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 manage and deploy models. One of its key components is the MLflow Model Registry, which allows users to manage the lifecycle of a machine learning model, including stages like 'Staging', 'Production', and 'Archived'.
When working with the MLflow Model Registry, you might encounter the error: mlflow.exceptions.MlflowException: Transition request failed. This error typically occurs when there is an issue transitioning a model version to a new stage in the registry.
While attempting to change the stage of a model version, the transition fails, and the above exception is raised. This can disrupt the workflow, especially if the model needs to be moved to 'Production' for deployment.
The error indicates that the transition request for a model version was unsuccessful. This can happen due to several reasons:
MLflow Model Registry can have lifecycle policies that restrict transitions between stages. Additionally, user permissions might prevent certain actions, such as moving a model to 'Production'.
To resolve the transition failure, follow these steps:
Ensure that the model version and the target stage exist. Use the MLflow UI or the following command to list model versions:
mlflow models list --name <model_name>
Check that the version you are trying to transition is listed and that the target stage is valid.
Review the lifecycle policies set in your MLflow Model Registry. Ensure that the transition you are attempting is allowed. You might need to consult with your MLflow administrator if you do not have access to view or modify these policies.
Ensure you have the necessary permissions to perform the transition. If you are using a managed MLflow service, check with your administrator to confirm your access rights.
Once you have verified the above, attempt the transition again using the MLflow UI or the following command:
mlflow models transition --name <model_name> --version <version_number> --stage <target_stage>
For more information on MLflow and managing model transitions, refer to the official MLflow Model Registry documentation. If you encounter further issues, consider reaching out to the MLflow community on GitHub for support.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
