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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/README.md
# container and start fresh, run "docker rm -f tf". Removing the container # destroys some temporary bazel data and causes longer builds. # # You will need the NVIDIA Container Toolkit for GPU testing: # https://github.com/NVIDIA/nvidia-container-toolkit # # Note: if you interrupt a bazel command on docker (ctrl-c), you # will need to run `docker exec tf pkill bazel` to quit bazel. #
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.github/workflows/arm-ci-extended.yml
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.github/workflows/sigbuild-docker.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: ${{ secrets.DOCKERHUB_TOKEN }}
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ci/official/containers/linux_arm64/devel.bashrc
# Do not print anything if this is not being used interactively [ -z "$PS1" ] && return # Set up attractive prompt export PS1="\[\e[31m\]tf-docker\[\e[m\] \[\e[33m\]\w\[\e[m\] > " export TERM=xterm-256color alias grep="grep --color=auto" alias ls="ls --color=auto" # Fix nvidia-docker
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.github/workflows/update-rbe.yml
# ============================================================================== # This Workflow updates tensorflow/tools/toolchains/remote_config/configs.bzl # to reference the most recent versions of the SIG Build Docker images. name: Update RBE Configs on: workflow_dispatch: permissions: contents: read jobs: rbe: name: Update RBE Configs runs-on: ubuntu-latest
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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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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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