MLflow mlflow.exceptions.MlflowException: Invalid model version stage
MLflow mlflow.exceptions.MlflowException: Invalid model version stage
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 particularly useful for data scientists and engineers who need to manage multiple models and experiments efficiently.
Identifying the Symptom
When working with MLflow, you might encounter the error: 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 MLflow.
What You Observe
The error message is displayed in your console or logs, indicating that the model version stage you specified is invalid. This prevents the model from being transitioned to the desired stage, potentially halting your deployment process.
Explaining the Issue
The error occurs because MLflow uses predefined stages for model versioning, such as "None", "Staging", "Production", and "Archived". If you attempt to set a model to a stage that doesn't exist or is misspelled, MLflow will raise this exception.
Common Mistakes
Common mistakes include typos in the stage name or using a custom stage name that hasn't been configured in your MLflow setup. It's crucial to use the exact stage names as recognized by MLflow.
Steps to Fix the Issue
To resolve this issue, follow these steps:
Step 1: Verify Stage Names
Ensure that the stage name you are using is one of the predefined stages. You can refer to the MLflow Model Registry documentation for a list of valid stages.
Step 2: Correct the Stage Name
If you find a typo or incorrect stage name, update your code to use the correct stage. For example, if you mistakenly used "Prod" instead of "Production", correct it in your script:
from mlflow.tracking import MlflowClient
client = MlflowClient()
client.transition_model_version_stage(
name="YourModelName",
version=1,
stage="Production"
)
Step 3: Re-run Your Code
After making the necessary corrections, re-run your code to ensure that the model transitions to the correct stage without errors.
Conclusion
By ensuring that you use valid and correctly spelled stage names, you can avoid the Invalid model version stage error in MLflow. For more information on managing model stages, visit the MLflow Model Registry documentation.
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