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ci/official/envs/rbe
# check This script checks for such errors early if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then cat <<EOF ERROR: RBE was enabled via the 'rbe' env in the 'TFCI' variable. TFCI: $TFCI TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX is "$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX". But in .bazelrc, there is no "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX".
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ci/official/envs/linux_x86_cuda
# ============================================================================== source ci/official/envs/linux_x86 TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_gpu_linux" TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cuda TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ARGS="--gpus all" TFCI_LIB_SUFFIX="-gpu-linux-x86_64"
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code # > on the host machine. If we don't have --distinct_host_configuration=false, # > the core TensorFlow code will be built once for the host and once for the # > target platform. # See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation build --distinct_host_configuration=false # Store performance profiling log in the mounted artifact directory.
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ci/official/wheel_test/README.md
specific Python version. ##### Parameters `path_to_tensorflow_wheel`: The local path to the TensorFlow wheel file. Example: `/tmp/tensorflow-2.14.0-cp311-cp311-linux_x86_64.whl` `python_version`: The target Python version, replacing `.` with `_`. Example: For Python 3.11, use `3_11` The script performs the following steps: 1. Navigates to the `../requirements_updater` directory.
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ci/official/envs/macos_x86_cross_compile
# ============================================================================== source ci/official/envs/macos_x86 TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config cross_compile_macos_x86" TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=cross_compile_macos_x86 TFCI_MACOS_CROSS_COMPILE_ENABLE=1 TFCI_MACOS_CROSS_COMPILE_SDK_DEST="tensorflow/tools/toolchains/cross_compile/cc/MacOSX.sdk"
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WORKSPACE
hdrs = [":includes"], strip_include_prefix="site-packages/numpy/core/include/", ) """, ), } pip_parse( name = "pypi", annotations = NUMPY_ANNOTATIONS, python_interpreter_target = interpreter, requirements = "//:requirements_lock_" + TF_PYTHON_VERSION.replace(".", "_") + ".txt", ) load("@pypi//:requirements.bzl", "install_deps") install_deps()
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ci/official/README.md
# "build_file" item that indicates the script used. ci/official/wheel.sh # Advanced: Select specific build/test targets with "any.sh". # TF_ANY_TARGETS=":your/target" TF_ANY_MODE="test" ci/official/any.sh # Afterwards: Examine the results, which will include: The bazel cache, # generated artifacts like .whl files, and "script.log", from the script.
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ci/official/envs/linux_arm64
# limitations under the License. # ============================================================================== TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_arm64_linux" TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64 # Note: this is not set to "--cpu", because that changes the package name # to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow" # despite lacking Nvidia CUDA support.
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SECURITY.md
ways to detect malicious models/graphs/checkpoints, so the recommended way to mitigate the risk in this scenario is to sandbox the model execution. ### Hardware attacks Physical GPUs or TPUs can also be the target of attacks. [Published research](https://scholar.google.com/scholar?q=gpu+side+channel) shows that it might be possible to use side channel attacks on the GPU to leak data from other
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ci/official/envs/linux_arm64_cross_compile
source ci/official/envs/linux_arm64 TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config cross_compile_linux_arm64"
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