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 help data scientists and engineers track experiments, package code into reproducible runs, and share and deploy models. One of the key features of MLflow is its Model Registry, which allows users to manage the lifecycle of a machine learning model, including versioning and stage transitions.
When working with 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 MLflow Model Registry.
In MLflow, model version stages are predefined states that a model can be in, such as None, Staging, Production, and Archived. These stages help in managing the lifecycle of a model, allowing for controlled transitions and deployments.
This error occurs when the specified stage does not match any of the recognized stages in the MLflow Model Registry. It could be due to a typo, or an attempt to use a custom stage name that has not been properly configured.
First, ensure that the stage you are trying to set is one of the valid stages. The valid stages are:
NoneStagingProductionArchivedCheck your code or configuration to ensure that you are using one of these stages. For example, if you are using the MLflow Python client, your code might look like this:
from mlflow.tracking import MlflowClient
client = MlflowClient()
client.transition_model_version_stage(
name="MyModel",
version=1,
stage="Production"
)
If you have a typo in the stage name, correct it to match one of the valid stages. For instance, if you mistakenly typed Prodction instead of Production, update your code accordingly.
For more information on MLflow and managing model stages, consider visiting the following resources:
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 in MLflow. Proper management of model stages is crucial for maintaining a smooth machine learning workflow and ensuring that models are deployed correctly.
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