palanteer
scalene
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palanteer | scalene | |
---|---|---|
6 | 32 | |
2,027 | 11,015 | |
- | 2.0% | |
0.0 | 9.3 | |
over 1 year ago | 5 days ago | |
C++ | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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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.
palanteer
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Tracking mentions began in Dec 2020.
scalene
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Memray – 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)
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How can I find out why my python is so slow?
Try using scalene to find where your code is running slow (and/or consuming lots of memory). You're talking two different OSs here, there are a ton of things that could explain the difference. But profiling will help you find where the bottleneck is
Use this my fren: https://github.com/plasma-umass/scalene
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Making Python 100x faster with less than 100 lines of Rust
You should take a look at Scalene - it's even better.
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Blog Post: Making Python 100x faster with less than 100 lines of Rust
I like seeing another Python profiler. The one I've been playing with is Scalene (GitHub). It does some fun things related to letting you see how much things are moving across the system Python memory boundary.
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OpenAI might be training its AI technology to replace some software engineers, report says
I tried out some features of machine learning models suggesting optimisations on code profiled by scalene and pretty much all of them would make the code less efficient, both time and memory wise. I am not worried. The devil is in the details and ML will not replace all of us anytime soon
- Modules Import and Optimisation
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[2022 day 16 (part 2)] [python 3.10] Can my solution be optimized?
Can't say about the floyd-warshall, since that's from elsewhere. However, I'd suggest profiling the code. For example scalene is pretty decent python profiler.
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What are some features you wish Python had?
How about scalene?
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Dwarf-Based Stack Walking Using eBPF
This is super awesome work and a great technical explanation of a very deep topic.
What happens in the case of JIT or FFI? I think I've only ever seen the Python profiler, scalene[0], handle these cases.
What are some alternatives?
flask-profiler - a flask profiler which watches endpoint calls and tries to make some analysis.
pytest-austin - Python Performance Testing with Austin
tracy - Frame profiler
memray - Memray is a memory profiler for Python
pyshader - Write modern GPU shaders in Python!
MTuner - MTuner is a C/C++ memory profiler and memory leak finder for Windows, PlayStation 4 and 3, Android and other platforms
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
easy_profiler - Lightweight profiler library for c++
python-benchmark-harness - A micro/macro benchmark framework for the Python programming language that helps with optimizing your software.
Dask - Parallel computing with task scheduling
magic-trace - magic-trace collects and displays high-resolution traces of what a process is doing
Keras - Deep Learning for humans