State of Python 3.13 Performance: Free-Threading

1. max

   1 23 23,851 10.0 Mojo

   The MAX Platform (includes Mojo)

   

2. Judoscale

   judoscale.com featured

   Save 47% on cloud hosting with autoscaling that just works. Judoscale integrates with Django, FastAPI, Celery, and RQ to make autoscaling easy and reliable. Save big, and say goodbye to request timeouts and backed-up task queues.

   

3. SWIG

   2 28 5,955 9.6 C++

   SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages.

   

   > it’s extremely easy to replace parts of your Python code with something like C

   I tend to use C++, so use SWIG [1] to make python code to interface with C++ (or C). You can nearly just give it a header file, and a python class pops out, with native types and interfaces. It's really magical.

   [1] https://www.swig.org

4. xarray

   3 11 3,764 9.7 Python

   N-D labeled arrays and datasets in Python

   

   Sadly, several python projects do not use semantic versioning, for example xarray [0] and dask. Numpy can make backward incompatible changes after a warning for two releases[1]. In general, the python packaging docs do not really read as an endorsement of semantic versioning [2]:

   > A majority of Python projects use a scheme that resembles semantic versioning. However, most projects, especially larger ones, do not strictly adhere to semantic versioning, since many changes are technically breaking changes but affect only a small fraction of users...

   [0] https://github.com/pydata/xarray/issues/6176

5. SciPy

   4 53 13,537 10.0 Python

   SciPy library main repository

   

   The race condition bugs are typically hidden by different software layers. For instance, we found one that involves OpenBLAS's pthreads-based thread pool management and maybe its scipy bindings:

   - https://github.com/scipy/scipy/issues/21479

   it might be the same as this one that further involves OpenMP code generated by Cython:

   - https://github.com/scikit-learn/scikit-learn/issues/30151

   We haven't managed to write minimal reproducers for either of those but as you can observe, those race conditions can only be triggered when composing many independently developed components.

6. scikit-learn

   5 88 61,717 9.9 Python

   scikit-learn: machine learning in Python

   

   The race condition bugs are typically hidden by different software layers. For instance, we found one that involves OpenBLAS's pthreads-based thread pool management and maybe its scipy bindings:

   - https://github.com/scipy/scipy/issues/21479

   it might be the same as this one that further involves OpenMP code generated by Cython:

   - https://github.com/scikit-learn/scikit-learn/issues/30151

   We haven't managed to write minimal reproducers for either of those but as you can observe, those race conditions can only be triggered when composing many independently developed components.

7. CodeRabbit

   coderabbit.ai featured

   CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.

   

Suggest a related project