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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
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" # Prevent double-compilation of some TF code, ref. b/183279666 (internal)
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CONTRIBUTING.md
for the required packages. Alternatively, use the said [tensorflow/build Docker images](https://hub.docker.com/r/tensorflow/build) (`tensorflow/tensorflow:devel` and `tensorflow/tensorflow:devel-gpu` are no longer supported for) development. Use TF SIG Build Dockerfiles in development to avoid installing the packages directly on your system (in which case remember to change the directory from `/root` to `/tensorflow`
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ci/official/containers/linux_arm64/builder.devtoolset/build_devtoolset.sh
--enable-initfini-array \ --enable-languages="c,c++" \ --enable-linker-build-id \ --enable-plugin \ --enable-shared \ --enable-threads=posix \ --with-default-libstdcxx-abi=${LIBSTDCXX_ABI} \ --with-gcc-major-version-only \ --with-linker-hash-style="gnu" \ && \ make -j$(nproc) && \ make install
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ci/official/README.md
# variables in between script executions. To forcibly remove the # container and start fresh, run "docker rm -f tf". Removing the container # destroys some temporary bazel data and causes longer builds. # # You will need the NVIDIA Container Toolkit for GPU testing: # https://github.com/NVIDIA/nvidia-container-toolkit # # Note: if you interrupt a bazel command on docker (ctrl-c), you
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tensorflow/c/eager/c_api_experimental.h
// Creates a new TensorHandle from memory residing in the physical device // device_name. Takes ownership of the memory, and will call deleter to release // it after TF no longer needs it or in case of error. // // Custom devices must use TFE_NewCustomDeviceTensorHandle instead. TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandleFromDeviceMemory(
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RELEASE.md
`DynamicLossScale` will no longer affect the weights of the LossScaleOptimizer, and vice versa. * The global policy can no longer be set to a non-floating point policy in `tf.keras.mixed_precision.experimental.set_policy` * In `Layer.call`, `AutoCastVariable`s will no longer be casted within
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tensorflow/BUILD
# the shared library, only export the core TF API functions to avoid # causing library conflicts (e.g., those reported in github issue 1924). # On Linux, tell the linker (-Wl,<option>) to use a version script that # excludes all but a subset of function names. # On MacOS, the linker does not support version_script, but has an # an "-exported_symbols_list" command. -z defs disallows undefined # symbols in object files.
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tensorflow/c/eager/BUILD
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.bazelrc
# TODO(mihaimaruseac): Document this option or remove if no longer needed build --enable_platform_specific_config # Enable XLA support by default. build --define=with_xla_support=true # TODO(mihaimaruseac): Document this option or remove if no longer needed build --config=short_logs # TODO(mihaimaruseac): Document this option or remove if no longer needed build --config=v2 # Disable AWS/HDFS support by default
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configure.py
if is_ppc64le(): # Enable MMA Dynamic Dispatch support if 'gcc' and if linker >= 2.35 gcc_env = get_gcc_compiler(environ_cp) if gcc_env is not None: # Use gold linker if 'gcc' and if 'ppc64le' write_to_bazelrc('build --linkopt="-fuse-ld=gold"') # Get the linker version ld_version = run_shell([gcc_env, '-Wl,-version']).split()
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