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.bazelrc
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.bazelrc
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# TensorFlow Bazel configuration file.
# This file tries to group and simplify build options for TensorFlow
#
# ----CONFIG OPTIONS----
# Android options:
# android:
# android_arm:
# android_arm64:
# android_x86:
# android_x86_64:
#
# iOS options:
# ios:
# ios_armv7:
# ios_arm64:
# ios_x86_64:
# ios_fat:
#
# Macosx options
# darwin_arm64:
#
# Compiler options:
# cuda_clang: Use Clang when building CUDA code.
# avx_linux: Build with avx instruction set on linux.
# avx_win: Build with avx instruction set on windows
#
# Other build options:
# short_logs: Only log errors during build, skip warnings.
# verbose_logs: Show all compiler warnings during build.
# monolithic: Build all TF C++ code into a single shared object.
# dynamic_kernels: Try to link all kernels dynamically (experimental).
# dbg: Build with debug info
#
# TF version options;
# v2: Build TF v2
#
# Feature and Third party library support options:
# xla: Build TF with XLA
# tpu: Build TF with TPU support
# cuda: Build with CUDA support.
# cuda_clang Build with CUDA Clang support.
# mkl: Enable full mkl support.
# tensorrt: Enable Tensorrt support.
# noaws: Disable AWS S3 storage support
# nogcp: Disable GCS support.
# nohdfs: Disable hadoop hdfs support.
# nonccl: Disable nccl support.
#
#
# Remote build execution options (only configured to work with TF team projects for now.)
# rbe_base: General RBE options shared by all flavors.
# rbe_linux: General RBE options used on all linux builds.
# rbe_win: General RBE options used on all windows builds.
#
# rbe_linux_cpu: RBE options to build with only CPU support.
# rbe_linux_cuda: RBE options to build with GPU support using clang.
#
# rbe_win_py39: Windows Python 3.9 RBE config
#
# tensorflow_testing_rbe_win: RBE options to use RBE with tensorflow-testing project on windows
#
# Embedded Linux options (experimental and only tested with TFLite build yet)
# elinux: General Embedded Linux options shared by all flavors.
# elinux_aarch64: Embedded Linux options for aarch64 (ARM64) CPU support.
# elinux_armhf: Embedded Linux options for armhf (ARMv7) CPU support.
#
# Release build options (for all operating systems)
# release_base: Common options for all builds on all operating systems.
# release_cpu_linux: Toolchain and CUDA options for Linux CPU builds.
# release_gpu_linux: Toolchain and CUDA options for Linux GPU builds.
# release_cpu_macos: Toolchain and CUDA options for MacOS CPU builds.
# release_cpu_windows: Toolchain and CUDA options for Windows CPU builds.
# Default build options. These are applied first and unconditionally.
# 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
build --define tsl_protobuf_header_only=true
build --define=use_fast_cpp_protos=true
build --define=allow_oversize_protos=true
build --spawn_strategy=standalone
build -c opt
# Make Bazel print out all options from rc files.
build --announce_rc
# TODO(mihaimaruseac): Document this option or remove if no longer needed
build --define=grpc_no_ares=true
# See https://github.com/bazelbuild/bazel/issues/7362 for information on what
# --incompatible_remove_legacy_whole_archive flag does.
# This flag is set to true in Bazel 1.0 and newer versions. We tried to migrate
# Tensorflow to the default, however test coverage wasn't enough to catch the
# errors.
# There is ongoing work on Bazel team's side to provide support for transitive
# shared libraries. As part of migrating to transitive shared libraries, we
# hope to provide a better mechanism for control over symbol exporting, and
# then tackle this issue again.
#
# TODO: Remove the following two lines once TF doesn't depend on Bazel wrapping
# all library archives in -whole_archive -no_whole_archive.
build --noincompatible_remove_legacy_whole_archive
