1014-mlflow-handler-tutorial

README.md

@@ -150,6 +150,8 @@ This is example walks through using a Triton Server and Python client using MONA
 An example of experiment management with [Aim](https://aimstack.io/aim-monai-tutorial/), using 3D spleen segmentation as an example.
 ##### [MLFlow](./experiment_management/spleen_segmentation_mlflow.ipynb)
 An example of experiment management with [MLFlow](https://www.mlflow.org/docs/latest/tracking.html), using 3D spleen segmentation as an example.
+##### [MONAI bundle integrates MLFlow](./experiment_management/bundle_integrate_mlflow.ipynb)
+An example shows how to easily enable and customize the MLFlow for experiment management in MONAI bundle.
 
 #### <ins>**Federated Learning**</ins>
 ##### [NVFlare](./federated_learning/nvflare)

experiment_management/README.md

@@ -0,0 +1,7 @@
+# Overview
+This directory shows how to do experiment management in MONAI.
+
+## Files
+1. spleen_segmentation_aim.ipynb: an example about how to deploy aim experiment management in pytorch code.
+2. spleen_segmentation_mlflow.ipynb: an example about how to deploy mlflow experiment management in pytorch code.
+3. bundle_integrate_mlflow.ipynb: an example shows how to easily enable and customize the MLFlow for experiment management in MONAI bundle.

experiment_management/mlflow_example.json

@@ -0,0 +1,26 @@
+{
+    "handlers_id": {
+        "trainer": {
+            "id": "train#trainer",
+            "handlers": "train#handlers"
+        }
+    },
+    "configs": {
+        "tracking_uri": "$@output_dir + '/mlruns'",
+        "experiment_name": "monai_experiment",
+        "run_name": "test1",
+        "is_not_rank0": "$torch.distributed.is_available() and torch.distributed.is_initialized() and torch.distributed.get_rank() > 0",
+        "trainer": {
+            "_target_": "MLFlowHandler",
+            "_disabled_": "@is_not_rank0",
+            "tracking_uri": "@tracking_uri",
+            "experiment_name": "@experiment_name",
+            "run_name": "@run_name",
+            "iteration_log": true,
+            "epoch_log": true,
+            "tag_name": "train_loss",
+            "output_transform": "$monai.handlers.from_engine(['loss'], first=True)",
+            "experiment_param": "${'backbone':'unet', 'norm':'Batch'}"
+        }
+    }
+}