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. One of its key features is the model registry, which allows users to track and manage different versions of their models.
When working with MLflow, you might encounter the error: mlflow.exceptions.MlflowException: Invalid model version. This error typically arises when attempting to interact with a model version that is not recognized by the MLflow model registry.
Upon executing a command or script that interacts with a specific model version, the operation fails, and the above error message is displayed. This prevents further actions related to the model version, such as deployment or transition to a different stage.
The error is triggered when the specified model version does not exist in the MLflow model registry. This could be due to a typo in the version number, an attempt to access a version that has not been registered, or a version that has been deleted.
To resolve this issue, follow these steps to ensure the model version is correctly specified and exists in the registry.
First, confirm the model version you are trying to access. Use the MLflow UI or the MLflow CLI to list all available versions of the model:
mlflow models list --model-name <your_model_name>
This command will display all registered versions of the specified model. Ensure the version you are trying to access is listed.
If the version number was incorrect, update your script or command to use the correct version number as displayed in the list from Step 1.
If the version does not exist, you may need to register it. Use the following command to register a new version:
mlflow register-model --model-uri <model_uri> --name <your_model_name>
Replace <model_uri> with the URI of your model artifact and <your_model_name> with the desired model name.
For more information on managing model versions in MLflow, refer to the MLflow Model Registry documentation. Additionally, explore the MLflow CLI documentation for more commands and options.
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