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 in the data science community for its flexibility and ease of integration with various ML libraries.
When working with MLflow, you might encounter the error: mlflow.exceptions.MlflowException: Invalid model version name. This error typically arises when attempting to access or manipulate a model version that MLflow cannot recognize.
This error often occurs during operations such as model registration, version retrieval, or deployment. It indicates that the specified model version name is either incorrect or does not exist in the MLflow registry.
The error is triggered when MLflow fails to locate a model version with the given name. This can happen due to typographical errors, incorrect version numbers, or attempting to access a version that hasn't been registered.
MLflow uses a model registry to manage different versions of a model. Each version is uniquely identified by a name and version number. Ensuring the correct specification of these identifiers is crucial for successful model management.
To resolve this issue, follow these steps:
Use the MLflow CLI or API to list available model versions. This helps confirm the existence of the specified version.
mlflow models list --model-name <your_model_name>
For more details, refer to the MLflow CLI documentation.
Update your code or script to reference the correct model version name. Ensure consistency with the names listed in the registry.
For further assistance, consider exploring the following resources:
By following these steps, you should be able to resolve the 'Invalid model version name' error and continue with your MLflow operations smoothly.
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(Perfect for DevOps & SREs)
