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. MLflow is widely used by data scientists and engineers to streamline their ML workflows.
When working with MLflow, you might encounter the error: mlflow.exceptions.MlflowException: Invalid model version name. This error typically arises when attempting to interact with a model version that MLflow cannot recognize.
During operations such as model registration, version retrieval, or deployment, the system throws an exception indicating that the model version name is invalid. This halts the process and prevents further actions on the model.
The error mlflow.exceptions.MlflowException: Invalid model version name suggests that the model version name provided in your command or script does not match any existing version in the MLflow Model Registry. This could be due to a typo, an incorrect version number, or a non-existent version.
To resolve this issue, follow these steps:
mlflow models list-versions --model-name <your_model_name>
mlflow models create-version --model-name <your_model_name> --source <model_source>
For more information on managing models in MLflow, refer to the MLflow Model Registry documentation. Additionally, the MLflow Python API documentation provides further insights into handling models and versions programmatically.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
