MLflow mlflow.exceptions.MlflowException: Invalid model version description

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

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. For more information, you can visit the official MLflow website.

Identifying the Symptom: Invalid Model Version Description

When working with MLflow, you might encounter the error: mlflow.exceptions.MlflowException: Invalid model version description. This error typically arises when there is an issue with the model version description you are trying to use.

Explaining the Issue: What Causes This Error?

The error occurs because the model version description provided is either not valid or does not exist. This can happen if there is a typo in the description, if the description is not properly formatted, or if the specified version does not exist in the MLflow registry.

Common Scenarios Leading to This Error

Incorrectly formatted model version description. Non-existent model version specified. Typographical errors in the description.

Steps to Fix the Invalid Model Version Description Issue

To resolve this issue, follow these steps:

Step 1: Verify the Model Version Description

Ensure that the model version description is correctly specified. Double-check for any typographical errors or incorrect formatting. The description should match exactly with what is stored in the MLflow registry.

Step 2: Check the Model Version Existence

Use the MLflow CLI or API to list available model versions and verify that the specified version exists. You can use the following command to list model versions:

mlflow models list-versions --name <model_name>

Replace <model_name> with the actual name of your model.

Step 3: Correct the Model Version Description

If you find any discrepancies, correct the model version description in your code or configuration. Ensure that it matches the version in the MLflow registry.

Additional Resources

For further assistance, you can refer to the MLflow Documentation or seek help from the MLflow community on Stack Overflow.