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This is Ian Ozsvald's blog (@IanOzsvald), I'm an entrepreneurial geek, a Data Science/ML/NLP/AI consultant, founder of the Annotate.io social media mining API, author of O'Reilly's High Performance Python book, co-organiser of PyDataLondon, co-founder of the SocialTies App, author of the A.I.Cookbook, author of The Screencasting Handbook, a Pythonista, co-founder of ShowMeDo and FivePoundApps and also a Londoner. Here's a little more about me.

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29 June 2011 - 7:49High Performance Python Tutorial v0.1 (from my 4 hour tutorial at EuroPython 2011)

UPDATE – the v0.2 High Performance Python tutorial is now available.

I enjoyed running a 4 hour tutorial on High Performance Python at EuroPython last week (great event guys!). The class was limited to 40 people and I’d love for more people to benefit from the several weeks of work that went into it so I’ve written it up as a 49 page PDF (license: Creative Commons By Attribution).

This is v0.1, please take a look and give me feedback so I can release an improved v0.2 within a few weeks. Is anything missing? Sure! A couple of sections just have src (no write-up) and there’s a bunch of IAN_TODO markers for me to complete for the next revision. The 49 pages should have something useful for you to chew on though.

Download “High Performance Python v0.1 (pdf)” and send me your feedback! The source code for the examples is on this github page (including the Sphinx src for the pdf). Get the updated v0.2 High Performance Python tutorial now.

The EuroPython tutorial slides are on slideshare as the High Performance Python tutorial.

Topics covered:

  • Python profiling (cProfile, RunSnake, line_profiler) – find bottlenecks
  • PyPy – Python’s new Just In Time compiler
  • Cython – annotate your code and compile to C
  • numpy integration with Cython – fast numerical Python library wrapped by Cython
  • ShedSkin – automatic code annotation and conversion to C
  • numpy vectors – fast vector operations using numpy arrays
  • NumExpr on numpy vectors – automatic numpy compilation to multiple CPUs and vector units
  • multiprocessing – built-in module to use multiple CPUs
  • ParallelPython – run tasks on multiple computers
  • pyCUDA – run tasks on your Graphics Processing Unit

If you haven’t been to a EuroPython – I definitely recommend them. Next year’s will also be in Florence (a lovely city with lovely people), the science/HPC tracks were very interesting to me and I hope to see more of the same next year.

 


Ian applies Data Science as an AI/Data Scientist for companies in ModelInsight, sign-up for Data Science tutorials in London. Historically Ian ran Mor Consulting. He also founded the image and text annotation API Annotate.io, co-authored SocialTies, programs Python, authored The Screencasting Handbook, lives in London and is a consumer of fine coffees.

6 Comments | Tags: Python