austin
pyinstrument
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austin | pyinstrument | |
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12 | 11 | |
1,355 | 6,105 | |
- | - | |
7.2 | 8.1 | |
16 days ago | 9 days ago | |
C | Python | |
GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
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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
pyinstrument
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How to profile an asynchronous FastAPI server
I was wondering if you have any synchronous routes in your app? We have an open issue regarding those and would love some ideas for solutions :)
- How to expand to full traceback
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Graphical Python Profiler
Is there something about the actual profiler that differs from existing tools like pyinstrument [1] or py-spy [2]? I know pyinstrument has various output options and I wonder if it could potentially output something readable by the Firefox Profiler tool.
[1] : https://github.com/joerick/pyinstrument
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DRF Performance (How bad is this code?)
I would highly recommend looking into pyinstrument to profile your requests. I find the reports to be very readable, and it's very easy to setup with Django.
- Looking for app that visualizes python program
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What are some python micro optimisations that you can/may *actually use* in your codebase?
I like to use pyinstrument for profiling. Uses stat sampling so it's quicker and builds a easy to grok report.
- (How to) profile python code?
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Profiling async code with pyinstrument 4.0
Hello! I just released pyinstrument 4.0 on PyPI. pyinstrument was my first successful open-source package and I love seeing how much use it's getting in the community.
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Getting started on profiling with python
PyInstrument, compared to cProfile or Yappi, it's a lot easier to use and requires much less configuration.
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Looking Beyond Nox
Run pytest, under pyinstrument, and generate a webpage that presents performance data, in addition to a separate junit xml report.
What are some alternatives?
SnakeViz - An in-browser Python profile viewer
py-spy - Sampling profiler for Python programs
line_profiler - Line-by-line profiling for Python
yappi - Yet Another Python Profiler, but this time multithreading, asyncio and gevent aware.
schema - Schema validation just got Pythonic
memray - Memray is a memory profiler for Python
Bazel - a fast, scalable, multi-language and extensible build system
pystack - π π Like pstack but for Python!
junit2html
austin-python - Python wrapper for Austin, the CPython frame stack sampler.
doit - task management & automation tool