scalene
rayon
scalene | rayon | |
---|---|---|
32 | 67 | |
11,174 | 10,277 | |
1.4% | 1.9% | |
9.2 | 9.0 | |
7 days ago | 9 days ago | |
Python | Rust | |
Apache License 2.0 | 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.
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
rayon
- Rayon: Data-race free parallelization of sequential computations in Rust
- Too Dangerous for C++
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Which application/problem would you choose for presenting Rust to newcomers in 1h30min?
Do some operations with .iter() then later use rayon to parallelize. So you can show how easy is to add a dependency and how easy is to parallelize.
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What Are The Rust Crates You Use In Almost Every Project That They Are Practically An Extension of The Standard Library?
rayon: Async CPU runtime for parallelism.
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Moving from Typescript and Langchain to Rust and Loops
In the quest for more efficient solutions, the ONNX runtime emerged as a beacon of performance. The decision to transition from Typescript to Rust was an unconventional yet pivotal one. Driven by Rust's robust parallel processing capabilities using Rayon and seamless integration with ONNX through the ort crate, Repo-Query unlocked a realm of unparalleled efficiency. The result? A transformation from sluggish processing to, I have to say it, blazing-fast performance.
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AreWeMegafactoryYet? I just breached simulating 1M buildings @ 60 fps (If I'm not recording, Ryzen 7 1700X 8 Core)
With a lot of rayon, blood, sweat and tears I finally managed to simulate a million buildings at 60fps :) Feel free to AMA, game is Combine And Conquer
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The Rust I Wanted Had No Future
(see https://github.com/rayon-rs/rayon/tree/master/src/iter/plumbing)
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Parallel event iterator?
I did some very basic testing with this crate : https://crates.io/crates/rayon and it seems to work :
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General Recommendations: Should I Use Tree-sitter as the AST for the LSP I am developing?
Sequentially, generating tree-sitter AST for each file and querying for the links of each file takes around 2.3 seconds. However, I randomly remembered this crate rayon, and I decided to test it. It ended up improving the performance (just by changing 2 lines of code) to 200-300ms by parallelizing the iterators and tree-sitter queries. MAJOR.
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python to rust migration
Now if you really want to use Rust, you can rewrite only the part that are slowing down your consumer. It's easy by using Py03 and maturin. Maybe also rayon to parallelize.
What are some alternatives?
flask-profiler - a flask profiler which watches endpoint calls and tries to make some analysis.
crossbeam - Tools for concurrent programming in Rust
palanteer - Visual Python and C++ nanosecond profiler, logger, tests enabler
tokio - A runtime for writing reliable asynchronous applications with Rust. Provides I/O, networking, scheduling, timers, ...
pytest-austin - Python Performance Testing with Austin
RxRust - The Reactive Extensions for the Rust Programming Language
memray - Memray is a memory profiler for Python
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
pyshader - Write modern GPU shaders in Python!
tokio-rayon - Mix async code with CPU-heavy thread pools using Tokio + Rayon
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
coroutine-rs - Coroutine Library in Rust