update service data

.gitignore

@@ -1,2 +1,3 @@
 .DS_Store
+__pycache__
 model

README.md

@@ -29,5 +29,7 @@ Try to use [MLflow](https://mlflow.org) platform to log PyTorch model training,
   * a script to test MLflow REST api
 * **log_unsupported_model.py**
   * a sample script to apply mlflow.pyfunc to package unsupported ml model which can be logged and registered by mlflow
+* **optimize_model.py**
+  * a sample script to demonstrate how to use MLflow and TensorRT libs to optimize Pytorch model on edge devices and fetch it out on client
 
 ###### tags: `MLOps`

optimize_model.py

@@ -10,11 +10,13 @@ import torch
 import mlflow
 
 
-def optimize_pytorch_model(run_id: str) -> None:
+def optimize_pytorch_model(run_id: str,
-    """Optimize Pytorch model on MLflow server, the optimized model will be sent back
+                           model_artifact_dir: str = 'model') -> None:
+    """Optimize Pytorch model from MLflow server on edge device
 
     Args:
         run_id (str): mlflow run id
+        model_artifact_dir (str, optional): model dir of run on server. Defaults to 'model'.
     """
 
     download_path = Path('./model/downloaded_pytorch_model')
@@ -22,26 +24,52 @@ def optimize_pytorch_model(run_id: str) -> None:
         print(f'Remove existed dir: {download_path}')
         shutil.rmtree(download_path)
 
-    # Download Pytorch model to local file system
+    # Download model artifacts to local file system
-    mlflow_model = mlflow.pytorch.load_model(f'runs:/{run_id}/model')
+    mlflow_model = mlflow.pytorch.load_model(Path(f'runs:/{run_id}').joinpath(model_artifact_dir).as_posix())
     mlflow.pytorch.save_model(mlflow_model, download_path)
 
     # Optimize model
     model = torch.load(download_path.joinpath('data/model.pth'))
     dummy_input = torch.randn(5)
     torch.onnx.export(model, dummy_input, download_path.joinpath('data/model.onnx'))
-    # we can not call TensorRT on macOS, so imagine we get a serialized model
+    # we can not call TensorRT on macOS, so imagine we get a serialized model😘
     download_path.joinpath('data/model.trt').touch()
 
-    # Save optimized model to given run
+    # Sent optimized model back to given run
     with mlflow.start_run(run_id=run_id):
         mlflow.log_artifact(download_path.joinpath('data/model.trt'), 'model/data')
         print(f'Optimized model had been uploaded to server: {mlflow.get_tracking_uri()}')
 
 
+def download_optimized_model(run_id: str,
+                             save_dir: str,
+                             model_artifact_path: str = 'model/data/model.trt') -> None:
+    """Download optimized model from MLflow server on clent
+
+    Args:
+        run_id (str): mlflow run id
+        save_dir (str): dir of local file system to save model
+        model_artifact_path (str, optional): artifact path of model on server. Defaults to 'model/data/model.trt'.
+    """
+
+    mlflow.artifacts.download_artifacts(
+        run_id= run_id,
+        artifact_path=model_artifact_path,
+        dst_path=save_dir
+    )
+
+    print(f'Optimized model had been saved, please check: {Path(save_dir).joinpath(model_artifact_path)}')
+
+
 if __name__ == '__main__':
 
     mlflow.set_tracking_uri('http://127.0.0.1:5001')
+
     optimize_pytorch_model(
         run_id='f1b7b9a5ba934f158c07975a8a332de5'
     )
+
+    download_optimized_model(
+        run_id='f1b7b9a5ba934f158c07975a8a332de5',
+        save_dir='./model/download_tensorrt'
+    )