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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# 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 # Prevent double-compilation of some TF code, ref. b/183279666 (internal) # > TF's gen_api_init_files has a genrule to run the core TensorFlow code
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tensorflow/BUILD
config_setting( name = "dynamic_loaded_kernels", define_values = { "dynamic_loaded_kernels": "true", "framework_shared_object": "true", }, visibility = ["//visibility:public"], ) config_setting( name = "using_rocm_hipcc", define_values = {"using_rocm_hipcc": "true"}, ) config_setting( name = "override_eigen_strong_inline", define_values = {"override_eigen_strong_inline": "true"},
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# 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 build --linkopt="-fuse-ld=lld" build --linkopt="-lm" build --linkopt="-Wl,--undefined-version"
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SECURITY.md
inspected and debugged and it is intended to be used during the development phase. As part of the differences that make Eager mode easier to debug, the [shape inference functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface) are skipped, and any checks implemented inside the shape inference code are not executed. The security impact of skipping those checks should be low, since the attack
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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
} @test "Pip package doesn't depend on CUDA for static builds (i.e. Windows)" { bazel cquery \ --experimental_cc_shared_library \ --@local_config_cuda//:enable_cuda \ --define framework_shared_object=false \ "somepath(//tensorflow/tools/pip_package:build_pip_package, " \ "@local_config_cuda//cuda:cudart + "\ "@local_config_cuda//cuda:cudart + "\
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RELEASE.md
compatibility with GPU delegate. * Extension Types * Add experimental API to define new Python classes that can be handled by TensorFlow APIs. To create an extension type, simply define a Python class with `tf.experimental.ExtensionType` as its base, and use type annotations to specify the type for each field. E.g.: `python class
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.bazelrc
# Default paths for TF_SYSTEM_LIBS build:linux --define=PREFIX=/usr build:linux --define=LIBDIR=$(PREFIX)/lib build:linux --define=INCLUDEDIR=$(PREFIX)/include build:linux --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include build:macos --define=PREFIX=/usr build:macos --define=LIBDIR=$(PREFIX)/lib build:macos --define=INCLUDEDIR=$(PREFIX)/include build:macos --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include
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ci/official/utilities/code_check_full.bats
} @test "Pip package doesn't depend on CUDA for static builds (i.e. Windows)" { bazel cquery \ --experimental_cc_shared_library \ --@local_config_cuda//:enable_cuda \ --define framework_shared_object=false \ "somepath(//tensorflow/tools/pip_package:wheel, " \ "@local_config_cuda//cuda:cudart + "\ "@local_config_cuda//cuda:cudart + "\ "@local_config_cuda//cuda:cuda_driver + "\
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