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Top 16 Python Cpu Projects
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scalene
Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
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WorkOS
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CuVec
Unifying Python/C++/CUDA memory: Python buffered array ↔️ `std::vector` ↔️ CUDA managed memory
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XNOR-popcount-GEMM-PyTorch-CPU-CUDA
A PyTorch implemenation of real XNOR-popcount (1-bit op) GEMM Linear PyTorch extension support both CPU and CUDA
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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)
github repository
There's a way of doing it via s-tui.
Project mention: Nvtop: Linux Task Monitor for Nvidia, AMD and Intel GPUs | news.ycombinator.com | 2024-03-12There’s also asitop https://github.com/tlkh/asitop
Project mention: T480 - Downgrading i7 8650 to i5 8250 because of fan noise | /r/thinkpad | 2023-12-08Yes i know it sounds wild but I need to be able to focus on my programming tasks. I run linux on my system and with throttled installed, it gets noisy and i'm tired of it. I'm not a gamer but I use win10 and win11 VMs and compile source code and just every day tasks spin up this fan. Before I installed throttled ( https://github.com/erpalma/throttled ) it was out of control. I haven HP 840 G5 that hardly even spins up. I got this because i had the same one for work and noticed it never bothered me. I have compared some compile times on the i5 and the i7 and even my desktop, they are all about the same so I dont see why I should care about having an i7. Im willing to buy a new mobo with the i5 8250 and just use that. i bought the dual heatsink fan and waiting for that to arrive so i'll install that first. But can anyone else share their experience with an i5 vs i7 and fan noise?
yappi
Project mention: FastSD CPU beta 21 - SDXL Turbo OpenVINO support (2.5 seconds on CPU) | /r/StableDiffusion | 2023-12-02Release : https://github.com/rupeshs/fastsdcpu/releases/tag/v1.0.0-beta.21
A related package is https://github.com/workhorsy/py-cpuinfo . You might look at it to see how they organize platform differences.
Shameless plug: if you are interested in relay processors, check out mine: https://github.com/artemonster/relay-cpu
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python Cpu related posts
- Nvtop: Linux Task Monitor for Nvidia, AMD and Intel GPUs
- Battery Drain Issue
- Does anyone know how Bitwig performs with the M series efficiency cores?
- FastSD CPU beta 21 - SDXL Turbo OpenVINO support (2.5 seconds on CPU)
- Krita AI Diffusion
- $95 AMD CPU Becomes 16GB GPU to Run AI Software
- How to cleanly uninstall auto-cpufreq?
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Index
What are some of the best open-source Cpu projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | scalene | 11,163 |
| 2 | psutil | 9,922 |
| 3 | auto-cpufreq | 5,002 |
| 4 | s-tui | 3,920 |
| 5 | asitop | 2,826 |
| 6 | throttled | 2,575 |
| 7 | yappi | 1,369 |
| 8 | OpenSeeFace | 1,312 |
| 9 | fastsdcpu | 870 |
| 10 | py-cpuinfo | 300 |
| 11 | 8bit-cpu | 297 |
| 12 | freqbench | 160 |
| 13 | CuVec | 79 |
| 14 | MCE-Ryzen-Decoder | 20 |
| 15 | XNOR-popcount-GEMM-PyTorch-CPU-CUDA | 14 |
| 16 | relay-cpu | 0 |
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