By Micha Gorelick, Ian Ozsvald
Your Python code could run competently, yet you wish it to run swifter. through exploring the elemental idea in the back of layout offerings, this useful advisor is helping you achieve a deeper knowing of Python's implementation. You'll how one can find functionality bottlenecks and considerably accelerate your code in high-data-volume programs.
How are you able to benefit from multi-core architectures or clusters? Or construct a procedure that could scale up and down with no wasting reliability? skilled Python programmers will research concrete ideas to those and different matters, in addition to conflict tales from businesses that use excessive functionality Python for social media analytics, productionized computing device studying, and different situations.
Get a greater clutch of numpy, Cython, and profilers
Learn how Python abstracts the underlying laptop architecture
Use profiling to discover bottlenecks in CPU time and reminiscence usage
Write effective courses by way of opting for applicable facts structures
Speed up matrix and vector computations
Use instruments to bring together Python all the way down to computing device code
Manage a number of I/O and computational operations concurrently
Convert multiprocessing code to run on a neighborhood or distant cluster
Solve huge difficulties whereas utilizing much less RAM
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Additional resources for High Performance Python: Practical Performant Programming for Humans
Always profile your code before compiling or rewriting your algo‐ rithms. You need evidence to determine the most efficient ways to make your code run faster. ” In particular, having an understanding of how Python’s stack-based virtual machine operates will help you to understand why certain coding styles run more slowly than others. Before the end of the chapter, we’ll review how to integrate unit tests while profiling (“Unit Testing During Optimization to Maintain Correctness” on page 56), to preserve the correctness of your code while you make it run more efficiently.
It may therefore make sense to run your tests on a smaller problem that completes in a useful amount of time. Overnight runs might be sensible for validation, but you need quick and reasonable iterations to diagnose prob‐ lems and hypothesize solutions. 5 hours to collect on Ian’s laptop. The requirement to modify the source code is a minor annoyance. As with line_profiler, a decorator (@profile) is used to mark the chosen function. This will break your unit tests unless you make a dummy decorator—see “No-op @profile Decorator” on page 57.
You need evidence to determine the most efficient ways to make your code run faster. ” In particular, having an understanding of how Python’s stack-based virtual machine operates will help you to understand why certain coding styles run more slowly than others. Before the end of the chapter, we’ll review how to integrate unit tests while profiling (“Unit Testing During Optimization to Maintain Correctness” on page 56), to preserve the correctness of your code while you make it run more efficiently.