Add MLFlow tutorial

examples/tutorial/hello-pt-tb-mlflow/README.md

@@ -0,0 +1,18 @@
+# Hello PyTorch with MLFlow Experimental Tracking
+
+This example, show that one can using the existing tracker code written for Tensorboard, 
+but change the metrics tracking with both Tensorflow and MLFLow 
+
+The only change compare with hello-pt-tb example is adding the following component in fed_server_config.json
+
+```json
+  {
+      "id": "mlflow_receiver",
+      "path": "nvflare.app_opt.tracking.mlflow.mlflow_receiver.MLFlowReceiver",
+      "args": {
+        "kwargs": {"experiment_name": "hello-pt-experiments"},
+        "artifact_location": "artifacts"
+      }
+  }
+
+```

examples/tutorial/hello-pt-tb-mlflow/hello-pt-tb-mlflow-job1/app/config/config_fed_client.json

@@ -0,0 +1,45 @@
+{
+  "format_version": 2,
+
+  "executors": [
+    {
+      "tasks": [
+        "train",
+        "submit_model",
+        "validate"
+      ],
+      "executor": {
+        "id": "Executor",
+        "path": "nvflare.app_common.executors.learner_executor.LearnerExecutor",
+        "args": {
+          "learner_id": "pt_learner"
+        }
+      }
+    }
+  ],
+  "task_result_filters": [
+  ],
+  "task_data_filters": [
+  ],
+  "components": [
+    {
+      "id": "pt_learner",
+      "path": "pt_learner.PTLearner",
+      "args": {
+        "lr": 0.01,
+        "epochs": 5,
+        "analytic_sender_id": "analytic_sender"
+      }
+    },
+    {
+      "id": "analytic_sender",
+      "name": "AnalyticsSender",
+      "args": {"event_type": "analytix_log_stats"}
+    },
+    {
+      "id": "event_to_fed",
+      "name": "ConvertToFedEvent",
+      "args": {"events_to_convert": ["analytix_log_stats"], "fed_event_prefix": "fed."}
+    }
+  ]
+}

examples/tutorial/hello-pt-tb-mlflow/hello-pt-tb-mlflow-job1/app/config/config_fed_server.json

@@ -0,0 +1,81 @@
+{
+  "format_version": 2,
+
+  "server": {
+    "heart_beat_timeout": 600
+  },
+  "task_data_filters": [],
+  "task_result_filters": [],
+  "components": [
+    {
+      "id": "persistor",
+      "path": "nvflare.app_opt.pt.file_model_persistor.PTFileModelPersistor",
+      "args": {
+        "model": {
+          "path": "simple_network.SimpleNetwork"
+        }
+      }
+    },
+    {
+      "id": "shareable_generator",
+      "path": "nvflare.app_common.shareablegenerators.full_model_shareable_generator.FullModelShareableGenerator",
+      "args": {}
+    },
+    {
+      "id": "aggregator",
+      "path": "nvflare.app_common.aggregators.intime_accumulate_model_aggregator.InTimeAccumulateWeightedAggregator",
+      "args": {
+        "expected_data_kind": "WEIGHTS"
+      }
+    },
+    {
+      "id": "model_locator",
+      "path": "nvflare.app_opt.pt.file_model_locator.PTFileModelLocator",
+      "args": {
+        "pt_persistor_id": "persistor"
+      }
+    },
+    {
+      "id": "json_generator",
+      "path": "nvflare.app_common.widgets.validation_json_generator.ValidationJsonGenerator",
+      "args": {}
+    },
+    {
+      "id": "tb_analytics_receiver",
+      "name": "TBAnalyticsReceiver",
+      "args": {"events": ["fed.analytix_log_stats"]}
+    },
+    {
+      "id": "mlflow_receiver",
+      "path": "nvflare.app_opt.tracking.mlflow.mlflow_receiver.MLFlowReceiver",
+      "args": {
+        "kwargs": {"experiment_name": "hello-pt-experiments"},
+        "artifact_location": "artifacts"
+      }
+    }
+  ],
+  "workflows": [
+      {
+        "id": "scatter_and_gather",
+        "name": "ScatterAndGather",
+        "args": {
+            "min_clients" : 2,
+            "num_rounds" : 1,
+            "start_round": 0,
+            "wait_time_after_min_received": 10,
+            "aggregator_id": "aggregator",
+            "persistor_id": "persistor",
+            "shareable_generator_id": "shareable_generator",
+            "train_task_name": "train",
+            "train_timeout": 0
+        }
+      },
+      {
+        "id": "cross_site_validate",
+        "name": "CrossSiteModelEval",
+        "args": {
+          "model_locator_id": "model_locator"
+        }
+      }
+  ]
+}

examples/tutorial/hello-pt-tb-mlflow/hello-pt-tb-mlflow-job1/app/custom/pt_constants.py

@@ -0,0 +1,22 @@
+# Copyright (c) 2021-2022, 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.
+
+
+class PTConstants:
+    PTServerName = "server"
+    PTFileModelName = "FL_global_model.pt"
+    PTLocalModelName = "local_model.pt"
+
+    PTModelsDir = "models"
+    CrossValResultsJsonFilename = "cross_val_results.json"