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faster-python-programs-through-optimization-part-0.json
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faster-python-programs-through-optimization-part-0.json
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{
"alias": "video/1293/faster-python-programs-through-optimization-part-0",
"category": "EuroPython 2012",
"copyright_text": "Standard YouTube License",
"description": "Objective This tutorial provides an overview of techniques to improve\nthe performance of Python programs. The focus is on concepts such as\nprofiling, diffrence of data structures and algorithms as well as a\nselection of tools an libraries that help to speed up Python. Intended\nAudience Python programmers who would like concepts to improve\nperformance. Audience Level Programmers with good Python knowledge.\nPrerequisites Please bring your laptop with the operating system of your\nchoice (Linux, Mac OS X, Windows). In addition to Python 2.6 or 2.7, we\nneed: RunSnakeRun (`http://www.vrplumber.com/programming\n/runsnakerun <http://www.vrplumber.com/programming/runsnakerun>`__)\nGuppy\\_PE framework (http://guppy-pe.sourceforge.net) (<= Python 2.6 )\nlineprofiler\n(`http://packages.python.org/line\\_profiler/ <htt%20p://packages.python.org/line_profiler/>`__)\npympler (http://code.google.com/p/pympler/) psyco\n(http://psyco.sourceforge.net, Python 2.6 only, version 1.5.2 or higher)\npypy (http://pypy.org) and NumPy (http://numpy.scipy.org, version 1.2 or\nhigher). Method This is a hands-on course. Students are strongly\nencouraged to work along with the trainer at the interactive prompt.\nThere will be exercises the students need to do on their own. Experience\nshows that this active involvement is essential for an effective\nlearning. Outline How fast is fast enough? Optimization guidelines\nPremature optimization Optimization rules Seven steps for incremental\noptimization Optimization strategy Measuring in stones Profiling CPU\nusage Profiling memory usage Algorithms and Anti-patterns String\nconcatenation List and generator comprehensions The right data structure\nCaching The example Testing speed Pure Python Meet Psyco, the JIT Using\nPyPy NumPy for numeric arrays Using multiple CPUs with multiprocessing\nCombination of optimization strategies Results of different example\nimplementations I taught this tutorial multiple times and will update\nthe content as I regularly do.\n",
"duration": null,
"id": 1293,
"language": "eng",
"quality_notes": "",
"recorded": "2012-07-05",
"related_urls": [
"http://code.google.com/p/pympler/)",
"http://guppy-pe.sourceforge.net)",
"http://numpy.scipy.org,",
"http://packages.python.org/line\\_profiler/",
"http://psyco.sourceforge.net,",
"http://pypy.org)",
"http://www.vrplumber.com/programming",
"http://www.vrplumber.com/programming/runsnakerun"
],
"slug": "faster-python-programs-through-optimization-part-0",
"speakers": [
"M Mollerv"
],
"summary": "[EuroPython 2012] M Mollerv- 4 JULY 2012 in \"Track Pizza Margherita\"\n",
"tags": [],
"thumbnail_url": "https://i.ytimg.com/vi/lvyfpteeA54/hqdefault.jpg",
"title": "Faster python programs through optimization part 1",
"videos": [
{
"length": 0,
"type": "youtube",
"url": "https://www.youtube.com/watch?v=lvyfpteeA54"
}
]
}