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 streamline their workflow, from tracking experiments to deploying models. MLflow is widely used in the industry for its flexibility and integration capabilities with various machine learning libraries and frameworks.
When working with MLflow, you might encounter the error: mlflow.exceptions.MlflowException: Invalid model version. This error typically occurs when attempting to interact with a model version that is not recognized by the MLflow system.
Upon executing a command to retrieve or manipulate a model version, the system throws an exception indicating that the model version is invalid. This halts any further operations related to that model version.
The Invalid model version error arises when the specified model version does not exist in the MLflow registry or is incorrectly referenced. This can happen due to typographical errors, incorrect version numbers, or attempting to access a version that has been deleted or never created.
To resolve this issue, follow these steps to ensure the model version is correctly specified and exists in the MLflow registry.
Ensure that the model version you are trying to access is correct. Double-check the version number for any typographical errors. You can list all available versions of a model using the MLflow CLI or API:
mlflow models list --model-name <your_model_name>
This command will display all registered versions of the specified model.
Access the MLflow Model Registry to confirm the existence of the model version. You can do this through the MLflow UI or programmatically:
from mlflow.tracking import MlflowClient
client = MlflowClient()
versions = client.get_latest_versions("<your_model_name>")
for version in versions:
print(f"Version: {version.version}, Status: {version.current_stage}")
This script will print all versions of the model along with their current stage.
If the model version does not exist, you may need to register it first. Ensure that your code references the correct version number. If the version was deleted, consider re-registering it or using an alternative version.
For more information on managing models with MLflow, refer to the official MLflow Model Registry documentation. Additionally, explore the MLflow Python API for programmatic access to model versions and other features.
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