Add API for Pythonic Job Creation

examples/hello-world/job_api/pt/client_api_kmeans.py

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+# 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 csv
+import os
+
+from src.kmeans_assembler import KMeansAssembler
+from src.split_csv import distribute_header_file, split_csv
+
+from nvflare import FedJob, ScriptExecutor
+from nvflare.app_common.aggregators.collect_and_assemble_aggregator import CollectAndAssembleAggregator
+from nvflare.app_common.shareablegenerators.full_model_shareable_generator import FullModelShareableGenerator
+from nvflare.app_common.workflows.scatter_and_gather import ScatterAndGather
+from nvflare.app_opt.sklearn.joblib_model_param_persistor import JoblibModelParamPersistor
+from nvflare.client.config import ExchangeFormat
+
+
+def split_higgs(input_data_path, input_header_path, output_dir, site_num, sample_rate, site_name_prefix="site-"):
+    input_file = input_data_path
+    output_directory = output_dir
+    num_parts = site_num
+    site_name_prefix = site_name_prefix
+    sample_rate = sample_rate
+    split_csv(input_file, output_directory, num_parts, site_name_prefix, sample_rate)
+    distribute_header_file(input_header_path, output_directory, num_parts, site_name_prefix)
+
+
+if __name__ == "__main__":
+    n_clients = 3
+    num_rounds = 2
+    train_script = "src/kmeans_fl.py"
+    data_input_dir = "/tmp/nvflare/higgs/data"
+    data_output_dir = "/tmp/nvflare/higgs/split_data"
+
+    # Download data
+    os.makedirs(data_input_dir, exist_ok=True)
+    higgs_zip_file = os.path.join(data_input_dir, "higgs.zip")
+    if not os.path.exists(higgs_zip_file):
+        os.system(
+            f"curl -o {higgs_zip_file} https://archive.ics.uci.edu/static/public/280/higgs.zip"
+        )  # This might take a while. The file is 2.8 GB.
+        os.system(f"unzip -d {data_input_dir} {higgs_zip_file}")
+        os.system(
+            f"gunzip -c {os.path.join(data_input_dir, 'HIGGS.csv.gz')} > {os.path.join(data_input_dir, 'higgs.csv')}"
+        )
+
+    # Generate the csv header file
+    # Your list of data
+    features = [
+        "label",
+        "lepton_pt",
+        "lepton_eta",
+        "lepton_phi",
+        "missing_energy_magnitude",
+        "missing_energy_phi",
+        "jet_1_pt",
+        "jet_1_eta",
+        "jet_1_phi",
+        "jet_1_b_tag",
+        "jet_2_pt",
+        "jet_2_eta",
+        "jet_2_phi",
+        "jet_2_b_tag",
+        "jet_3_pt",
+        "jet_3_eta",
+        "jet_3_phi",
+        "jet_3_b_tag",
+        "jet_4_pt",
+        "jet_4_eta",
+        "jet_4_phi",
+        "jet_4_b_tag",
+        "m_jj",
+        "m_jjj",
+        "m_lv",
+        "m_jlv",
+        "m_bb",
+        "m_wbb",
+        "m_wwbb",
+    ]
+
+    # Specify the file path
+    file_path = os.path.join(data_input_dir, "headers.csv")
+
+    with open(file_path, "w", newline="") as file:
+        csv_writer = csv.writer(file)
+        csv_writer.writerow(features)
+
+    print(f"features written to {file_path}")
+
+    # Split the data
+    split_higgs(
+        input_data_path=os.path.join(data_input_dir, "higgs.csv"),
+        input_header_path=os.path.join(data_input_dir, "headers.csv"),
+        output_dir=data_output_dir,
+        site_num=n_clients,
+        sample_rate=0.3,
+    )
+
+    # Create the federated learning job
+    job = FedJob(name="kmeans")
+
+    controller = ScatterAndGather(
+        min_clients=n_clients,
+        num_rounds=num_rounds,
+        wait_time_after_min_received=0,
+        aggregator_id="aggregator",
+        persistor_id="persistor",  # TODO: Allow adding python objects rather than ids
+        shareable_generator_id="shareable_generator",
+        train_task_name="train",  # Client will start training once received such task.
+        train_timeout=0,
+    )
+    job.to(controller, "server")
+
+    # For kmeans with sklean, we need a custom persistor
+    # JoblibModelParamPersistor is a persistor which save/read the model to/from file with JobLib format.
+    persistor = JoblibModelParamPersistor(initial_params={"n_clusters": 2})
+    # When assigning the persistor to the server, we need to specify the id that's expected
+    # by the ScatterAndGather controller.
+    job.to(persistor, "server")
+
+    # Similarly, ScatterAndGather expects a "shareable_generator" which we need to assign to the server.
+    job.to(FullModelShareableGenerator(), "server")
+
+    # ScatterAndGather also expects an "aggregator" which we define here.
+    # The actual aggregation function is defined by an "assembler" to specify how to handle the collected updates.
+    aggregator = CollectAndAssembleAggregator(
+        assembler_id="kmeans_assembler"
+    )  # TODO: Allow adding KMeansAssembler() directly
+    job.to(aggregator, "server")
+
+    # This is the assembler designed for k-Means algorithm.
+    # As CollectAndAssembleAggregator expects an assembler_id, we need to specify it here.
+    job.to(KMeansAssembler(), "server", id="kmeans_assembler")
+
+    # Add clients
+    for i in range(n_clients):
+        executor = ScriptExecutor(
+            task_script_path=train_script,
+            task_script_args=f"--data_root_dir {data_output_dir}",
+            params_exchange_format=ExchangeFormat.RAW,  # kmeans requires raw values only rather than PyTorch Tensors (the default)
+        )
+        job.to(executor, f"site-{i+1}", gpu=0)  # HIGGs data splitter assumes site names start from 1
+
+    # job.export_job("/tmp/nvflare/jobs/job_config")
+    job.simulator_run("/tmp/nvflare/jobs/workdir")

