MLflow mlflow.exceptions.MlflowException: Invalid model version stage

What is MLflow mlflow.exceptions.MlflowException: Invalid model version stage

Understanding MLflow and Its Purpose

MLflow is an open-source platform designed to manage the machine learning lifecycle, including experimentation, reproducibility, and deployment. It provides tools to track experiments, package code into reproducible runs, and share and deploy models. MLflow is widely used by data scientists and machine learning engineers to streamline their workflow and ensure consistency across different stages of model development.

Identifying the Symptom

When working with MLflow, you might encounter the following error: mlflow.exceptions.MlflowException: Invalid model version stage. This error typically arises when attempting to transition a model to a stage that is not recognized by MLflow.

Exploring the Issue

What Does the Error Mean?

This error indicates that the model version stage you are trying to assign does not exist or is not valid within the MLflow model registry. MLflow uses predefined stages such as 'None', 'Staging', 'Production', and 'Archived' to manage model lifecycle transitions.

Common Causes

The most common cause of this error is a typo or incorrect stage name in the code. It may also occur if you attempt to use a custom stage name that has not been set up in your MLflow environment.

Steps to Fix the Issue

Verify the Model Version Stage

First, ensure that the stage name you are using is correct. MLflow supports the following default stages:

None: The initial stage for a newly created model version. Staging: Used for testing and validation. Production: Indicates the model is ready for production use. Archived: For models that are no longer in use.

Check your code to ensure you are using one of these stages. If you need to use a custom stage, make sure it is properly configured in your MLflow setup.

Correcting the Stage Name

If you find a typo or incorrect stage name, update your code to use the correct stage. For example, if you intended to set the model to 'Production', ensure your code reflects this:

mlflow.register_model(model_uri, name, stage='Production')

Using the MLflow UI

You can also use the MLflow UI to verify and change model stages. Navigate to the MLflow Model Registry in your browser, find the model version, and manually set the stage to the desired value.

Conclusion

By ensuring the correct model version stage is specified, you can resolve the mlflow.exceptions.MlflowException: Invalid model version stage error. Always double-check stage names and utilize the MLflow UI for managing model stages effectively. For more information, refer to the MLflow Documentation.