MLflow PyTorch example

This example demonstrates how you can upload and deploy an MLflow PyTorch model using the MLOps Python client. It uploads an MLflow PyTorch model to MLOps and analyzes it. It then sets its metadata and parameters, and deploys it to the dev environment in MLOps.

Before you begin

• Install PyTorch
• Install MLflow
• Install scikit-learn
• You will need the values for the following constants in order to successfully carry out the task. Contact your administrator to obtain deployment specific values.

Constant	Value	Description
MLOPS_API_URL	Usually: https://api.mlops.my.domain	Defines the URL for the MLOps Gateway component. You can verify the correct URL by navigating to the API URL in your browser. It should provide a page with a list of available routes.
TOKEN_ENDPOINT_URL	https://mlops.keycloak.domain/auth/realms/[fill-in-realm-name]/protocol/openid-connect/token	Defines the token endpoint URL of the Identity Provider. This uses Keycloak as the Identity Provider. Keycloak Realm should be provided.
REFRESH_TOKEN	<your-refresh-token>	Defines the user's refresh token
CLIENT_ID	<your-client-id>	Sets the client id for authentication. This is the client you will be using to connect to MLOps.
PROJECT_NAME	MLflow+PyTorch Upload And Deploy Example	Defines a project that the script will create for the MLflow model.
EXPERIMENT_NAME	pytorch-mlflow-model	Defines the experiment display name.
DEPLOYMENT_ENVIRONMENT	DEV	Defines the target deployment environment.
REFRESH_STATUS_INTERVAL	1.0	Defines a refresh interval for the deployment health check.
MAX_WAIT_TIME	300	Defines maximum waiting time for the deployment to become healthy.

The following steps demonstrate how you can use MLOps Python client to upload and deploy an MLflow PyTorch model in MLOps.

1. Download the MLflowPyTorchExample.py file.

2. Change the values of the following constants in your MLflowPyTorchExample.py file as given in the preceding data table.

   MLflowPyTorchExample.py

   ### Constants
   MLOPS_API_URL = <MLOPS_API_URL>
   TOKEN_ENDPOINT_URL = <TOKEN_ENDPOINT_URL>
   REFRESH_TOKEN = <REFRESH_TOKEN>
   CLIENT_ID = <CLIENT_ID>
   PROJECT_NAME = <PROJECT_NAME>
   EXPERIMENT_NAME = <EXPERIMENT_NAME>
   DEPLOYMENT_ENVIRONMENT = <DEPLOYMENT_ENVIRONMENT>
   REFRESH_STATUS_INTERVAL = <REFRESH_STATUS_INTERVAL>
   MAX_WAIT_TIME = <MAX_WAIT_TIME>

   MLflowPyTorchExample.py

   ### Constants
   MLOPS_API_URL = "https://api.mlops.my.domain"
   TOKEN_ENDPOINT_URL="https://mlops.keycloak.domain/auth/realms/[fill-in-realm-name]/protocol/openid-connect/token"
   REFRESH_TOKEN="<your-refresh-token>"
   CLIENT_ID="<your-mlops-client>"
   PROJECT_NAME = "MLflow+PyTorch Upload And Deploy Example"
   EXPERIMENT_NAME = "pytorch-mlflow-model"
   DEPLOYMENT_ENVIRONMENT = "DEV"
   REFRESH_STATUS_INTERVAL = 1.0
   MAX_WAIT_TIME = 300

3. Run the MLflowPyTorchExample.py file.

   python3 MLflowPyTorchExample.py

   Deployment has become healthy  

4. Finally, navigate to MLOps and click the project name MLflow+PyTorch Upload And Deploy Example under Projects to view the deployed model.

   

   Note

   For more information about model deployments in MLOps, see Understand deployments in MLOps.

Example walkthrough

This section provides a walkthrough of each of the sections in the MLflowPyTorchExample.py file.

1. Include the Helper function, which waits for the deployment to be healthy.

2. Convert the extracted metadata into storage compatible value objects.

3. Set up the token provider using an existing refresh token.

4. Set up the MLOps client.

5. Train the PyTorch model.

   MLflowPyTorchExample.py

   # Train pytorch model.
   X_train, y_train = sklearn.datasets.load_wine(return_X_y=True, as_frame=True)
   X_tensor = torch.from_numpy(X_train.to_numpy())
   y_tensor = torch.from_numpy(y_train.to_numpy())
   dataset = torch.utils.data.dataset.TensorDataset(X_tensor, y_tensor)
   
   torch_model = torch.nn.Linear(13, 1)
   loss_fn = torch.nn.MSELoss(reduction="sum")
   
   learning_rate = 1e-6
   for batch in dataset:
       torch_model.zero_grad()
   
       X, y = batch
       y_prediction = torch_model(X.float())
       loss = loss_fn(y_prediction, y.float())
       loss.backward()
   
       with torch.no_grad():
           for param in torch_model.parameters():
               param -= learning_rate * param.grad
   # Infering and setting model signature
   # Model signature is mandatory for models that are going to be loadable by the
   # server. Only ColSpec inputs and output are supported.
   model_signature = signature.infer_signature(X_train)
   model_signature.outputs = mlflow.types.Schema(
       [mlflow.types.ColSpec(name="quality", type=mlflow.types.DataType.float)]
   )

6. Create a project in MLOps and create an artifact in MLOps storage.

7. Store, zip, and upload the model.

   MLflowPyTorchExample.py

   # Storing, zipping and uploading the model
   model_tmp = tempfile.TemporaryDirectory()
   try:
       model_dir_path = os.path.join(model_tmp.name, "wine_model")
       mlflow.pytorch.save_model(
           torch_model, model_dir_path, signature=model_signature
       )
       zip_path = shutil.make_archive(
           os.path.join(model_tmp.name, "artifact"), "zip", model_dir_path
       )
       with open(zip_path, mode="rb") as zipped:
           mlops_client.storage.artifact.upload_artifact(
               file=zipped, artifact_id=artifact.id
           )
   finally:
       model_tmp.cleanup()

8. Analyze the MLflow zip file and create an experiment from it. Then link the artifact to the experiment.

9. Fetch the available deployment environments and search for the ID of the selected deployment environment.

10. Customize the composition of the deployment and specify the deployment as a single deployment.

11. Finally, create the deployment and wait for the deployment to become healthy. This analyzes and sets the metadata and parameters of the model, and deploys it to the DEV environment.

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