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.github/workflows/arm-ci.yml
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ci/official/envs/rbe
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ci/official/envs/linux_x86_cuda
TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_gpu_linux" TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cuda TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ARGS="--gpus all" TFCI_LIB_SUFFIX="-gpu-linux-x86_64"
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ci/official/utilities/code_check_changed_files.bats
# ============================================================================== setup_file() { bazel version # Start the bazel server # Fixes "fatal: detected dubious ownership in repository" for Docker. git config --system --add safe.directory '*' git config --system protocol.file.allow always # Note that you could generate a list of all the affected targets with e.g.:
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.github/workflows/arm-ci-extended-cpp.yml
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ci/official/envs/linux_arm64
# despite lacking Nvidia CUDA support. TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ENABLE=1 TFCI_DOCKER_IMAGE=gcr.io/tensorflow-sigs/build-arm64:tf-2-16-multi-python TFCI_DOCKER_PULL_ENABLE=1 TFCI_DOCKER_REBUILD_ARGS="--target=tf ci/official/containers/linux_arm64" TFCI_INDEX_HTML_ENABLE=1 TFCI_LIB_SUFFIX="-cpu-linux-arm64" TFCI_OUTPUT_DIR=build_output
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.github/workflows/arm-cd.yml
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ci/official/containers/linux_arm64/cuda.packages.txt
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation libcudnn8=8.9.6.50-1+cuda12.2 libcudnn8-dev=8.9.6.50-1+cuda12.2 # This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image. # For now it ensures that we install at least version 12.3.107 of PTXAS, # since 12.3.103 has a bug.
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.github/workflows/sigbuild-docker-branch.yml
- name: Set up Docker Buildx uses: docker/setup-buildx-action@8c0edbc76e98fa90f69d9a2c020dcb50019dc325 # v2.2.1 - name: Login to DockerHub uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a # v2.1.0 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_TOKEN }} -
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ci/official/containers/linux_arm64/build.sh
# almost all of the same cache layers export DOCKER_BUILDKIT=1 for target in jax tf; do IMAGE="gcr.io/tensorflow-sigs/build-arm64:$target-$TAG" docker pull "$IMAGE" || true # Due to some flakiness of resources pulled in the build, allow the docker # command to reattempt build a few times in the case of failure (b/302558736) set +e for i in $(seq 1 5) do docker build \
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