Add content for logistic regression for DLI

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.3_convert_machine_learning_to_federated_learning/02.3.1_convert_logistic_regression_to_federated_learning/code/data/prepare_heart_disease_data.sh

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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.
+
+
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
+DATA_DIR=/tmp/flare/dataset/heart_disease_data
+
+# Install dependencies
+#pip install wget
+FLAMBY_INSTALL_DIR=$(python3 -c "import sysconfig; print(sysconfig.get_path('purelib'))")
+# git clone https://github.com/owkin/FLamby.git && cd FLamby && pip install -e .
+
+# Download data using FLamby
+mkdir -p ${DATA_DIR}
+python3 ${FLAMBY_INSTALL_DIR}/flamby/datasets/fed_heart_disease/dataset_creation_scripts/download.py --output-folder ${DATA_DIR}
+
+# Convert data to numpy files
+python3 ${SCRIPT_DIR}/utils/convert_data_to_np.py ${DATA_DIR}

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.3_convert_machine_learning_to_federated_learning/02.3.1_convert_logistic_regression_to_federated_learning/code/data/utils/convert_data_to_np.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 argparse
+import os
+
+import numpy as np
+from flamby.datasets.fed_heart_disease import FedHeartDisease
+from torch.utils.data import DataLoader as dl
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser("save UCI Heart Disease as numpy arrays.")
+    parser.add_argument("save_dir", type=str, help="directory to save converted numpy arrays as .npy files.")
+    args = parser.parse_args()
+
+    if not os.path.exists(args.save_dir):
+        os.makedirs(args.save_dir, exist_ok=True)
+
+    for site in range(4):
+
+        for flag in ("train", "test"):
+
+            # To load data a pytorch dataset
+            data = FedHeartDisease(center=site, train=(flag == "train"))
+
+            # Save training dataset
+            data_x = []
+            data_y = []
+            for x, y in dl(data, batch_size=1, shuffle=False, num_workers=0):
+                data_x.append(x.cpu().numpy().reshape(-1))
+                data_y.append(y.cpu().numpy().reshape(-1))
+
+            data_x = np.array(data_x).reshape(-1, 13)
+            data_y = np.array(data_y).reshape(-1, 1)
+
+            print("site {} - {} - variables shape: {}".format(site, flag, data_x.shape))
+            print("site {} - {} - outcomes shape: {}".format(site, flag, data_y.shape))
+
+            save_x_path = "{}/site-{}.{}.x.npy".format(args.save_dir, site + 1, flag)
+            print("saving data: {}".format(save_x_path))
+            np.save(save_x_path, data_x)
+
+            save_y_path = "{}/site-{}.{}.y.npy".format(args.save_dir, site + 1, flag)
+            print("saving data: {}".format(save_y_path))
+            np.save(save_y_path, data_y)

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.3_convert_machine_learning_to_federated_learning/02.3.1_convert_logistic_regression_to_federated_learning/code/log_config.json

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+{
+    "version": 1,
+    "disable_existing_loggers": false,
+    "formatters": {
+        "baseFormatter": {
+            "()": "nvflare.fuel.utils.log_utils.BaseFormatter",
+            "fmt": "%(asctime)s - %(name)s - %(levelname)s - %(fl_ctx)s - %(message)s"
+        },
+        "colorFormatter": {
+            "()": "nvflare.fuel.utils.log_utils.ColorFormatter",
+            "fmt": "%(asctime)s - %(levelname)s - %(message)s",
+            "datefmt": "%Y-%m-%d %H:%M:%S"
+        },
+        "jsonFormatter": {
+            "()": "nvflare.fuel.utils.log_utils.JsonFormatter",
+            "fmt": "%(asctime)s - %(identity)s - %(name)s - %(fullName)s - %(levelname)s - %(fl_ctx)s - %(message)s"
+        }
+    },
+    "filters": {
+        "FLFilter": {
+            "()": "nvflare.fuel.utils.log_utils.LoggerNameFilter",
+            "logger_names": ["custom", "nvflare.app_common", "nvflare.app_opt"]
+        }
+    },
+    "handlers": {
+        "consoleHandler": {
+            "class": "logging.StreamHandler",
+            "level": "INFO",
+            "formatter": "colorFormatter",
+            "filters": ["FLFilter"],
+            "stream": "ext://sys.stdout"
+        },
+        "logFileHandler": {
+            "class": "logging.handlers.RotatingFileHandler",
+            "level": "DEBUG",
+            "formatter": "baseFormatter",
+            "filename": "log.txt",
+            "mode": "a",
+            "maxBytes": 20971520,
+            "backupCount": 10
+        },
+        "errorFileHandler": {
+            "class": "logging.handlers.RotatingFileHandler",
+            "level": "ERROR",
+            "formatter": "baseFormatter",
+            "filename": "log_error.txt",
+            "mode": "a",
+            "maxBytes": 20971520,
+            "backupCount": 10
+        },
+        "jsonFileHandler": {
+            "class": "logging.handlers.RotatingFileHandler",
+            "level": "DEBUG",
+            "formatter": "jsonFormatter",
+            "filename": "log.json",
+            "mode": "a",
+            "maxBytes": 20971520,
+            "backupCount": 10
+        },
+        "FLFileHandler": {
+            "class": "logging.handlers.RotatingFileHandler",
+            "level": "DEBUG",
+            "formatter": "baseFormatter",
+            "filters": ["FLFilter"],
+            "filename": "log_fl.txt",
+            "mode": "a",
+            "maxBytes": 20971520,
+            "backupCount": 10,
+            "delay": true
+        }
+    },
+    "loggers": {
+        "root": {
+            "level": "INFO",
+            "handlers": ["consoleHandler", "logFileHandler", "errorFileHandler", "jsonFileHandler", "FLFileHandler"]
+        }
+    }
+}
+
+
+
+
+
+
+
+
+

