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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
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) # > TF's gen_api_init_files has a genrule to run the core TensorFlow code
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RELEASE.md
metrics, preprocessing layers, and stateful RNN layers) * Update Keras batch normalization layer to use the running mean and average computation in the `fused_batch_norm`. You should see significant performance improvements when using `fused_batch_norm` in Eager mode. * `tf.lite`: * Enable TFLite experimental new converter by default. * XLA
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configure.py
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() ld_version_int = convert_version_to_int(ld_version[3]) if ld_version_int is None:
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.bazelrc
build:release_linux_base --copt=-Wno-error=array-parameter build:release_linux_base --copt=-Wno-error=unused-command-line-argument # Set lld as the linker. build:release_linux_base --linkopt="-fuse-ld=lld" build:release_linux_base --linkopt="-lm" # We have some invalid linker scripts in the build, # so we need to disable this check build:release_linux_base --linkopt=-Wl,--undefined-version
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