Let's Remove the Global Interpreter Lock

Re:Let's Remove the Global Interpreter Lock by ogrisel on Wednesday August 16, 2017 @05:05AM (#15025995)

The pickling implementation of joblib has support for memory mapping numpy arrays nested in arbitrary data structures such as pandas dataframes.

Save the dataframe in a folder that can be accessed by the gunicorn worker:

    import joblib
    joblib.dump(df, '/folder/shared_data.pkl')

Then in the code run by the flask / gunicorn workers themselves:

    import joblib
    shared_df = joblib.load('/folder/shared_data.pkl', mmap_mode='r')
    # use the shared_df as usual (inplace modifications are not
    # authorized)

Some pandas function can have issues with read-only buffer though: https://github.com/pandas-dev/pandas/issues/17192 (caused by a currently unsolved bug / limitation of Cython) but it can work for your use case.

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Re:Let's Remove the Global Interpreter Lock by detroitcoder on Wednesday August 16, 2017 @10:18AM (#15027627)

This looks very interesting. I am reading the docs https://pythonhosted.org/joblib/parallel.html#manual-managem... and it looks like it would help a lot (possibly solve the issue). Do you have any experience using this in production?

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Re:Let's Remove the Global Interpreter Lock by detroitcoder on Wednesday August 16, 2017 @12:00PM (#15028534)

DAMN. I just did a basic test and it kinnda just worked?!? I created a test dataframe of 100M rows X 10 cols which took up ~2.3G and then used joblib.dump within the on_starting hook which is run when the gunicorn master starts up. Then loaded that df in with joblib.load within the worker and the total memory consumption was practically flat. Then I bumped up the number of workers to 20 and still flat. That is actually amazing. Coolest thing I have seen in months for how easy it is. Now I have to test out if the analytics actually work and a deep dive into the mechanics of mem-mapping.

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Re:Let's Remove the Global Interpreter Lock by ogrisel on Thursday August 24, 2017 @08:31AM (#15089518)

Thanks for your feedback. I am glad I could help you.

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