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.3_convert_machine_learning_to_federated_learning/02.3.1_convert_logistic_regression_to_federated_learning/code/lr_fl_job.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.newton_raphson_persistor import NewtonRaphsonModelPersistor
+from src.newton_raphson_workflow import FedAvgNewtonRaphson
+
+from nvflare.app_opt.pt.job_config.base_fed_job import BaseFedJob
+from nvflare.client.config import ExchangeFormat
+from nvflare.job_config.script_runner import ScriptRunner
+
+if __name__ == "__main__":
+    n_clients = 4
+    num_rounds = 5
+
+    job = BaseFedJob(
+        name="logistic_regression_fedavg",
+        model_persistor=NewtonRaphsonModelPersistor(n_features=13),
+    )
+
+    controller = FedAvgNewtonRaphson(
+        num_clients=n_clients,
+        num_rounds=num_rounds,
+        damping_factor=0.8,
+        persistor_id="newton_raphson_persistor",
+    )
+    job.to(controller, "server")
+
+    # Add clients
+    for i in range(n_clients):
+        runner = ScriptRunner(
+            script="src/newton_raphson_train.py",
+            script_args="--data_root /tmp/flare/dataset/heart_disease_data",
+            launch_external_process=True,
+            params_exchange_format=ExchangeFormat.RAW,
+        )
+        job.to(runner, f"site-{i + 1}")
+
+    job.export_job("/tmp/nvflare/jobs/job_config")
+    job.simulator_run("/tmp/nvflare/jobs/workdir", gpu="0", log_config="./log_config.json")

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.3_convert_machine_learning_to_federated_learning/02.3.1_convert_logistic_regression_to_federated_learning/code/newton_raphson/app/config/config_fed_client.json

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+{
+  "format_version": 2,
+  "app_script": "newton_raphson_train.py",
+  "app_config": "--data_root /tmp/flare/dataset/heart_disease_data",
+  "executors": [
+    {
+      "tasks": [
+        "train"
+      ],
+      "executor": {
+        "path": "nvflare.app_common.executors.client_api_launcher_executor.ClientAPILauncherExecutor",
+        "args": {
+          "launcher_id": "launcher",
+          "pipe_id": "pipe",
+          "heartbeat_timeout": 60,
+          "params_exchange_format": "raw",
+          "params_transfer_type": "FULL",
+          "train_with_evaluation": false
+        }
+      }
+    }
+  ],
+  "task_data_filters": [],
+  "task_result_filters": [],
+  "components": [
+    {
+      "id": "launcher",
+      "path": "nvflare.app_common.launchers.subprocess_launcher.SubprocessLauncher",
+      "args":  {
+        "script": "python3 custom/{app_script} {app_config}",
+        "launch_once": true
+      }
+    },
+    {
+      "id": "pipe",
+      "path": "nvflare.fuel.utils.pipe.cell_pipe.CellPipe",
+      "args": {
+        "mode": "PASSIVE",
+        "site_name": "{SITE_NAME}",
+        "token": "{JOB_ID}",
+        "root_url": "{ROOT_URL}",
+        "secure_mode": "{SECURE_MODE}",
+        "workspace_dir": "{WORKSPACE}"
+      }
+    },
+    {
+      "id": "metrics_pipe",
+      "path": "nvflare.fuel.utils.pipe.cell_pipe.CellPipe",
+      "args": {
+        "mode": "PASSIVE",
+        "site_name": "{SITE_NAME}",
+        "token": "{JOB_ID}",
+        "root_url": "{ROOT_URL}",
+        "secure_mode": "{SECURE_MODE}",
+        "workspace_dir": "{WORKSPACE}"
+      }
+    },
+    {
+      "id": "metric_relay",
+      "path": "nvflare.app_common.widgets.metric_relay.MetricRelay",
+      "args": {
+        "pipe_id": "metrics_pipe",
+        "event_type": "fed.analytix_log_stats",
+        "read_interval": 0.1
+      }
+    },
+    {
+      "id": "client_api_config_preparer",
+      "path": "nvflare.app_common.widgets.external_configurator.ExternalConfigurator",
+      "args": {
+        "component_ids": ["metric_relay"]
+      }
+    }
+  ]
+}

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.3_convert_machine_learning_to_federated_learning/02.3.1_convert_logistic_regression_to_federated_learning/code/newton_raphson/app/config/config_fed_server.json

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+{
+  "format_version": 2,
+  "server": {
+    "heart_beat_timeout": 600
+  },
+  "task_data_filters": [],
+  "task_result_filters": [],
+  "components": [
+    {
+      "id": "newton_raphson_persistor",
+      "path": "newton_raphson_persistor.NewtonRaphsonModelPersistor",
+      "args": {
+        "n_features": 13
+      }
+    },
+    {
+      "id": "tb_analytics_receiver",
+      "path": "nvflare.app_opt.tracking.tb.tb_receiver.TBAnalyticsReceiver",
+      "args.events": ["fed.analytix_log_stats"]
+    }
+  ],
+  "workflows": [
+    {
+      "id": "fedavg_newton_raphson",
+      "path": "newton_raphson_workflow.FedAvgNewtonRaphson",
+      "args": {
+        "num_clients": 4,
+        "num_rounds": 5,
+        "damping_factor": 0.8,
+        "persistor_id": "newton_raphson_persistor"
+      }
+    }
+  ]
+}