June 02, 2025

#434 Most of OpenAI’s tech stack runs on Python

29 minutes

Topics covered in this episode:

Making PyPI’s test suite 81% faster

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

Sponsored by Digital Ocean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit

Connect with the hosts

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

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

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

Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too.

Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it.

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

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.

#434 Most of OpenAI’s tech stack runs on Python

29 minutes

Topics covered in this episode:

Making PyPI’s test suite 81% faster

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

Sponsored by Digital Ocean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit

Connect with the hosts

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

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

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

Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too.

Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it.

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.

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

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

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

Run -X importtime on test collection.

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

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#434 Most of OpenAI’s tech stack runs on Python

#434 Most of OpenAI’s tech stack runs on Python

More shows like Python Bytes

Software Engineering Radio - the podcast for professional software developers

The Changelog: Software Development, Open Source

Data Skeptic

Talk Python To Me

Software Engineering Daily

Test & Code

Super Data Science: ML & AI Podcast with Jon Krohn

Data Engineering Podcast

Syntax - Tasty Web Development Treats

CoRecursive: Coding Stories

DataFramed

Practical AI

The Stack Overflow Podcast

The Real Python Podcast

Oxide and Friends