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. MLflow's components include tracking experiments, packaging code into reproducible runs, and sharing and deploying models.
When working with MLflow, you might encounter the error: mlflow.exceptions.MlflowException: Invalid model version stage. This error typically occurs when you attempt to transition a model to a stage that is not recognized by MLflow.
Upon executing a command to transition a model version to a different stage, the operation fails, and the above exception is raised. This prevents the model from being moved to the desired stage, impacting deployment or further experimentation.
The error indicates that the specified stage for the model version is not valid. MLflow uses predefined stages such as "None", "Staging", "Production", and "Archived". If a stage outside these predefined ones is specified, MLflow will raise an exception.
To resolve this issue, follow these steps:
Ensure that the stage name you are using is one of the predefined stages. Double-check for any typographical errors. The valid stages are:
"None""Staging""Production""Archived"Use the following command to transition a model version to a valid stage:
mlflow models transition --model-name "your_model_name" --version "your_version_number" --stage "Staging"
Replace "your_model_name" and "your_version_number" with your actual model name and version number.
Refer to the MLflow Model Registry documentation for more details on managing model stages.
By ensuring that you are using valid stage names and following the correct procedures, you can effectively manage your model versions in MLflow. For further assistance, consider exploring the MLflow documentation or reaching out to the community for support.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
