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mergennachinclaude
mergennachin
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claude
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Move torch pin from the 2.11 to the 2026-04-09 nightly, and drop deprecated CUDA versions from CI (#19072)
- Move torch pin from the 2.11 to the 2026-04-09 nightly - Drop 12.8 and 12.9 from CI; now builds 12.6 and 13.0 only. Windows only 12.6 for now. --------- Co-authored-by: Claude <noreply@anthropic.com>
1 parent 32a6cec commit d7f8718

15 files changed

Lines changed: 135 additions & 63 deletions

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‎.ci/docker/build.sh‎

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@@ -81,7 +81,7 @@ case "${IMAGE_NAME}" in
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LINTRUNNER=""
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GCC_VERSION=11
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CUDA_WINDOWS_CROSS_COMPILE=yes
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CUDA_VERSION=12.8
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CUDA_VERSION=12.6
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SKIP_PYTORCH=yes
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;;
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*)

‎.ci/docker/ci_commit_pins/pytorch.txt‎

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@@ -1 +1 @@
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release/2.11
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358117c166b75167a09bca81ac9925940feda339

‎.ci/docker/common/install_cuda_windows_cross_compile.sh‎

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@@ -11,12 +11,13 @@ set -ex
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INSTALL_DIR="${WINDOWS_CUDA_INSTALL_DIR:-/opt/cuda-windows}"
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# Mapping of CUDA versions to their corresponding driver versions for Windows installers
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# Mapping of CUDA versions to their corresponding driver versions for Windows installers.
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# Source: https://developer.nvidia.com/cuda-toolkit-archive
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# Format: "PATCH_VERSION:DRIVER_VERSION". Starting with CUDA 13.0, NVIDIA dropped the
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# driver suffix from the Windows installer filename, so the driver field is empty.
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declare -A CUDA_DRIVER_MAP=(
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["12.6"]="12.6.3:561.17"
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["12.8"]="12.8.1:572.61"
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["12.9"]="12.9.1:576.57"
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["13.0"]="13.0.3:"
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)
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install_mingw() {
@@ -83,7 +84,8 @@ install_windows_cuda() {
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mkdir -p "${INSTALL_DIR}"
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cd "${INSTALL_DIR}"
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CUDA_INSTALLER="cuda_${CUDA_VERSION}_${CUDA_DRIVER_VERSION}_windows.exe"
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# CUDA 13.0+ installers no longer include the driver version in the filename.
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CUDA_INSTALLER="cuda_${CUDA_VERSION}${CUDA_DRIVER_VERSION:+_${CUDA_DRIVER_VERSION}}_windows.exe"
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CUDA_URL="https://developer.download.nvidia.com/compute/cuda/${CUDA_VERSION}/local_installers/${CUDA_INSTALLER}"
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# Check if already downloaded and extracted

‎.ci/docker/common/install_pytorch.sh‎

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@@ -27,14 +27,19 @@ install_pytorch_and_domains() {
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chown -R ci-user .
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export _GLIBCXX_USE_CXX11_ABI=1
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# PyTorch's FindARM.cmake hard-fails when the SVE+BF16 compile probe
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# doesn't pass — gcc-11 in this image is too old to accept the combined
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# NEON/SVE/bfloat16 intrinsics the probe exercises. Executorch's aarch64
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# runtime targets (phones, embedded) don't use SVE, so bypass the check.
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export BUILD_IGNORE_SVE_UNAVAILABLE=1
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# Then build and install PyTorch
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conda_run python setup.py bdist_wheel
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pip_install "$(echo dist/*.whl)"
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# Grab the pinned audio and vision commits from PyTorch
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TORCHAUDIO_VERSION=release/2.11
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TORCHAUDIO_VERSION=$(cat .github/ci_commit_pins/audio.txt)
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export TORCHAUDIO_VERSION
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TORCHVISION_VERSION=release/0.26
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TORCHVISION_VERSION=$(cat .github/ci_commit_pins/vision.txt)
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export TORCHVISION_VERSION
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install_domains

