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ci/official/utilities/setup_docker.sh
# Pulling an already-pulled container image will finish instantly, so # repeating the command costs nothing. docker pull "$TFCI_DOCKER_IMAGE" || sleep 15 docker pull "$TFCI_DOCKER_IMAGE" || sleep 15 docker pull "$TFCI_DOCKER_IMAGE" fi if [[ "$TFCI_DOCKER_REBUILD_ENABLE" == 1 ]]; then DOCKER_BUILDKIT=1 docker build --cache-from "$TFCI_DOCKER_IMAGE" -t "$TFCI_DOCKER_IMAGE" $TFCI_DOCKER_REBUILD_ARGS
Shell Script - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Jan 26 15:27:59 GMT 2024 - 1.8K bytes - Viewed (0) -
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/envs/linux_arm64
# to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow" # 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"
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.github/workflows/update-rbe.yml
- name: Update the RBE Configs run: | function map() { # The "digest" that allows us to pull an image is not the digest as # returned by the API, but a sha256sum of the entire chunk of image # metadata. gcr.io helpfully includes it in the header of the response # as docker-content-digest: sha256:[digest]. Note we use egrep to
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.github/workflows/arm-ci-extended-cpp.yml
echo "No running containers"; else echo "Running container(s) found" && \ docker stop $running_containers; fi docker container prune -f docker image prune -af - name: Clean repository shell: bash run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
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.github/workflows/arm-ci-extended.yml
echo "No running containers"; else echo "Running container(s) found" && \ docker stop $running_containers; fi docker container prune -f docker image prune -af - name: Clean repository shell: bash run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
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RELEASE.md
ops. * Add `tf.image.psnr`, `tf.image.ssim`, `tf.image.ssim_multiscale`, `tf.image.image_gradients`, `tf.image.sobel_edges`. * Add links to https://js.tensorflow.org. * Fix non-uniformity of orthogonal matrices. * Fix bug where multi-image Estimator eval summaries were not displayed correctly.
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ci/official/utilities/setup_macos.sh
# those VMs does not support installing Python 3.12 and above which we need # for running smoke tests in nightly/release wheel builds. if [[ "${TFCI_MACOS_UPGRADE_PYENV_ENABLE}" == 1 ]]; then # The TFCI Mac VM image seems to have uncommitted local changes to the Pyenv # repository so we have to discard them and reset the working directory before # we can pull in the latest changes.
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ci/official/envs/linux_x86
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cpu TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow_cpu" TFCI_DOCKER_ENABLE=1 TFCI_DOCKER_IMAGE=tensorflow/build:2.16-python${TFCI_PYTHON_VERSION} TFCI_DOCKER_PULL_ENABLE=1 TFCI_DOCKER_REBUILD_ARGS="--build-arg PYTHON_VERSION=python$TFCI_PYTHON_VERSION --target=devel tensorflow/tools/tf_sig_build_dockerfiles"
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CONTRIBUTING.md
to fix this. - Once the internal tests pass, we go ahead and merge the code internally as well as externally on GitHub. In a graphical form, the entire lifetime of a PR looks like ![image](https://github.com/tensorflow/tensorflow/assets/52792999/3eea4ca5-daa0-4570-b0b5-2a2b03a724a3) ### Contributor License Agreements
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