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 is widely used for tracking experiments, packaging code into reproducible runs, and sharing and deploying models.
When using MLflow, you might encounter the error mlflow.exceptions.MlflowException: Invalid lifecycle stage. This error typically arises when there is an issue with the lifecycle stage of a model version or experiment.
While attempting to transition a model version or experiment to a different lifecycle stage, you receive the above error message. This prevents you from successfully managing the lifecycle of your MLflow entities.
The error mlflow.exceptions.MlflowException: Invalid lifecycle stage indicates that the specified lifecycle stage is not recognized by MLflow. MLflow uses predefined lifecycle stages to manage models and experiments, such as "None", "Active", "Staging", and "Production". If an unrecognized stage is specified, this error will occur.
To resolve this issue, follow these steps:
Ensure that you are using one of the valid lifecycle stages supported by MLflow. The valid stages are:
NoneActiveStagingProductionCheck for any typographical errors in the stage name you are using.
If you find an error in the lifecycle stage name, correct it in your code or configuration. For example, if you are using the MLflow Python API, ensure your code looks like this:
mlflow.register_model(
"runs://",
""
)
mlflow.transition_model_version_stage(
name="",
version=,
stage="Staging"
)
Consult the MLflow Model Registry documentation to understand the correct usage of lifecycle stages and ensure compliance with MLflow's standards.
By ensuring that you use valid lifecycle stages and correcting any typographical errors, you can resolve the mlflow.exceptions.MlflowException: Invalid lifecycle stage error. This will allow you to effectively manage the lifecycle of your models and experiments in MLflow.
For further assistance, consider visiting the MLflow GitHub Issues page to see if others have encountered similar problems and solutions.
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