‎.ci/scripts/test_model_e2e.sh‎

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@@ -260,7 +260,7 @@ if [ "$AUDIO_URL" != "" ]; then
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elif [[ "$MODEL_NAME" == *whisper* ]] || [ "$MODEL_NAME" = "voxtral_realtime" ]; then
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conda install -y -c conda-forge "ffmpeg<8"
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pip install datasets soundfile
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pip install torchcodec==0.11.0 --extra-index-url https://download.pytorch.org/whl/test/cpu
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pip install torchcodec==0.12.0.dev20260409 --extra-index-url https://download.pytorch.org/whl/nightly/cpu
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python -c "from datasets import load_dataset;import soundfile as sf;sample = load_dataset('distil-whisper/librispeech_long', 'clean', split='validation')[0]['audio'];sf.write('${MODEL_DIR}/$AUDIO_FILE', sample['array'][:sample['sampling_rate']*30], sample['sampling_rate'])"
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fi
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‎.ci/scripts/test_wheel_package_qnn.sh‎

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@@ -158,17 +158,17 @@ print(module_vars["TORCH_VERSION"])
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PY
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)
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# NIGHTLY_VERSION=$(
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# "$PYBIN" - <<'PY'
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# import runpy
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# module_vars = runpy.run_path("torch_pin.py")
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# print(module_vars["NIGHTLY_VERSION"])
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# PY
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# )
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echo "=== [$LABEL] Install torch==${TORCH_VERSION} ==="
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# Install torch based on the pinned PyTorch version, preferring the PyTorch test index
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"$PIPBIN" install torch=="${TORCH_VERSION}" --extra-index-url "https://download.pytorch.org/whl/test"
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NIGHTLY_VERSION=$(
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"$PYBIN" - <<'PY'
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import runpy
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module_vars = runpy.run_path("torch_pin.py")
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print(module_vars["NIGHTLY_VERSION"])
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PY
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)
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echo "=== [$LABEL] Install torch==${TORCH_VERSION}.${NIGHTLY_VERSION} ==="
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# Install torchao based on the pinned PyTorch version
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"$PIPBIN" install torch=="${TORCH_VERSION}.${NIGHTLY_VERSION}" --index-url "https://download.pytorch.org/whl/nightly/cpu"
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"$PIPBIN" install wheel
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# Install torchao based on the pinned commit from third-party/ao submodule

‎.ci/scripts/utils.sh‎

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@@ -53,7 +53,7 @@ dedupe_macos_loader_path_rpaths() {
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pushd ..
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torch_lib_dir=$(python -c "import importlib.util; print(importlib.util.find_spec('torch').submodule_search_locations[0])")/lib
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popd
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if [[ -z "${torch_lib_dir}" || ! -d "${torch_lib_dir}" ]]; then
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return
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fi
@@ -141,9 +141,9 @@ install_pytorch_and_domains() {
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dedupe_macos_loader_path_rpaths
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# Grab the pinned audio and vision commits from PyTorch
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TORCHAUDIO_VERSION=release/2.11
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TORCHAUDIO_VERSION=$(cat .github/ci_commit_pins/audio.txt)
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export TORCHAUDIO_VERSION
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TORCHVISION_VERSION=release/0.26
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TORCHVISION_VERSION=$(cat .github/ci_commit_pins/vision.txt)
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export TORCHVISION_VERSION
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install_domains

