#434 Most of OpenAI’s tech stack runs on Python
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Topics covered in this episode:

- [**Making PyPI’s test suite 81% faster**](https://blog.trailofbits.com/2025/05/01/making-pypis-test-suite-81-faster/?featured_on=pythonbytes))

• People aren’t talking enough about how most of OpenAI’s tech stack runs on Python)

• PyCon Talks on YouTube)

• Optimizing Python Import Performance)

• Extras

• Joke

Watch on YouTube)

About the show

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• Michael: @[email protected]) / @mkennedy.codes) (bsky)

• Brian: @[email protected]) / @brianokken.bsky.social)

• Show: @[email protected]) / @pythonbytes.fm) (bsky)

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Brian #1: Making PyPI’s test suite 81% faster)

• Alexis Challande

• The PyPI backend is a project called Warehouse

• It’s tested with pytest, and it’s a large project, thousands of tests.

• Steps for speedup

• Parallelizing test execution with pytest-xdist

• 67% time reduction

• --numprocesses=auto allows for using all cores

• DB isolation - cool example of how to config postgress to give each test worker it’s on db

• They used pytest-sugar to help with visualization, as xdist defaults to quite terse output

• Use Python 3.12’s sys.monitoring to speed up coverage instrumentation

• 53% time reduction

• Nice example of using COVERAGE_CORE=sysmon

• Optimize test discovery

• Always use testpaths

• Sped up collection time. 66% reduction (collection was 10% of time)

• Not a huge savings, but it’s 1 line of config

• Eliminate unnecessary imports

• Use python -X importtime

• Examine dependencies not used in testing.

• Their example: ddtrace

• A tool they use in production, but it also has a couple pytest plugins included

• Those plugins caused ddtrace to get imported

• Using -p:no ddtrace turns off the plugin bits

• Notes from Brian:

• I often get questions about if pytest is useful for large projects.

• Short answer: Yes!

• Longer answer: But you’ll probably want to speed it up

• I need to extend this article with a general purpose “speeding up pytest” post or series.

• -p:no can also be used to turn off any plugin, even builtin ones.

• Examples include

• nice to have developer focused pytest plugins that may not be necessary in CI

• CI reporting plugins that aren’t needed by devs running tests locally

Michael #2: People aren’t talking enough about how most of OpenAI’s tech stack runs on Python)

• Original article: Building, launching, and scaling ChatGPT Images)

• Tech stack: The technology choices behind the product are surprisingly simple; dare I say, pragmatic!

• Python: most of the product’s code is written in this language.

• FastAPI): the Python framework used for building APIs quickly, using standard Python type hints. As the name suggests, FastAPI’s strength is that it takes less effort to create functional, production-ready APIs to be consumed by other services.

• C: for parts of the code that need to be highly optimized, the team uses the lower-level C programming language

• Temporal): used for asynchronous workflows and operations inside OpenAI. Temporal is a neat workflow solution that makes multi-step workflows reliable even when individual steps crash, without much effort by developers. It’s particularly useful for longer-running workflows like image generation at scale

Michael #3: PyCon Talks on YouTube)

• Some talks that jumped out to me:

• Keynote by Cory Doctorow)

• 503 days working full-time on FOSS: lessons learned)

• Going From Notebooks to Scalable Systems)

• And my Talk Python conversation) around it. (edited episode pending)

• Unlearning SQL)

• The Most Bizarre Software Bugs in History )

• The PyArrow revolution in Pandas)

• And my Talk Python episode) about it.

• What they don't tell you about building a JIT compiler for CPython)

• And my Talk Python conversation) around it (edited episode pending)

• Design Pressure: The Invisible Hand That Shapes Your Code )

• Marimo: A Notebook that) )"Compiles") Python for Reproducibility and Reusability)

• And my Talk Python episode) about it.

• GPU Programming in Pure Python )

• And my Talk Python conversation) around it (edited episode pending)

• Scaling the Mountain: A Framework for Tackling Large-Scale Tech Debt)

Brian #4: Optimizing Python Import Performance)

• Mostly pay attention to #'s 1-3

• This is related to speeding up a test suite, speeding up necessary imports.

• Finding what’s slow

• Use python -X importtime <the reset of the command

• Ex: python -X importtime ptyest

• Techniques

• Lazy imports

• move slow-to-import imports into functions/methods

• Avoiding circular imports

• hopefully you’re doing that already

• Optimize init.py files

• Avoid unnecessary imports, heavy computations, complex logic

• Notes from Brian

• Some questions remain open for me

• Does module aliasing really help much?

• This applies to testing in a big way

• Test collection imports your test suite, so anything imported at the top level of a file gets imported at test collection time, even if you only are running a subset of tests using filtering like -x or -m or other filter methods.

• Run -X importtime on test collection.

• Move slow imports into fixtures, so they get imported when needed, but NOT at collection.

• See also:

• option) )-X) in the standard docs)

• Consider using import_profile)

Extras

Brian:

• PEPs & Co.)

• PEP is a ‘backronym”, an acronym where the words it stands for are filled in after the acronym is chosen. Barry Warsaw made this one up.

• There are a lot of “enhancement proposal” and “improvement proposal” acronyms now from other communities

• pythontest.com) has a new theme

• More colorful. Neat search feature

• Now it’s excruciatingly obvious that I haven’t blogged regularly in a while

• I gotta get on that

• Code highlighting might need tweaked for dark mode

Michael:

• git-bug)

• Pyrefly follow up)

Joke: There is hope).