SnakeViz VS line_profiler

From the same guy, use snakeviz to diagnose code. Video: [9:57] https://www.youtube.com/watch?v=m_a0fN48Alw

Python profiling is enabled primarily through cprofile, and can be visualized with help of tools like snakeviz (output flame graph can look like this). There are also memory profilers like memray which does in-depth traces, or sampling profilers like py-spy.

I'm looking for a Rust equivalent Python's cProfile https://docs.python.org/3/library/profile.html if possible with visualizations like in SnakeViz https://jiffyclub.github.io/snakeviz/

If you want stats for only your list of functions, you can do that with pstats, or you could use some third-party tool like https://jiffyclub.github.io/snakeviz/ or myriad other options.

I would make a list of tuples where each tuple is (2,0),(-2,0),(0,2),etc... and put those in a list. Then you can use random.choice to pick a random one out of the list. How often are you going to be doing this next to the edge of the grid? If its not that much then I would just try to have it re-draw a new random choice from the list in those cases. This isn't an elegant solution, since it could take an unknown amount of time to draw something valid, but it might be fast enough in practice. Then if that ends up causing issues on the edge cases you could have some logic to select a list to draw from that only contains the valid choices. Also remember, don't assume that some part of your code is the slow part. Always profile it to get some actual information on how long each section is taking so you can optimize the parts of the code that actually need it. I use snakeviz for profiling python code.

Official site: http://jiffyclub.github.io/snakeviz/

I've always used snakeviz with the stdlib profiler https://jiffyclub.github.io/snakeviz/

In prod, the pyinstrument profiler has worked well for me https://pyinstrument.readthedocs.io/en/latest/guide.html#pro...

its going to be very hard to give advice on this without seeing the code. could you share the relevant code? if you're not sure what the slow part is, i recommend https://jiffyclub.github.io/snakeviz/ it should allow you to see what function is taking long

LineProfiler is the best tool to learn how to write performant Python and code optimization.

https://github.com/pyutils/line_profiler

You can literally see the hot spot of your code, then you can grind different algorithms or change the whole architecture to make it faster.

For example replace short for loops to list comprehensions, vectorize all numpy operations (only vectorize partially do not help the issue), using 'not any()' instead or 'all()' for boolean, etc.

Doing this for like 2 weeks, basically you can automatically recognized most bad code patterns in a glance.

You may want to try using a package like line_profiler to narrow down where the time is spent.

When tuning for performance, always measure. Never assume you know where the slow parts are. Run a line profiler and see where all the time is actually going.

Either way I would highly recommend running a profiler on your code to see where the program is spending most of its time. line_profiler is a very nice one, as it shows you execution time for each line.

There are dedicated tools like line_profiler for python - if this doesn't do exactly what you need it can be easily modified.

It's surprising to me that an sklearn pipeline and a spacy pipeline both doing simple regexing are vastly different in performance. I would go one layer deeper with measurement with something like line_profiler, which I've used to great effect to get line-by-line perf stats. This should illuminate why.

This looks really nice! Does it use line_profiler or is it a different implementation for the profiling? Either way the interface is fantastic!

# https://github.com/pyutils/line_profiler pip install line_profiler kernprof -l -v some-code.py # This might take a while... Wrote profile results to some-code.py.lprof Timer unit: 1e-06 s Total time: 13.0418 s File: some-code.py Function: exp at line 3 Line # Hits Time Per Hit % Time Line Contents ============================================================== 3 @profile 4 def exp(x): 5 1 4.0 4.0 0.0 getcontext().prec += 2 6 1 0.0 0.0 0.0 i, lasts, s, fact, num = 0, 0, 1, 1, 1 7 5818 4017.0 0.7 0.0 while s != lasts: 8 5817 1569.0 0.3 0.0 lasts = s 9 5817 1837.0 0.3 0.0 i += 1 10 5817 6902.0 1.2 0.1 fact *= i 11 5817 2604.0 0.4 0.0 num *= x 12 5817 13024902.0 2239.1 99.9 s += num / fact 13 1 5.0 5.0 0.0 getcontext().prec -= 2 14 1 2.0 2.0 0.0 return +s