
+        if threads is None:
+            threads = n_clients
+
+        job_root = os.path.join(workspace, "job")
+        self.job.simulator_run(
+            job_root,
+            workspace,
+            clients=",".join(self.clients),
+            n_clients=n_clients,
+            threads=threads,
+            gpu=",".join(self.gpus),
+        )