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33 | 33 | # are not published for them. |
34 | 34 | DISABLED_PYTHON_VERSIONS: List[str] = ["3.13t", "3.14", "3.14t", "3.15", "3.15t"] |
35 | 35 |
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36 | | -# CUDA versions to publish. cu130 first because it is the generator's stable choice, and |
37 | | -# cu126 because it is the floor a consumer pairing with an older PyTorch needs. |
38 | | -SUPPORTED_CUDA_VERSIONS: List[str] = ["cu126", "cu130"] |
| 36 | +# CUDA versions to publish. |
| 37 | +# |
| 38 | +# Chosen so that every consumer row can find a matching wheel rather than by what is |
| 39 | +# convenient to verify. A delegate built against one of these has to be able to depend on an |
| 40 | +# ExecuTorch wheel for the same CUDA version, and a missing version means that consumer has |
| 41 | +# nothing to depend on: |
| 42 | +# |
| 43 | +# cu126 the floor, and what Jetson devices are limited to |
| 44 | +# cu130 the generator's stable choice, and the default for accelerator consumers |
| 45 | +# cu132 the newest, which consumers building against a current TensorRT need |
| 46 | +# |
| 47 | +# cu132 is included even though no machine here can execute it, because omitting it would |
| 48 | +# leave a published consumer row with no ExecuTorch wheel to pair with. The packaging |
| 49 | +# properties are checked on every row; executing a model is a release-gate step on hardware |
| 50 | +# that has the matching GPU. |
| 51 | +SUPPORTED_CUDA_VERSIONS: List[str] = ["cu126", "cu130", "cu132"] |
39 | 52 |
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40 | 53 | # The single row built for a pull request. A full matrix on every push would cost hours for |
41 | 54 | # little signal, and this pair is the one with a machine that can run a model on it. |
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