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ci/official/containers/linux_arm64/setup.sources.sh
# # setup.python.sh: Install a specific Python version and packages for it. # Usage: setup.python.sh <pyversion> <requirements.txt> # Sets up custom apt sources for our TF images. # Prevent apt install tzinfo from asking our location (assumes UTC) export DEBIAN_FRONTEND=noninteractive # Set up shared custom sources apt-get update apt-get install -y gnupg ca-certificates
Shell Script - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Sep 18 14:52:45 GMT 2023 - 1.7K bytes - Viewed (0) -
.github/workflows/update-rbe.yml
# the end of the line. # See https://cloud.google.com/architecture/using-container-images#exploring_image_manifests_digests_and_tags echo -n "Trying to map name $1 to tag $2... " digest=$(curl -s --head "https://gcr.io/v2/tensorflow-sigs/build/manifests/$2" | egrep -o "sha256:[[:alnum:]]*") # Find the line matching the regex "sigbuild-r2.9" (with quotes) and
Others - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Wed Apr 10 15:40:34 GMT 2024 - 7.2K bytes - Viewed (0) -
ci/official/envs/macos_arm64
TFCI_MACOS_BAZEL_TEST_DIR_PATH="/Volumes/BuildData/bazel_output" TFCI_OUTPUT_DIR=build_output TFCI_WHL_BAZEL_TEST_ENABLE=1 TFCI_WHL_SIZE_LIMIT=240M TFCI_WHL_SIZE_LIMIT_ENABLE=1 # 3.11 is the system python on our images case $TFCI_PYTHON_VERSION in 3.11) TFCI_MACOS_PYENV_INSTALL_ENABLE=0 ;; *) TFCI_MACOS_PYENV_INSTALL_ENABLE=1 ;;
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ci/official/utilities/setup_macos.sh
pyenv install -s "$TFCI_PYTHON_VERSION" pyenv local "$TFCI_PYTHON_VERSION" # Do a sanity check to make sure that we using the correct Python version python --version fi # TFCI Mac VM images do not have twine installed by default so we need to # install it manually. We use Twine in nightly builds to upload Python packages # to PyPI. if [[ "$TFCI_MACOS_TWINE_INSTALL_ENABLE" == 1 ]]; then pip install twine==3.6.0
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ci/official/containers/linux_arm64/build.sh
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ci/official/envs/macos_x86
TFCI_MACOS_TWINE_INSTALL_ENABLE=1 TFCI_OUTPUT_DIR=build_output TFCI_WHL_BAZEL_TEST_ENABLE=1 TFCI_WHL_SIZE_LIMIT=255M TFCI_WHL_SIZE_LIMIT_ENABLE=1 # 3.11 is the system python on our images case $TFCI_PYTHON_VERSION in 3.11) TFCI_MACOS_PYENV_INSTALL_ENABLE=0 TFCI_MACOS_UPGRADE_PYENV_ENABLE=0 ;; *) TFCI_MACOS_PYENV_INSTALL_ENABLE=1 TFCI_MACOS_UPGRADE_PYENV_ENABLE=1 ;;
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SECURITY.md
running the `tf.train.Server`. ## Untrusted inputs during training and prediction TensorFlow supports a wide range of input data formats. For example it can process images, audio, videos, and text. There are several modules specialized in taking those formats, modifying them, and/or converting them to intermediate formats that can be processed by TensorFlow.
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# different compilation methods. E.g. for a PR to test a new CUDA version, set # the CACHEBUSTER to the PR number. build --action_env=CACHEBUSTER=20220325 # Use Python 3.X as installed in container image build --action_env PYTHON_BIN_PATH="/usr/local/bin/python3" build --python_path="/usr/local/bin/python3" # Build TensorFlow v2 build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
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ci/official/containers/linux_arm64/setup.python.sh
/usr/bin/$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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