MLflow mlflow.exceptions.MlflowException: Invalid model version name
MLflow mlflow.exceptions.MlflowException: Invalid model version name
Understanding MLflow
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 engineers to streamline their ML workflows.
Identifying the Symptom
When working with MLflow, you might encounter the error: mlflow.exceptions.MlflowException: Invalid model version name. This error typically arises when attempting to interact with a model version that MLflow cannot recognize.
What You Observe
During operations such as model registration, version retrieval, or deployment, the system throws an exception indicating that the model version name is invalid. This halts the process and prevents further actions on the model.
Exploring the Issue
The error mlflow.exceptions.MlflowException: Invalid model version name suggests that the model version name provided in your command or script does not match any existing version in the MLflow Model Registry. This could be due to a typo, an incorrect version number, or a non-existent version.
Common Causes
- Typographical errors in the version name.
- Attempting to access a version that hasn't been created or registered.
- Using an incorrect format for the version name.
Steps to Fix the Issue
To resolve this issue, follow these steps:
Verify the Model Version Name
- Check the version name you are using in your script or command. Ensure it matches the format and name of an existing version in the MLflow Model Registry.
- Use the MLflow UI or API to list all available model versions. This can help confirm the correct version name. You can do this by running the following command:
mlflow models list-versions --model-name <your_model_name>
Correct the Version Name
- If you find a typo or incorrect version number, update your script or command with the correct version name.
- Ensure that the version you are trying to access has been registered. If not, register the model version using:
mlflow models create-version --model-name <your_model_name> --source <model_source>
Additional Resources
For more information on managing models in MLflow, refer to the MLflow Model Registry documentation. Additionally, the MLflow Python API documentation provides further insights into handling models and versions programmatically.
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