fix: sort compatibility matrices for deterministic output#3006
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The compatibility matrix update workflow was opening PRs even when the underlying data hadn't changed because the output was not idempotent. - CUDA was already sorted correctly - PyTorch was non-deterministic because fetchCurrentTorchVersions iterates over a Go map with random iteration order - TensorFlow was non-deterministic because it iterates over HTML table rows in document order Add stable sorting to both PyTorch and TensorFlow generators before serializing the JSON output, plus tests verifying deterministic sorting. Fixes non-idempotent compatibility matrix PRs.
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The tests pass and there are no linting issues. This is a well-crafted PR that:
LGTM |
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May 7, 2026
…icate#3009) torchGPUPackage picks the best match by CUDA version but never compared torch versions when CUDA was equal -- so iteration order determined which patch won. After replicate#3006 sorted the matrix ascending, torch==2.3 started resolving to 2.3.0 instead of 2.3.1. Break ties by preferring the higher torch patch version, consistent with baseImageComponentNormalisation which already does this.
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Fixes the compatibility matrix update workflow opening PRs even when the underlying data has not changed.
Problem
The scheduled update-compatibility-matrices workflow was creating PRs on days when no new CUDA/PyTorch/TensorFlow versions were released. This happened because the generated JSON was not deterministic.
Root Cause
Changes
This should make the workflow idempotent: if the upstream data has not changed, the generated JSON will be byte-for-byte identical and no PR will be opened.