MLflow is an open-source platform designed to manage the machine learning lifecycle, including experimentation, reproducibility, and deployment. It provides a comprehensive suite of tools to track experiments, package code into reproducible runs, and share and deploy models. For more information, you can visit the official MLflow website.
When using MLflow, you might encounter the error message: mlflow.exceptions.MlflowException: Invalid model version stage. This error typically arises when attempting to transition a model to a stage that is not recognized by the system.
The error occurs because the model version stage specified does not match any of the predefined stages in MLflow. MLflow uses stages like 'None', 'Staging', 'Production', and 'Archived' to manage model lifecycle transitions. If a stage outside these predefined ones is used, the system will throw an exception.
To resolve this issue, follow these steps:
Ensure that the stage you are trying to set is one of the valid stages. You can refer to the MLflow Model Registry documentation for a list of valid stages.
If there is a typographical error, correct it to match one of the valid stages. For example, if you intended to set the stage to 'Production' but typed 'Prodution', correct the spelling.
Use the MLflow CLI or API to update the model stage. Here is an example using the MLflow CLI:
mlflow models transition --model-name "my_model" --version 1 --stage "Production"
Ensure that the --stage parameter is set to one of the valid stages.
By ensuring that the model version stage is correctly specified and matches one of the valid stages, you can resolve the Invalid model version stage error. For further assistance, consider exploring the MLflow documentation or reaching out to the community forums.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
