yappi
austin
Our great sponsors
yappi | austin | |
---|---|---|
5 | 12 | |
1,369 | 1,353 | |
- | - | |
7.3 | 7.5 | |
about 1 month ago | 15 days ago | |
Python | C | |
MIT License | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
yappi
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Profiling Python Code for Performance
yappi
- Yet Another Python Profiler, but this time thread&coroutine&greenlet aware.
- 10 Tools I Wish I Knew When I Started Working with Python
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Spy on Python down to the Linux kernel level
I've just realised after posting that the AUR package uses the git version, so it's actually normal that we have to use git version for austin-tui too and not the pypi one. Just if someone like me install the pypi version without paying attention, the git one is necessary.
For async code, the issue with normal profiler is that we end up mostly in the event loop. In Python there is https://github.com/sumerc/yappi which has a notion of coroutine profiling (check the README there), so I'm wondering if this would make sense in the context of Austin.
Anyway thanks for your work!
- (How to) profile python code?
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
What are some alternatives?
py-spy - Sampling profiler for Python programs
pyinstrument - π΄Β Call stack profiler for Python. Shows you why your code is slow!
SnakeViz - An in-browser Python profile viewer
python-socketio - Python Socket.IO server and client
line_profiler - Line-by-line profiling for Python
sanic - Accelerate your web app development | Build fast. Run fast.
schema - Schema validation just got Pythonic
aioflask - Flask running on asyncio!
pystack - π π Like pstack but for Python!
magda - Library for building Modular and Asynchronous Graphs with Directed and Acyclic edges (MAGDA)
austin-python - Python wrapper for Austin, the CPython frame stack sampler.