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joblib error, which may be from a deprecated pickling method #5

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freiburgermsu opened this issue Dec 19, 2023 · 0 comments
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@freiburgermsu
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Hello!

I receive the attached error when . This StOF post suggests (and ChatGPT concurs) that the error results from a new joblib version that no longer supports back compatibility. Can the pickled model be recreated using the newest joblib release?

Screen Shot 2023-12-19 at 1 54 13 PM

Here is ChatGPT's evaluation:

The KeyError: 118 you are encountering in your Python code when using the joblib package is likely due to a compatibility issue between the version of joblib (or Python) used to create the pickle file and the version you are using to load it.

Here are some steps to troubleshoot and potentially resolve the issue:

  1. Check Python Version Compatibility: Ensure that the Python version used for pickling (creating the .pkl file) is compatible with the version you are currently using. Incompatibilities between major Python versions (e.g., Python 2 vs 3) can lead to such errors.

  2. Joblib Version: Similarly, the version of joblib used to create and load the pickle file should be compatible. If the pickle file was created with an older version of joblib, try installing that version in your environment to load the file.

  3. Inspect the Pickle File: If possible, try to inspect the contents of the pickle file using the environment and joblib version it was created with. This can help determine if the file is corrupted or if there are specific compatibility issues.

  4. Recreate the Pickle File: If you have access to the original data and the environment used to create the pickle file, consider recreating it with your current setup of Python and joblib. This often resolves compatibility issues.

  5. Environment Replication: If the pickle file was created in a specific environment (like a different machine or a container), try to replicate that environment as closely as possible.

  6. Fallback to Python's Pickle Module: If joblib continues to give errors, consider using Python's built-in pickle module as a fallback, though this may not always be a viable solution depending on the specifics of the data and model.

If none of these solutions work, you might need more specific information about how the pickle file was created, including the versions of Python and joblib used, as well as the nature of the data and model it contains.

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