austin
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austin | SnakeViz | |
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12 | 10 | |
1,355 | 2,231 | |
- | - | |
7.2 | 5.2 | |
17 days ago | 5 months ago | |
C | Python | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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austin
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Memray β A Memory Profiler for Python
I collected a list of profilers (also memory profilers, also specifically for Python) here: https://github.com/albertz/wiki/blob/master/profiling.md
Currently I actually need a Python memory profiler, because I want to figure out whether there is some memory leak in my application (PyTorch based training script), and where exactly (in this case, it's not a problem of GPU memory, but CPU memory).
I tried Scalene (https://github.com/plasma-umass/scalene), which seems to be powerful, but somehow the output it gives me is not useful at all? It doesn't really give me a flamegraph, or a list of the top lines with memory allocations, but instead it gives me a listing of all source code lines, and prints some (very sparse) information on each line. So I need to search through that listing now by hand to find the spots? Maybe I just don't know how to use it properly.
I tried Memray, but first ran into an issue (https://github.com/bloomberg/memray/issues/212), but after using some workaround, it worked now. I get a flamegraph out, but it doesn't really seem accurate? After a while, there don't seem to be any new memory allocations at all anymore, and I don't quite trust that this is correct.
There is also Austin (https://github.com/P403n1x87/austin), which I also wanted to try (have not yet).
Somehow this experience so far was very disappointing.
(Side node, I debugged some very strange memory allocation behavior of Python before, where all local variables were kept around after an exception, even though I made sure there is no reference anymore to the exception object, to the traceback, etc, and I even called frame.clear() for all frames to really clear it. It turns out, frame.f_locals will create another copy of all the local variables, and the exception object and all the locals in the other frame still stay alive until you access frame.f_locals again. At that point, it will sync the f_locals again with the real (fast) locals, and then it can finally free everything. It was quite annoying to find the source of this problem and to find workarounds for it. https://github.com/python/cpython/issues/113939)
- Pystack: Like Pstack but for Python
- High performance profiling for Python 3.11
- What are my Python processes at?
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tqdm (Python)
Just wanted to add Austin: Python frame stack sampler for CPython written in pure C (https://github.com/P403n1x87/austin)
- Pyheatmagic: Profile and view your Python code as a heat map
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Spy on Python down to the Linux kernel level
If you follow the call stack carefully you should be able to get to the point where sklearn calls ddot_kernel_8 (indirectly in this case). Austin(p) reports source files as well, so that shouldn't be a problem (provided all the debug symbols are available). If you're collecting data with austinp, don't forget to resolve symbol names with the resolve.py utility (https://github.com/P403n1x87/austin/blob/devel/utils/resolve..., see the README for more details: https://github.com/P403n1x87/austin/blob/devel/utils/resolve...)
- (How to) profile python code?
- Spy on the Python garbage collector with Austin 3.1
- Austin 3: 0-instrumentation, 0-impact Python CPU/wall time and memory profiling
SnakeViz
- Alternative to for loop in python ?
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Python Built-in vs Looping
From the same guy, use snakeviz to diagnose code. Video: [9:57] https://www.youtube.com/watch?v=m_a0fN48Alw
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Python 3.11 delivers.
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.
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Hey Rustaceans! Got a question? Ask here! (40/2022)!
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/
- Scanning Function calls in a script - is there a tool?
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Apply decorator to all functions in a list
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.
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An efficient way of getting second neighbors of a point in a grid.
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.
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Profiled my Python video game code and then used snakeviz to check for performance bottlenecks
Official site: http://jiffyclub.github.io/snakeviz/
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Pyheatmagic: Profile and view your Python code as a heat map
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...
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Need a help to optimize the code when using pandas
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
What are some alternatives?
pyinstrument - π΄Β Call stack profiler for Python. Shows you why your code is slow!
tuna - :fish: Python profile viewer
line_profiler - Line-by-line profiling for Python
pygraphviz - Python interface to Graphviz graph drawing package
schema - Schema validation just got Pythonic
GooPyCharts - A Google Charts API for Python, meant to be used as an alternative to matplotlib.
yappi - Yet Another Python Profiler, but this time multithreading, asyncio and gevent aware.
Flask JSONDash - :snake: :bar_chart: :chart_with_upwards_trend: Build complex dashboards without any front-end code. Use your own endpoints. JSON config only. Ready to go.
pystack - π π Like pstack but for Python!
VisPy - Main repository for Vispy
austin-python - Python wrapper for Austin, the CPython frame stack sampler.