scalene
magic-trace
Our great sponsors
scalene | magic-trace | |
---|---|---|
32 | 29 | |
11,163 | 4,432 | |
1.9% | 1.1% | |
9.3 | 6.9 | |
3 days ago | about 2 months ago | |
Python | OCaml | |
Apache License 2.0 | MIT License |
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
-
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
-
How can I find out why my python is so slow?
Use this my fren: https://github.com/plasma-umass/scalene
-
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
-
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.
-
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
-
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
magic-trace
-
When Optimising Code, Measure
I really like magic-trace [0].
https://github.com/janestreet/magic-trace
Not that the exact tracing relies on Intel PT - support for AMD was added recently but uses perf so suffers from the same sampling/skew issues, but is still very useful.
-
Grafana Phlare, open source database for continuous profiling at scale
Would love to see this integrate with magic trace [1]. I'll need to look at the code for the flamegraph plugin, because handling nanosecond timestamps in flamegraphs seems to break most tools due to float precision.
(1) https://github.com/janestreet/magic-trace
-
How to break into Hudson River Trading?
As for inner workings, have you looked into magic trace? I want to play around with it but last I checked, it doesn't work on macs.
-
Brendan Intel.com
I really hope he can work with cloud vendors and Intel to make Processor Trace a more popular and easier to use capability.
It's unfortunate how https://github.com/janestreet/magic-trace and PMUs in general can't be used by lots of people using cloud VMs.
- GitHub - janestreet/magic-trace: magic-trace collects and displays high-resolution traces of what a process is doing
-
Hacker News top posts: Apr 23, 2022
Magic-trace – High-resolution traces of what a process is doing\ (133 comments)
- Magic-trace – High-resolution traces of what a process is doing
- Magic-trace - 高分辨率跟踪一个进程正在做什么 (Magic-trace – High-resolution traces of what a process is doing)
What are some alternatives?
flask-profiler - a flask profiler which watches endpoint calls and tries to make some analysis.
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
palanteer - Visual Python and C++ nanosecond profiler, logger, tests enabler
perspective - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
pytest-austin - Python Performance Testing with Austin
perfetto - Frontend for magic-trace; forks https://ui.perfetto.dev
memray - Memray is a memory profiler for Python
linux - Linux kernel source tree
pyshader - Write modern GPU shaders in Python!
tracy - Frame profiler