build --features=-force_no_whole_archive
# 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
build --define=no_aws_support=true
build --define=no_hdfs_support=true
# TF now has `cc_shared_library` targets, so it needs the experimental flag
# TODO(rostam): Remove when `cc_shared_library` is enabled by default
build --experimental_cc_shared_library
# cc_shared_library ensures no library is linked statically more than once.
build --experimental_link_static_libraries_once=false
# Prevent regressions on those two incompatible changes
# TODO: remove those flags when they are flipped in the default Bazel version TF uses.
build --incompatible_enforce_config_setting_visibility
# TODO: also enable this flag after fixing the visbility violations
# build --incompatible_config_setting_private_default_visibility
# Default options should come above this line.
# 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
build:android_x86 --config=android
build:android_x86 --cpu=x86
build:android_x86 --fat_apk_cpu=x86
build:android_x86_64 --config=android
build:android_x86_64 --cpu=x86_64
build:android_x86_64 --fat_apk_cpu=x86_64
# Build everything statically for Android since all static libs are later
# bundled together into a single .so for deployment.
build:android --dynamic_mode=off
# Sets the default Apple platform to macOS.
build:macos --apple_platform_type=macos
# gRPC on MacOS requires this #define
build:macos --copt=-DGRPC_BAZEL_BUILD
# Avoid hitting command line argument limit
build:macos --features=archive_param_file
# Settings for MacOS on ARM CPUs.
build:macos_arm64 --cpu=darwin_arm64
build:macos_arm64 --macos_minimum_os=11.0
# iOS configs for each architecture and the fat binary builds.
build:ios --apple_platform_type=ios
build:ios --apple_bitcode=embedded --copt=-fembed-bitcode
build:ios --copt=-Wno-c++11-narrowing
build:ios_armv7 --config=ios
build:ios_armv7 --cpu=ios_armv7
build:ios_arm64 --config=ios
build:ios_arm64 --cpu=ios_arm64
build:ios_arm64e --config=ios
build:ios_arm64e --cpu=ios_arm64e
build:ios_sim_arm64 --config=ios
build:ios_sim_arm64 --cpu=ios_sim_arm64
build:ios_x86_64 --config=ios
build:ios_x86_64 --cpu=ios_x86_64
build:ios_fat --config=ios
build:ios_fat --ios_multi_cpus=armv7,arm64,i386,x86_64
# 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
build:monolithic --define tsl_protobuf_header_only=false
build:monolithic --experimental_link_static_libraries_once=false # b/229868128
# Please note that MKL on MacOS 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 --define=build_with_openmp=true
build:mkl -c opt
# config to build OneDNN backend with a user specified threadpool.
build:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
build:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0
build:mkl_threadpool --define=build_with_mkl_opensource=true
build:mkl_threadpool -c opt
# Config setting to build oneDNN with Compute Library for the Arm Architecture (ACL).
build:mkl_aarch64 --define=build_with_mkl_aarch64=true
build:mkl_aarch64 --define=build_with_openmp=true
build:mkl_aarch64 --define=build_with_acl=true
build:mkl_aarch64 -c opt
# Config setting to build oneDNN with Compute Library for the Arm Architecture (ACL).
# with Eigen threadpool support
build:mkl_aarch64_threadpool --define=build_with_mkl_aarch64=true
build:mkl_aarch64_threadpool -c opt
# CUDA: This config refers to building CUDA op kernels with nvcc.
build:cuda --repo_env TF_NEED_CUDA=1
build:cuda --crosstool_top=@local_config_cuda//crosstool:toolchain
build:cuda --@local_config_cuda//:enable_cuda
# CUDA: This config refers to building CUDA op kernels with clang.
build:cuda_clang --config=cuda
# Enable TensorRT optimizations https://developer.nvidia.com/tensorrt
build:cuda_clang --config=tensorrt
build:cuda_clang --action_env=TF_CUDA_CLANG="1"
