#labeille — Public Fediverse posts

labeille Package Registry stats

Top 3.15 Blockers (364 packages):
* PyO3 / Rust / maturin: 111
* C extension build failures: 108
* pydantic-core (transitive PyO3): 69
* numpy / scipy / meson: 43

Once PyO3 adds 3.15 support, ~180 more packages will unlock (PyO3 direct + pydantic-core transitive)

Skip Reasons (418 packages):
* Monorepo subpackage (Azure, GCloud, etc.): 214
* No test suite found: 70
* No source repository: 52
* Type stub packages: 42

#Python #CPython #JIT #registry #labeille

labeille Package Registry stats

We've grown the registry: https://github.com/devdanzin/labeille/tree/main/registry/packages

* Total packages: 1,500
* Enriched (information collected and present): 1,500 (100%)
* Fully runnable on CPython 3.15: 654 (43.6%)
* Skipped (no tests, monorepo, etc.): 418 (27.9%)
* 3.15-specific blockers (skip_versions): 364 (24.3%)
* pytest: 95.1% (1,427 packages)
* unittest: 4.8% (72 packages)
* GitHub: 96.4% of repos
* Same JIT crash found in 7 packages

#Python #CPython #JIT #debugging #registry #labeille

labeille runs test suites from popular PyPI packages against a JIT-enabled CPython build and catches crashes: segfaults, assertion failures, etc.

If all of requests, flask, attrs, etc. pass their tests under the JIT, that shows the JIT is working. If one crashes, there's a bug with a reproducer. We've found one crash so far: https://github.com/python/cpython/issues/145197

This requires curating a local package registry with repo URLs, install and test commands, etc.

#Python #CPython #JIT #fuzzing #labeille #testing

labeille runs test suites from popular PyPI packages against a JIT-enabled CPython build and catches crashes: segfaults, assertion failures, etc.

If all of requests, flask, attrs, etc. pass their tests under the JIT, that shows the JIT is working. If one crashes, there's a bug with a reproducer. We've found one crash so far: https://github.com/python/cpython/issues/145197

This requires curating a local package registry with repo URLs, install and test commands, etc.

#Python #CPython #JIT #fuzzing #labeille #testing

labeille runs test suites from popular PyPI packages against a JIT-enabled CPython build and catches crashes: segfaults, assertion failures, etc.

If all of requests, flask, attrs, etc. pass their tests under the JIT, that shows the JIT is working. If one crashes, there's a bug with a reproducer. We've found one crash so far: https://github.com/python/cpython/issues/145197

This requires curating a local package registry with repo URLs, install and test commands, etc.

#Python #CPython #JIT #fuzzing #labeille #testing

labeille runs test suites from popular PyPI packages against a JIT-enabled CPython build and catches crashes: segfaults, assertion failures, etc.

If all of requests, flask, attrs, etc. pass their tests under the JIT, that shows the JIT is working. If one crashes, there's a bug with a reproducer. We've found one crash so far: https://github.com/python/cpython/issues/145197

This requires curating a local package registry with repo URLs, install and test commands, etc.

#Python #CPython #JIT #fuzzing #labeille #testing

labeille runs test suites from popular PyPI packages against a JIT-enabled CPython build and catches crashes: segfaults, assertion failures, etc.

If all of requests, flask, attrs, etc. pass their tests under the JIT, that shows the JIT is working. If one crashes, there's a bug with a reproducer. We've found one crash so far: https://github.com/python/cpython/issues/145197

This requires curating a local package registry with repo URLs, install and test commands, etc.

#Python #CPython #JIT #fuzzing #labeille #testing

I've been working on a new Python tool: labeille. Its main purpose is to look for CPython JIT crashes by running real world test suites.

https://github.com/devdanzin/labeille

But it's grown a feature that might interest more people: benchmarking using PyPI packages.

How does that work?

labeille allows you to run test suites in 2 different configurations. Say, with coverage on and off, or memray on and off. Here's an example:

https://gist.github.com/devdanzin/63528343df98779b5fedf657bf8286cd

#Python #labeille #fuzzing #JIT #PyPI #benchmarking

I've been working on a new Python tool: labeille. Its main purpose is to look for CPython JIT crashes by running real world test suites.

https://github.com/devdanzin/labeille

But it's grown a feature that might interest more people: benchmarking using PyPI packages.

How does that work?

labeille allows you to run test suites in 2 different configurations. Say, with coverage on and off, or memray on and off. Here's an example:

https://gist.github.com/devdanzin/63528343df98779b5fedf657bf8286cd

#Python #labeille #fuzzing #JIT #PyPI #benchmarking

I've been working on a new Python tool: labeille. Its main purpose is to look for CPython JIT crashes by running real world test suites.

https://github.com/devdanzin/labeille

But it's grown a feature that might interest more people: benchmarking using PyPI packages.

How does that work?

labeille allows you to run test suites in 2 different configurations. Say, with coverage on and off, or memray on and off. Here's an example:

https://gist.github.com/devdanzin/63528343df98779b5fedf657bf8286cd

#Python #labeille #fuzzing #JIT #PyPI #benchmarking