MLflow is an open-source platform designed to manage the machine learning lifecycle, including experimentation, reproducibility, and deployment. It provides a suite of tools to streamline the process of developing and deploying machine learning models. For more information, you can visit the official MLflow website.
When working with MLflow, you might encounter the following error message: mlflow.exceptions.MlflowException: Invalid model version name. This error indicates that there is an issue with the model version name you are trying to use.
The error typically occurs when attempting to retrieve or manipulate a model version using the MLflow Model Registry. The error message suggests that the version name provided is not recognized by MLflow.
The error mlflow.exceptions.MlflowException: Invalid model version name arises when the specified model version name does not conform to the expected format or does not exist in the MLflow Model Registry. This could be due to a typo, incorrect versioning, or attempting to access a version that has not been registered.
To resolve the Invalid model version name error, follow these steps:
Ensure that the model version name you are using is correct. Double-check for any typographical errors or incorrect formatting. The version name should match exactly with what is registered in the MLflow Model Registry.
Use the MLflow CLI or API to list all available model versions. This will help you confirm the correct version names. You can use the following command:
mlflow models list --model-name <your_model_name>
This command will display all versions of the specified model, allowing you to verify the correct version name.
If you find that the version name is incorrect, update your code or script to use the correct version name. Ensure that the version name is exactly as it appears in the MLflow Model Registry.
If the version does not exist, you may need to register it. Use the MLflow API to register a new version:
mlflow.register_model(
"runs:/<run_id>/<model_path>",
"<model_name>"
)
Replace <run_id>, <model_path>, and <model_name> with your specific details.
By following these steps, you should be able to resolve the Invalid model version name error in MLflow. Ensuring that your model version names are correct and registered will help maintain a smooth workflow. For further reading, check out the MLflow Model Registry documentation.
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