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 in the data science community to streamline the process of developing and deploying machine learning models.
When working with MLflow, you might encounter the error: mlflow.exceptions.MlflowException: Invalid model version description. This error typically arises when there is an issue with the model version description you have specified in your MLflow project.
Upon executing a command or script that interacts with MLflow models, the process fails, and the error message mentioned above is displayed. This indicates that MLflow is unable to process the model version description provided.
The error mlflow.exceptions.MlflowException: Invalid model version description is triggered when the model version description does not meet the expected criteria. This could be due to several reasons, such as:
In MLflow, a model version description is a string that provides additional context about a specific version of a model. It is important to ensure that this description is correctly formatted and accurately reflects the model version it is associated with.
To resolve the error, follow these steps:
Ensure that the model version description is correctly specified in your code or configuration. Check for any typos, invalid characters, or excessive length. The description should be a concise and clear string.
Confirm that the model version you are referencing exists in the MLflow model registry. You can list all model versions using the MLflow CLI or API:
mlflow models list --model-name <your_model_name>
Ensure that the version you are trying to describe is present in the list.
If the description is incorrect, update it using the MLflow API. Here is an example of how to update a model version description:
from mlflow.tracking import MlflowClient
client = MlflowClient()
client.update_model_version(
name="your_model_name",
version="your_version_number",
description="New valid description"
)
For more information on managing models in MLflow, refer to the MLflow Model Registry documentation. If you need further assistance, consider visiting the MLflow GitHub Issues page to see if others have encountered similar problems.
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