Exploring OpenAI's Tech Stack: The Role of Python and More

• 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

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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.