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 track experiments, package code into reproducible runs, and share and deploy models. For more information, you can visit the official MLflow website.
When working with MLflow, you might encounter the error: mlflow.exceptions.MlflowException: Invalid run ID. This error typically occurs when attempting to access or manipulate a run using an ID that MLflow does not recognize.
The error arises when the run ID specified in your MLflow command or script does not match any existing run in the MLflow tracking server. This can happen due to typographical errors, using an outdated or incorrect run ID, or if the run has been removed from the tracking server.
Ensure that the run ID you are using is correct and exists in the MLflow tracking server. You can list all available runs using the MLflow CLI or API to verify the run ID.
Follow these steps to resolve the issue:
Use the MLflow CLI to list all runs and verify the correct run ID:
mlflow runs list
This command will display all runs, allowing you to confirm the correct run ID.
Double-check the run ID in your script or command for any typographical errors. Ensure that the ID is copied correctly from the list of available runs.
If you suspect the run might have been deleted, check the MLflow tracking server's logs or database to confirm its existence. You can also use the MLflow UI to visually inspect available runs.
Once you have verified the correct run ID, update your script or command to use this ID. Re-run your script to ensure the error is resolved.
For further assistance, consider exploring the following resources:
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