Download High Performance Python: Practical Performant Programming by Micha Gorelick, Ian Ozsvald PDF

By Micha Gorelick, Ian Ozsvald

Your Python code could run properly, yet you would like it to run speedier. by way of exploring the basic concept in the back of layout offerings, this functional advisor is helping you achieve a deeper figuring out 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 process that may scale up and down with out wasting reliability? skilled Python programmers will research concrete options to those and different matters, besides struggle tales from businesses that use excessive functionality Python for social media analytics, productionized laptop studying, and different situations.

Get a greater seize of numpy, Cython, and profilers
Learn how Python abstracts the underlying machine architecture
Use profiling to discover bottlenecks in CPU time and reminiscence usage
Write effective courses via picking out applicable facts structures
Speed up matrix and vector computations
Use instruments to collect Python right down to computing device code
Manage a number of I/O and computational operations concurrently
Convert multiprocessing code to run on an area or distant cluster
Solve huge difficulties whereas utilizing much less RAM

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15 CHAPTER 2 Profiling to Find Bottlenecks Questions You’ll Be Able to Answer After This Chapter • How can I identify speed and RAM bottlenecks in my code? • How do I profile CPU and memory usage? • What depth of profiling should I use? • How can I profile a long-running application? • What’s happening under the hood with CPython? • How do I keep my code correct while tuning performance? Profiling lets us find bottlenecks so we can do the least amount of work to get the biggest practical performance gain.

Example 2-8. py ... 0 return output As expected, we can see from the output in Example 2-9 that calculate_z_seri al_purepython takes most (97%) of the time of its parent function. The list-creation steps are minor in comparison. Example 2-9. 218 s Line # Hits Per Hit % Time Line Contents ================================================== 24 @profile 25 def calc_pure_python(draw_output, desired_width, max_iterations): ... func_name + " took", secs, "seconds" 58 # this sum is expected for 1000^2 grid...

Some‐ times the additional information can lead to surprising insights into your code. cProfile is one of three profilers in the standard library; the others are hotshot and profile. hotshot is experimental code, and profile is the original pure-Python pro‐ filer. cProfile has the same interface as profile, is supported, and is the default profil‐ ing tool. If you’re curious about the history of these libraries, see Armin Rigo’s 2005 request to include cProfile in the standard library. A good practice when profiling is to generate a hypothesis about the speed of parts of your code before you profile it.

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