Stop publishing CUDA 13.0 wheels

.ci/scripts/tests/test_filter_cuda_matrix.py

@@ -284,7 +284,7 @@ class TestPublishedSets(unittest.TestCase):
     """
 
     def test_published_cuda_versions(self):
-        self.assertEqual(FILTER.SUPPORTED_CUDA_VERSIONS, ["cu130", "cu132", "cu134"])
+        self.assertEqual(FILTER.SUPPORTED_CUDA_VERSIONS, ["cu132", "cu134"])
 
     def test_published_cuda_versions_are_documented(self):
         # The install table on the getting started page is the only place a user is told

.github/scripts/filter_cuda_matrix.py

@@ -46,8 +46,8 @@
 # ExecuTorch wheel for the same CUDA version, and a missing version means that consumer has
 # nothing to depend on:
 #
-#   cu130   the floor, the generator's stable choice, and the default for accelerator consumers
-#   cu132   a current TensorRT build target
+#   cu132   the floor, the generator's stable choice, the default for accelerator consumers,
+#           and a current TensorRT build target
 #   cu134   the newest, which consumers building against the latest CUDA need
 #
 # Skip wholly absent trains so an upstream removal cannot block the remaining releases.
@@ -59,11 +59,15 @@
 # builds. A machine on CUDA 12.6 can still build from source, where the pinned torch comes
 # from a channel that carries 12.6.
 #
+# cu130 is not published. PyTorch keeps it on its nightly builds only as a temporary hold for
+# other projects, and its release-candidate builds do not carry it, so a release would have
+# no cu130 train even while nightlies do.
+#
 # cu132 is included because omitting it would leave a published consumer row with no
 # ExecuTorch wheel to pair with. It is executable on a device one minor behind, since CUDA
 # minor versions are compatible, so a cu132 wheel has been run end to end on a CUDA 13.0
 # device. The packaging properties are checked on every row regardless.
-SUPPORTED_CUDA_VERSIONS: List[str] = ["cu130", "cu132", "cu134"]
+SUPPORTED_CUDA_VERSIONS: List[str] = ["cu132", "cu134"]
 
 # Python versions to publish, stated rather than derived for the same reason the CUDA
 # versions are. Deriving them from the rows that survived the filter made the release
@@ -73,15 +77,15 @@
 SUPPORTED_PYTHON_VERSIONS: List[str] = ["3.10", "3.11", "3.12", "3.13", "3.14"]
 
 # The single row built for a pull request. A full matrix on every push would cost hours for
-# little signal, and cu130 is the version with a machine on hand that can run a model on it.
+# little signal, and cu132 is the version with a machine on hand that can run a model on it.
 #
 # The python is not a free choice. When a pull request is limited, the shared generator replaces
 # the offered python list with its first entry, so that entry is the only python any row can
 # carry. Naming a different one here matched no offered row: the tiebreaker below never fired and
 # the pull request silently built whichever python the generator had left, so the constant
 # described a row that was never built.
 PR_PYTHON_VERSION: str = SUPPORTED_PYTHON_VERSIONS[0]
-PR_CUDA_VERSION: str = "cu130"
+PR_CUDA_VERSION: str = "cu132"
 
 # Jetson devices are their own row: a JetPack image, one Python version, and one CUDA
 # version. Kept empty on purpose today, so no Jetson row is emitted.

docs/source/getting-started.md

@@ -34,19 +34,19 @@ pip install executorch torch \
 | Machine you export on | Variant |
 | --- | --- |
 | CPU only | `cpu` |
-| NVIDIA GPU, CUDA 13.0 | `cu130` |
 | NVIDIA GPU, CUDA 13.2 | `cu132` |
 | NVIDIA GPU, CUDA 13.4 | `cu134` |
 
 The CUDA packages are built for Linux, on x86_64 and ARM64. Use the `cpu`
 variant on macOS and on Windows. If your CUDA version is not in the table,
 choose the closest lower one with the same major version, because CUDA works
-across minor versions but not across major ones. There is no package for CUDA
-12, so on CUDA 12 use the `cpu` variant or build from source.
+across minor versions but not across major ones. There is no package for CUDA 12,
+so on CUDA 12 use the `cpu` variant or build from source. On CUDA 13.0 or 13.1 no
+lower variant exists, so use `cu132`.
 
 To get a change that has landed on the `main` branch but is not in a release
 yet, use a nightly build. These are rebuilt every day. Put `nightly/` in front
-of the variant name, for example `nightly/cu130`, and add `--pre` to the
+of the variant name, for example `nightly/cu132`, and add `--pre` to the
 command, otherwise pip skips development versions. Nightly builds cover the same
 variants. CUDA 13.4 is the newest, and until the next release it is in nightly
 builds only.

docs/source/using-executorch-cpp.md

@@ -370,12 +370,12 @@ last of the two settings decides the tag for every entry in the link.
 
 ### Running on a GPU with the CUDA package
 
-The CUDA build is a separate package. Releases cover CUDA 13.0, 13.2 and 13.4, so pick the index
-matching the CUDA version you have (`cu130`, `cu132` or `cu134`). For CUDA 13.0:
+The CUDA build is a separate package. Releases cover CUDA 13.2 and 13.4, so pick the index
+matching the CUDA version you have (`cu132` or `cu134`). For CUDA 13.2:
 
 ```
 pip install executorch torch \
-  --index-url https://download.pytorch.org/whl/cu130 \
+  --index-url https://download.pytorch.org/whl/cu132 \
   --extra-index-url https://pypi.org/simple
 ```