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.
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.
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.
To resolve this issue, follow these steps:
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.
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.
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.
For further assistance, you can refer to the MLflow Documentation or seek help from the MLflow community on Stack Overflow.
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