‎.github/workflows/cuda-windows.yml‎

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@@ -64,7 +64,7 @@ jobs:
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secrets-env: EXECUTORCH_HF_TOKEN
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runner: linux.g5.4xlarge.nvidia.gpu
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gpu-arch-type: cuda
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gpu-arch-version: 12.8
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gpu-arch-version: 12.6
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docker-image: ci-image:executorch-ubuntu-22.04-cuda-windows
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submodules: recursive
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upload-artifact: ${{ matrix.model_repo }}-${{ matrix.model_name }}-cuda-windows-${{ matrix.quant }}
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timeout: 240
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runner: windows.g5.4xlarge.nvidia.gpu
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gpu-arch-type: cuda
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gpu-arch-version: 12.8
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gpu-arch-version: 12.6
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download-artifact: ${{ matrix.model_repo }}-${{ matrix.model_name }}-cuda-windows-${{ matrix.quant }}
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ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
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script: |
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\$ErrorActionPreference = 'Stop'
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\$PSNativeCommandUseErrorActionPreference = \$true
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\$env:CUDA_HOME = 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.8'
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\$env:CUDA_HOME = 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.6'
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\$env:CUDA_PATH = \$env:CUDA_HOME
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\$env:PATH = \"\$env:CUDA_HOME\bin;\$env:PATH\"
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nvcc --version
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throw 'RUNNER_ARTIFACT_DIR is empty. Ensure download-artifact is configured for windows_job.yml.'
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}
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.ci/scripts/test_model_e2e_windows.ps1 -Device cuda-windows -HfModel '${{ matrix.model_repo }}/${{ matrix.model_name }}' -QuantName '${{ matrix.quant }}' -ModelDir \$artifactDir -ExpectedCudaVersion '12.8'
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.ci/scripts/test_model_e2e_windows.ps1 -Device cuda-windows -HfModel '${{ matrix.model_repo }}/${{ matrix.model_name }}' -QuantName '${{ matrix.quant }}' -ModelDir \$artifactDir -ExpectedCudaVersion '12.6'
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}"

‎.github/workflows/cuda.yml‎

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# Test ExecuTorch CUDA Build Compatibility
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# This workflow tests whether ExecuTorch can be successfully built with CUDA support
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# across different CUDA versions (12.6, 12.8, 12.9, 13.0) using the command:
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# across different CUDA versions (12.6, 13.0) using the command:
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# ./install_executorch.sh
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#
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# Note: ExecuTorch automatically detects the system CUDA version using nvcc and
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strategy:
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fail-fast: false
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matrix:
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cuda-version: ["12.6", "12.8", "12.9", "13.0"]
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cuda-version: ["12.6", "13.0"]
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name: test-executorch-cuda-build-${{ matrix.cuda-version }}
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uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
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echo "CUDA build results: ${{ needs.test-cuda-builds.result }}"
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exit 1
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else
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echo "SUCCESS: All ExecuTorch CUDA builds (12.6, 12.8, 12.9, 13.0) completed successfully!"
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echo "SUCCESS: All ExecuTorch CUDA builds (12.6, 13.0) completed successfully!"
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fi
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test-models-cuda:

‎.github/workflows/docker-builds.yml‎

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matrix:
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runner: [linux.4xlarge]
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docker-image-name: [
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executorch-ubuntu-22.04-gcc11,
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executorch-ubuntu-22.04-gcc9-nopytorch,
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executorch-ubuntu-22.04-clang12,
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executorch-ubuntu-22.04-linter,
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executorch-ubuntu-22.04-arm-sdk,
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executorch-ubuntu-22.04-zephyr-sdk,
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executorch-ubuntu-22.04-qnn-sdk,
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executorch-ubuntu-22.04-mediatek-sdk,
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executorch-ubuntu-22.04-clang12-android
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]
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include:
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# PyTorch is built from source in these images; 4xlarge OOMs mid-build.
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- docker-image-name: executorch-ubuntu-22.04-gcc11
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runner: linux.12xlarge
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- docker-image-name: executorch-ubuntu-22.04-zephyr-sdk
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runner: linux.12xlarge
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- docker-image-name: executorch-ubuntu-22.04-gcc11-aarch64
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runner: linux.arm64.2xlarge
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- docker-image-name: executorch-ubuntu-22.04-gcc11-aarch64-android

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