examples/hello-world/job_api/pt/client_api_lightning.py

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+# 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 src.lit_net import LitNet
+
+from nvflare import FedAvg, FedJob, ScriptExecutor
+
+if __name__ == "__main__":
+    n_clients = 2
+    num_rounds = 2
+    train_script = "src/cifar10_lightning_fl.py"
+
+    job = FedJob(name="cifar10_fedavg_lightning")
+
+    # Define the controller workflow and send to server
+    controller = FedAvg(
+        min_clients=n_clients,
+        num_rounds=num_rounds,
+    )
+    job.to(controller, "server")
+
+    # Define the initial global model and send to server
+    job.to(LitNet(), "server")
+
+    # Add clients
+    for i in range(n_clients):
+        executor = ScriptExecutor(
+            task_script_path=train_script, task_script_args=""  # f"--batch_size 32 --data_path /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")

examples/hello-world/job_api/pt/client_api_pt.py

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+# 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 src.net import Net
+
+from nvflare import FedAvg, FedJob, ScriptExecutor
+
+if __name__ == "__main__":
+    n_clients = 2
+    num_rounds = 2
+    train_script = "src/cifar10_fl.py"
+
+    job = FedJob(name="cifar10_fedavg")
+
+    # Define the controller workflow and send to server
+    controller = FedAvg(
+        min_clients=n_clients,
+        num_rounds=num_rounds,
+    )
+    job.to(controller, "server")
+
+    # Define the initial global model and send to server
+    job.to(Net(), "server")
+
+    # Add clients
+    for i in range(n_clients):
+        executor = ScriptExecutor(
+            task_script_path=train_script, task_script_args=""  # f"--batch_size 32 --data_path /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")

examples/hello-world/job_api/pt/client_api_pt_cyclic_cc.py

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+# 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 src.net import Net
+
+from nvflare import FedJob, ScriptExecutor
+from nvflare.app_common.ccwf import CyclicClientController, CyclicServerController
+from nvflare.app_common.ccwf.comps.simple_model_shareable_generator import SimpleModelShareableGenerator
+from nvflare.app_opt.pt.file_model_persistor import PTFileModelPersistor
+
+if __name__ == "__main__":
+    n_clients = 2
+    num_rounds = 3
+    train_script = "src/cifar10_fl.py"
+
+    job = FedJob(name="cifar10_cyclic")
+
+    controller = CyclicServerController(num_rounds=num_rounds, max_status_report_interval=300)
+    job.to(controller, "server")
+
+    for i in range(n_clients):
+        executor = ScriptExecutor(
+            task_script_path=train_script, task_script_args=""  # f"--batch_size 32 --data_path /tmp/data/site-{i}"
+        )
+        job.to(executor, f"site-{i}", gpu=0)
+
+        # Add client-side controller for cyclic workflow
+        executor = CyclicClientController()
+        job.to(executor, f"site-{i}", tasks=["cyclic_*"])
+
+        # In swarm learning, each client uses a model persistor and shareable_generator
+        job.to(PTFileModelPersistor(model=Net()), f"site-{i}", id="persistor")
+        job.to(SimpleModelShareableGenerator(), f"site-{i}", id="shareable_generator")
+
+    # job.export_job("/tmp/nvflare/jobs/job_config")
+    job.simulator_run("/tmp/nvflare/jobs/workdir")

examples/hello-world/job_api/pt/client_api_pt_dp_filter.py

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+# 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 src.net import Net
+
+from nvflare import FedAvg, FedJob, FilterType, ScriptExecutor
+from nvflare.app_common.filters.percentile_privacy import PercentilePrivacy
+
+if __name__ == "__main__":
+    n_clients = 2
+    num_rounds = 2
+    train_script = "src/cifar10_fl.py"
+
+    job = FedJob(name="cifar10_fedavg_privacy")
+
+    # Define the controller workflow and send to server
+    controller = FedAvg(
+        min_clients=n_clients,
+        num_rounds=num_rounds,
+    )
+    job.to(controller, "server")
+
+    # Define the initial global model and send to server
+    job.to(Net(), "server")
+
+    for i in range(n_clients):
+        executor = ScriptExecutor(task_script_path=train_script, task_script_args="")
+        job.to(executor, f"site-{i}", tasks=["train"], gpu=0)
+
+        # add privacy filter.
+        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")