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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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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
# ============================================================================== setup_file() { cd /tf/tensorflow bazel version # Start the bazel server } # Do a bazel query specifically for the licenses checker. It searches for # targets matching the provided query, which start with // or @ but not # //tensorflow (so it looks for //third_party, //external, etc.), and then
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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: ${{ secrets.DOCKERHUB_TOKEN }} -
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tensorflow/c/eager/gradient_checker.cc
See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #include "tensorflow/c/eager/gradient_checker.h" #include <memory> #include "absl/types/span.h" #include "tensorflow/c/eager/abstract_tensor_handle.h" #include "tensorflow/c/experimental/ops/math_ops.h" #include "tensorflow/c/tf_tensor.h"
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ci/official/containers/linux_arm64/build.sh
# almost all of the same cache layers export DOCKER_BUILDKIT=1 for target in jax tf; do IMAGE="gcr.io/tensorflow-sigs/build-arm64:$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 $(seq 1 5) do docker build \
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ci/official/envs/ci_default
TFCI_ARTIFACT_STAGING_GCS_URI= TFCI_BAZEL_BAZELRC_ARGS= TFCI_BAZEL_COMMON_ARGS= TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX= TFCI_BUILD_PIP_PACKAGE_ARGS= TFCI_DOCKER_ARGS= TFCI_DOCKER_ENABLE= TFCI_DOCKER_IMAGE= TFCI_DOCKER_PULL_ENABLE= TFCI_DOCKER_REBUILD_ARGS= TFCI_DOCKER_REBUILD_ENABLE= TFCI_DOCKER_REBUILD_UPLOAD_ENABLE= TFCI_GIT_DIR= TFCI_INDEX_HTML_ENABLE= TFCI_LIB_SUFFIX= TFCI_MACOS_BAZEL_TEST_DIR_ENABLE= TFCI_MACOS_BAZEL_TEST_DIR_PATH=
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ci/official/utilities/setup.sh
# complex. Write a well-documented script under utilities/ to encapsulate the # functionality instead. tfrun() { "$@"; } # Run all "tfrun" commands under Docker. See setup_docker.sh for details if [[ "$TFCI_DOCKER_ENABLE" == 1 ]]; then source ./ci/official/utilities/setup_docker.sh fi # Generate an overview page describing the build if [[ "$TFCI_INDEX_HTML_ENABLE" == 1 ]]; then
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