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. One of its core components is the Model Registry, which is a centralized store to manage the full lifecycle of an MLflow Model, including versioning, stage transitions, and annotations.
When working with MLflow, you might encounter the error: mlflow.exceptions.MlflowException: Model version already exists. This error typically occurs when you attempt to register a model version that already exists in the MLflow Model Registry.
Upon trying to register a new model version, the process fails, and the above exception is thrown. This prevents the new version from being added to the registry.
The error arises because MLflow enforces unique version numbers for each model within the registry. If you attempt to register a model with a version number that already exists, MLflow will raise an exception to prevent duplicate entries.
The root cause of this issue is that a model version with the specified name and version already exists in the model registry. This could happen if you are trying to re-register a model without incrementing the version number.
To resolve this issue, you have a couple of options:
import mlflow
client = mlflow.tracking.MlflowClient()
client.create_registered_model("MyModel")
client.create_model_version(name="MyModel", source="path/to/model", run_id="run-id")
If the existing version is no longer needed, you can delete it to free up the version number:
client.delete_model_version(name="MyModel", version=1)
Note: Deleting a model version is irreversible, so ensure that the version is no longer needed before proceeding.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
