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.bazelrc
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# Android configs. Bazel needs to have --cpu and --fat_apk_cpu both set to the
# target CPU to build transient dependencies correctly. See
# https://docs.bazel.build/versions/master/user-manual.html#flag--fat_apk_cpu
build:android --crosstool_top=//external:android/crosstool
build:android --host_crosstool_top=@bazel_tools//tools/cpp:toolchain
build:android_arm --config=android
build:android_arm --cpu=armeabi-v7a
build:android_arm --fat_apk_cpu=armeabi-v7a
build:android_arm64 --config=android
build:android_arm64 --cpu=arm64-v8a
build:android_arm64 --fat_apk_cpu=arm64-v8a
# Sets the default Apple platform to macOS.
build --apple_platform_type=macos
# Config to use a mostly-static build and disable modular op registration
# support (this will revert to loading TensorFlow with RTLD_GLOBAL in Python).
# By default, TensorFlow will build with a dependence on
# //tensorflow:libtensorflow_framework.so.
build:monolithic --define framework_shared_object=false
# For projects which use TensorFlow as part of a Bazel build process, putting
# nothing in a bazelrc will default to a monolithic build. The following line
# opts in to modular op registration support by default.
build --define framework_shared_object=true
# Please note that MKL on MacOS or windows is still not supported.
# If you would like to use a local MKL instead of downloading, please set the
# environment variable "TF_MKL_ROOT" every time before build.
build:mkl --define=build_with_mkl=true --define=enable_mkl=true
build:mkl --define=tensorflow_mkldnn_contraction_kernel=0
build:mkl -c opt
# This config option is used to enable MKL-DNN open source library only,
# without depending on MKL binary version.
build:mkl_open_source_only --define=build_with_mkl_dnn_only=true
build:mkl_open_source_only --define=build_with_mkl=true --define=enable_mkl=true
build:mkl_open_source_only --define=tensorflow_mkldnn_contraction_kernel=0
build:download_clang --crosstool_top=@local_config_download_clang//:toolchain
build:download_clang --define=using_clang=true
# Instruct clang to use LLD for linking.
# This only works with GPU builds currently, since Bazel sets -B/usr/bin in
# auto-generated CPU crosstool, forcing /usr/bin/ld.lld to be preferred over
# the downloaded one.
build:download_clang_use_lld --linkopt='-fuse-ld=lld'
build:cuda --crosstool_top=@local_config_cuda//crosstool:toolchain
build:cuda --define=using_cuda=true --define=using_cuda_nvcc=true
build:rocm --crosstool_top=@local_config_rocm//crosstool:toolchain
build:rocm --define=using_rocm=true --define=using_rocm_hipcc=true
build:cuda_clang --crosstool_top=@local_config_cuda//crosstool:toolchain
build:cuda_clang --define=using_cuda=true --define=using_cuda_clang=true --define=using_clang=true
build:sycl --crosstool_top=@local_config_sycl//crosstool:toolchain
build:sycl --define=using_sycl=true --define=using_trisycl=false
build:sycl_nodouble --crosstool_top=@local_config_sycl//crosstool:toolchain
build:sycl_nodouble --define=using_sycl=true --cxxopt -DTENSORFLOW_SYCL_NO_DOUBLE
build:sycl_asan --crosstool_top=@local_config_sycl//crosstool:toolchain
build:sycl_asan --define=using_sycl=true --define=using_trisycl=false --copt -fno-omit-frame-pointer --copt -fsanitize-coverage=3 --copt -DGPR_NO_DIRECT_SYSCALLS --linkopt -fPIC --linkopt -fsanitize=address
build:sycl_trisycl --crosstool_top=@local_config_sycl//crosstool:toolchain
build:sycl_trisycl --define=using_sycl=true --define=using_trisycl=true
build:rpi3 --host_crosstool_top=@bazel_tools//tools/cpp:toolchain
build:rpi3 --crosstool_top=//tools/arm_compiler/linaro-gcc72-armeabi:toolchain
build:rpi3 --cpu=armv7 --define=target_system=rpi3 --define tensorflow_mkldnn_contraction_kernel=0
build:rpi3 --copt=-march=armv7-a --copt=-mtune=cortex-a53 --copt=-mfloat-abi=hard --copt=-mfpu=neon-fp-armv8 --copt=-DRASPBERRY_PI --copt=-D_GLIBCXX_USE_CXX11_ABI=0 --copt=-std=gnu99 --copt=-mno-unaligned-access
build:rpi3_opt --copt=-funsafe-math-optimizations --copt=-ftree-vectorize --copt=-pipe
build:rpi3-armv8 --host_crosstool_top=@bazel_tools//tools/cpp:toolchain
build:rpi3-armv8 --crosstool_top=//tools/arm_compiler/linaro-gcc72-aarch64:toolchain
build:rpi3-armv8 --cpu=armv8 --define=target_system=rpi3-armv8 --define tensorflow_mkldnn_contraction_kernel=0
build:rpi3-armv8 --copt=-march=armv8-a --copt=-mtune=cortex-a53 --copt=-DRASPBERRY_PI --copt=-D_GLIBCXX_USE_CXX11_ABI=0 --copt=-std=gnu99
build:rpi3-armv8_opt --copt=-funsafe-math-optimizations --copt=-ftree-vectorize --copt=-pipe
# Options extracted from configure script
build:gdr --define=with_gdr_support=true
build:ngraph --define=with_ngraph_support=true
build:verbs --define=with_verbs_support=true
build:numa --define=with_numa_support=true
# Options to disable default on features
build:noaws --define=no_aws_support=true
build:nogcp --define=no_gcp_support=true
build:nohdfs --define=no_hdfs_support=true
build:nokafka --define=no_kafka_support=true
build:noignite --define=no_ignite_support=true
build:nonccl --define=no_nccl_support=true
build --define=use_fast_cpp_protos=true
build --define=allow_oversize_protos=true
build --spawn_strategy=standalone
build --strategy=Genrule=standalone
build -c opt
# Other build flags.
build --define=grpc_no_ares=true
# Modular TF build options
build:dynamic_kernels --define=dynamic_loaded_kernels=true
build:dynamic_kernels --copt=-DAUTOLOAD_DYNAMIC_KERNELS
# Build TF with C++ 17 features.
build:c++17 --cxxopt=-std=c++1z
build:c++17 --cxxopt=-stdlib=libc++
build:c++1z --cxxopt=-std=c++1z
build:c++1z --cxxopt=-stdlib=libc++
# Default paths for TF_SYSTEM_LIBS
build --define=PREFIX=/usr
build --define=LIBDIR=$(PREFIX)/lib
build --define=INCLUDEDIR=$(PREFIX)/include
# Disable MKL-DNN contraction kernels by default.
build --define=tensorflow_mkldnn_contraction_kernel=0
# Default options should come above this line
# Options from ./configure
try-import %workspace%/.tf_configure.bazelrc
# Put user-specific options in .bazelrc.user
try-import %workspace%/.bazelrc.user