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ci/official/envs/linux_arm64
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 TFCI_WHL_AUDIT_ENABLE=1 TFCI_WHL_AUDIT_PLAT=manylinux2014_aarch64 TFCI_WHL_BAZEL_TEST_ENABLE=1 TFCI_WHL_SIZE_LIMIT=240M
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ci/official/envs/linux_x86
TFCI_INDEX_HTML_ENABLE=1 TFCI_LIB_SUFFIX="-cpu-linux-x86_64" TFCI_OUTPUT_DIR=build_output TFCI_WHL_AUDIT_ENABLE=1 TFCI_WHL_AUDIT_PLAT=manylinux2014_x86_64 TFCI_WHL_BAZEL_TEST_ENABLE=1 TFCI_WHL_SIZE_LIMIT=240M
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ci/official/envs/ci_default
TFCI_MACOS_UPGRADE_PYENV_ENABLE= TFCI_NIGHTLY_UPDATE_VERSION_ENABLE= TFCI_NVIDIA_SMI_ENABLE= TFCI_OUTPUT_DIR= TFCI_PYCPP_SWAP_TO_BUILD_ENABLE= TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS= TFCI_PYTHON_VERSION= TFCI_WHL_AUDIT_ENABLE= TFCI_WHL_AUDIT_PLAT= TFCI_WHL_BAZEL_TEST_ENABLE= TFCI_WHL_IMPORT_TEST_ENABLE=1 TFCI_WHL_SIZE_LIMIT=
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ci/official/containers/linux_arm64/devel.usertools/wheel_verification.bats
} teardown_file() { rm -rf /tf/venv } @test "Wheel is manylinux2014 (manylinux_2_17) compliant" { python3 -m auditwheel show "$TF_WHEEL" > audit.txt grep --quiet -zoP 'is consistent with the following platform tag:\n"manylinux_2_17_(aarch|x86_)64"\.' audit.txt } @test "Wheel conforms to upstream size limitations" { WHEEL_MEGABYTES=$(stat --format %s "$TF_WHEEL" | awk '{print int($1/(1024*1024))}')
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ci/official/utilities/rename_and_verify_wheels.sh
ls -t *.whl | tail -n +2 | xargs mv -t $TFCI_OUTPUT_DIR/extra_wheels fi # Repair wheels with auditwheel and delete the old one. if [[ "$TFCI_WHL_AUDIT_ENABLE" == "1" ]]; then python3 -m auditwheel repair --plat "$TFCI_WHL_AUDIT_PLAT" --wheel-dir . *.whl # if the wheel is already named correctly, auditwheel won't rename it. so we # list all .whl files by their modification time (ls -t) and delete anything
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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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RELEASE.md
`tf.keras.layers.EinsumDense`. * Added `tf.keras.utils.audio_dataset_from_directory` utility to easily generate audio classification datasets from directories of `.wav` files. * Added `subset="both"` support in `tf.keras.utils.image_dataset_from_directory`,`tf.keras.utils.text_dataset_from_directory`, and `audio_dataset_from_directory`, to be used with the
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