What bottlenecks were preventing NumPy from scaling on free-threaded Python? Christopher Trudeau is back on the show this week with another batch of PyCoder’s Weekly articles and projects.
Christopher shares a recent article on the work done in NumPy to make multi-threaded workloads scale on the free-threaded build of CPython. It covers how removing the Global Interpreter Lock (GIL) is not enough on its own and how profiling revealed hidden bottlenecks in NumPy and CPython.
We also share other articles and projects from the Python community, including recent releases, a pair of announcements from PyPI, a tool for crawling your Django project, SIMD in pure Python, managing wildcard imports, a publication-quality Python Data Visualization library, and a tool to review your Django schema.
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Topics:
- 00:00:00 – Introduction
- 00:02:26 – Python 3.15.0 candidate 1 is here!
- 00:02:47 – Python 3.14.7 and 3.13.15 are now available!
- 00:03:00 – Django 6.1 released
- 00:03:39 – Planned Updates to the PyPI User Interface
- 00:04:13 – PyPI Releases Now Reject New Files After 14 Days
- 00:05:07 – PEP 837 – Extensible JSON serialization
- 00:06:20 – PEP 842: Module Exports
- 00:07:51 – Introducing
django-crawl
- 00:12:02 – SIMD in Pure Python
- 00:18:29 – Managing Imports With Python’s
__all__
- 00:29:34 – Spotlight: Modern Object-Oriented Python Book
- 00:30:53 – Scaling NumPy on Free-Threaded Python
- 00:37:53 – cnsplots: Python Data Visualization for Complex Datasets
- 00:42:19 – django-orm-lens: Django Schema Review
- 00:45:14 – Thanks and goodbye
News:
Show Links:
- Introducing
django-crawl – During a recent site migration, Adam used the Django test harness to crawl his site looking for missing security headers. In the process he uncovered seven other bugs for a project that had 100% code coverage. He has consolidated the crawling technique for testing into a library: django-crawl.
- SIMD in Pure Python – SIMD is Single Instruction, Multiple Data, an approach that does calculations with vectors of data sets. Python doesn’t support it natively, but libraries like NumPy allow you to code this way.
- Thoughts on “SIMD in Pure Python” - purplesyringa’s blog
- Managing Imports With Python’s
__all__ – Learn how Python’s dunder all variable controls wildcard imports and shapes the public API your packages and modules expose.
- Scaling NumPy on Free-Threaded Python – A recap on the work done in NumPy and CPython to make multi-threaded NumPy workloads scale on the free-threaded build of CPython.
Projects:
Additional Links:
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