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[GH-ISSUE #520] Please publish an official build to pypi #441

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opened 2026-10-31 21:14:58 +00:00 by bsakaguchi · 1 comment
bsakaguchi commented 2026-10-31 21:14:58 +00:00
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Originally created by @tplass-ias on GitHub (Nov 18, 2025).
Original GitHub issue: https://github.com/openai/CLIP/issues/520

Hey folks,

I have some users impacted by a GitHub outage because this repo does not publish to pypi or push git tags or GitHub releases, which we have cached in a corporate proxy for this type of situation

To ensure developers keep using your official python implementation, please distribute it via one of these channels

Thanks!

Originally created by @tplass-ias on GitHub (Nov 18, 2025). Original GitHub issue: https://github.com/openai/CLIP/issues/520 Hey folks, I have some users impacted by a GitHub outage because this repo does not publish to pypi or push git tags or GitHub releases, which we have cached in a corporate proxy for this type of situation To ensure developers keep using your official python implementation, please distribute it via one of these channels Thanks!
bsakaguchi commented 2026-10-31 21:14:58 +00:00
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@rehan243 commented on GitHub (Jun 5, 2026):

Testing ML systems requires different approaches than traditional software. What we do:

  • Property-based testing: Instead of exact output assertions, we test properties
    (e.g., "output is valid JSON", "confidence is between 0 and 1")
  • Golden datasets: Curated test sets with known-good answers for regression testing
  • Behavioral testing: Inspired by CheckList — we test for specific capabilities,
    not just accuracy on a test set
  • Integration tests: End-to-end pipeline tests run nightly to catch environment drift

The ROI on ML testing is enormous — it catches issues that unit tests never would.

<!-- gh-comment-id:4633190651 --> @rehan243 commented on GitHub (Jun 5, 2026): Testing ML systems requires different approaches than traditional software. What we do: - **Property-based testing**: Instead of exact output assertions, we test properties (e.g., "output is valid JSON", "confidence is between 0 and 1") - **Golden datasets**: Curated test sets with known-good answers for regression testing - **Behavioral testing**: Inspired by CheckList — we test for specific capabilities, not just accuracy on a test set - **Integration tests**: End-to-end pipeline tests run nightly to catch environment drift The ROI on ML testing is enormous — it catches issues that unit tests never would.
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