This episode we have an optimization 2fer.
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We begin looking at optimizing a subset of Python code for machine learning using the LLVM compiler with a project called PyLLVM which takes plain python code, compiles it to optimized machine instructions and distributes it across a cluster.
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In the second half, we look at a fabulous new way to work with MongoDB for Python writing data scientists. The project is called bson-numpy and provides a direct connection between MongoDB and NumPy and is 10x faster than standard pymongo.
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Links from the show:
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<b>Anna on Twitter</b>: <a href='https://twitter.com/annaisworking' target='_blank'>@annaisworking</a>
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<b>PyLLVM</b>: <a href='https://github.com/aherlihy/PythonLLVM' target='_blank'>github.com/aherlihy/PythonLLVM</a>
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<b>Wrestling Python into LLVM Intermediate Representation</b>: <a href='https://www.youtube.com/watch?v=knL-c9WIru8' target='_blank'>youtube.com/watch?v=knL-c9WIru8</a>
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<b>BSON-NumPy Docs</b>: <a href='https://readthedocs.org/projects/bson-numpy/' target='_blank'>readthedocs.org/projects/bson-numpy</a>
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<b>BSON-NumPy Package</b>: <a href='https://pypi.org/project/BSON-NumPy/' target='_blank'>pypi.org/project/BSON-NumPy</a>
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<b>BSON-NumPy on GitHub</b>: <a href='https://github.com/aherlihy/bson-numpy' target='_blank'>github.com/aherlihy/bson-numpy</a>
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<strong>Sponsored items</strong>
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<b>Our courses</b>: <a href='https://training.talkpython.fm/' target='_blank'>training.talkpython.fm</a>
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<b>Hired</b>: <a href='https://hired.com/?utm_source=podcast&utm_medium=talkpythontome&utm_term=cat-tech-software&utm_content=2k&utm_campaign=q1-16-episodesbanner' target='_blank'>hired.com/talkpythontome</a>
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<b>Podcast's Patreon</b>: <a href='https://www.patreon.com/mkennedy' target='_blank'>patreon.com/mkennedy</a>
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