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README.md
<div align="center"> <img src="https://www.tensorflow.org/images/tf_logo_horizontal.png"> </div> [![Python](https://img.shields.io/pypi/pyversions/tensorflow.svg)](https://badge.fury.io/py/tensorflow) [![PyPI](https://badge.fury.io/py/tensorflow.svg)](https://badge.fury.io/py/tensorflow) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4724125.svg)](https://doi.org/10.5281/zenodo.4724125)
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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/containers/linux_arm64/setup.python.sh
/usr/bin/$VERSION get-pip.py /usr/bin/$VERSION -m pip install --no-cache-dir --upgrade pip # Disable the cache dir to save image space, and install packages
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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/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/arm-ci-extended-cpp.yml
echo "No running containers"; else echo "Running container(s) found" && \ docker stop $running_containers; fi docker container prune -f docker image prune -af - name: Clean repository shell: bash run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# different compilation methods. E.g. for a PR to test a new CUDA version, set # the CACHEBUSTER to the PR number. build --action_env=CACHEBUSTER=20220325 # Use Python 3.X as installed in container image build --action_env PYTHON_BIN_PATH="/usr/local/bin/python3" build --python_path="/usr/local/bin/python3" # Build TensorFlow v2 build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 # Use lld as the linker
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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
_test_lib$ //tensorflow/cc/saved_model:saved_model_test_files //tensorflow/cc/saved_model:saved_model_half_plus_two //tensorflow:no_tensorflow_py_deps //tensorflow/tools/pip_package:win_pip_package_marker //tensorflow/core:image_testdata //tensorflow/core/lib/lmdb:lmdb_testdata //tensorflow/core/lib/lmdb/testdata:lmdb_testdata //tensorflow/core/kernels/cloud:bigquery_reader_ops //tensorflow/python:extra_py_tests_deps
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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
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LICENSE
liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor
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