scalene
palanteer
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| scalene | palanteer | |
|---|---|---|
| 32 | 6 | |
| 11,163 | 2,027 | |
| 1.9% | - | |
| 9.3 | 0.0 | |
| about 11 hours ago | over 1 year ago | |
| Python | C++ | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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.
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)
- Scalene: A high-performance CPU GPU and memory profiler for Python
- Scalene: A high-performance, CPU, GPU, and memory profiler for Python
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How can I find out why my python is so slow?
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.
https://github.com/plasma-umass/scalene
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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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Cum as putea sa imbunatatesc timpul de rulare al pitonului?
Ai vazut "Python Performance Matters" by Emery Berger (Strange Loop 2022)? E in principiu o prezentare si demo cu Scalene.
- Scalene - A Python CPU/GPU/memory profiler with optimization proposals
- Scalene: A Python CPU/GPU/memory profiler with optimization proposals
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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
palanteer
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Palanteer v0.5 is out! Live visual Python profiling with support of multiprocess
GitHub: https://github.com/dfeneyrou/palanteer
- Palanteer v0.5 is out! Live visual C++ profiling with support of auto instrumentation (GCC) and multi-process
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Palanteer, Python high performance visual profiler, now supports asyncio / gevent
The open source code is on github, the tool has very low dependencies (openGL 3.3 and a C++11 compiler)
- High performance visual profiler, debugger, tests enabler for C++ and Python
- Palanteer: High performance visual profiler, debugger for C++ and Python
What are some alternatives?
flask-profiler - a flask profiler which watches endpoint calls and tries to make some analysis.
tracy - Frame profiler
pytest-austin - Python Performance Testing with Austin
MTuner - MTuner is a C/C++ memory profiler and memory leak finder for Windows, PlayStation 4 and 3, Android and other platforms
memray - Memray is a memory profiler for Python
easy_profiler - Lightweight profiler library for c++
pyshader - Write modern GPU shaders in Python!
python-benchmark-harness - A micro/macro benchmark framework for the Python programming language that helps with optimizing your software.
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
optick - C++ Profiler For Games
Dask - Parallel computing with task scheduling
easyloggingpp - C++ logging library. It is extremely powerful, extendable, light-weight, fast performing, thread and type safe and consists of many built-in features. It provides ability to write logs in your own customized format. It also provide support for logging your classes, third-party libraries, STL and third-party containers etc.