build:cuda_clang --@local_config_cuda//:cuda_compiler=clang
# Select supported compute capabilities (supported graphics cards).
# This is the same as the official TensorFlow builds.
# See https://developer.nvidia.com/cuda-gpus#compute
# TODO(angerson, perfinion): What does sm_ vs compute_ mean? How can users
# select a good value for this? See go/tf-pip-cuda
build:cuda_clang --repo_env=TF_CUDA_COMPUTE_CAPABILITIES="sm_35,sm_50,sm_60,sm_70,sm_75,compute_80"
# Set up compilation CUDA version and paths and use the CUDA Clang toolchain.
build:cuda_clang_official --config=cuda_clang
build:cuda_clang_official --action_env=TF_CUDA_VERSION="11"
build:cuda_clang_official --action_env=TF_CUDNN_VERSION="8"
build:cuda_clang_official --action_env=CUDA_TOOLKIT_PATH="/usr/local/cuda-11.8"
build:cuda_clang_official --action_env=GCC_HOST_COMPILER_PATH="/dt9/usr/bin/gcc"
build:cuda_clang_official --action_env=CLANG_CUDA_COMPILER_PATH="/usr/lib/llvm-16/bin/clang"
build:cuda_clang_official --action_env=LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/tensorrt/lib"
build:cuda_clang_official --crosstool_top="@sigbuild-r2.14-clang_config_cuda//crosstool:toolchain"
# Debug config
build:dbg -c dbg
# Only include debug info for files under tensorflow/, excluding kernels, to
# reduce the size of the debug info in the binary. This is because if the debug
# sections in the ELF binary are too large, errors can occur. See
# https://github.com/tensorflow/tensorflow/issues/48919.
# Users can still include debug info for a specific kernel, e.g. with:
# --config=dbg --per_file_copt=+tensorflow/core/kernels/identity_op.*@-g
# Since this .bazelrc file is synced between the tensorflow/tensorflow repo and
# the openxla/xla repo, also include debug info for files under xla/.
build:dbg --per_file_copt=+.*,-tensorflow.*,-xla.*@-g0
build:dbg --per_file_copt=+tensorflow/core/kernels.*@-g0
# for now, disable arm_neon. see: https://github.com/tensorflow/tensorflow/issues/33360
build:dbg --cxxopt -DTF_LITE_DISABLE_X86_NEON
# AWS SDK must be compiled in release mode. see: https://github.com/tensorflow/tensorflow/issues/37498
build:dbg --copt -DDEBUG_BUILD
# Config to build TPU backend
build:tpu --define=with_tpu_support=true
build:tensorrt --repo_env TF_NEED_TENSORRT=1
# 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:nonccl --define=no_nccl_support=true
# Modular TF build options
build:dynamic_kernels --define=dynamic_loaded_kernels=true
build:dynamic_kernels --copt=-DAUTOLOAD_DYNAMIC_KERNELS
# Don't trigger --config=<host platform> when cross-compiling.
build:android --noenable_platform_specific_config
build:ios --noenable_platform_specific_config
# Suppress all C++ compiler warnings, otherwise build logs become 10s of MBs.
build:android --copt=-w
build:ios --copt=-w
build:linux --host_copt=-w
build:macos --copt=-w
build:windows --copt=/W0
build:windows --host_copt=/W0
# Suppress most C++ complier warnings to reduce log size but allow
# for specific warnings to still be present.
build:linux --copt="-Wno-all"
build:linux --copt="-Wno-extra"
build:linux --copt="-Wno-deprecated"
build:linux --copt="-Wno-deprecated-declarations"
build:linux --copt="-Wno-ignored-attributes"
build:linux --copt="-Wno-array-bounds"
# Add unused-result as an error on Linux.
build:linux --copt="-Wunused-result"
build:linux --copt="-Werror=unused-result"
# Add switch as an error on Linux.
build:linux --copt="-Wswitch"
build:linux --copt="-Werror=switch"
# Required for building with clang
build:linux --copt="-Wno-error=unused-but-set-variable"
# On Windows, `__cplusplus` is wrongly defined without this switch
# See https://devblogs.microsoft.com/cppblog/msvc-now-correctly-reports-__cplusplus/
build:windows --copt=/Zc:__cplusplus
build:windows --host_copt=/Zc:__cplusplus
# Tensorflow uses M_* math constants that only get defined by MSVC headers if
# _USE_MATH_DEFINES is defined.
build:windows --copt=/D_USE_MATH_DEFINES
build:windows --host_copt=/D_USE_MATH_DEFINES
# Windows has a relatively short command line limit, which TF has begun to hit.
# See https://docs.bazel.build/versions/main/windows.html
build:windows --features=compiler_param_file
build:windows --features=archive_param_file
# Speed Windows compile times. Available in VS 16.4 (we are on 16.11). See
# https://groups.google.com/a/tensorflow.org/d/topic/build/SsW98Eo7l3o/discussion
build:windows --copt=/d2ReducedOptimizeHugeFunctions
build:windows --host_copt=/d2ReducedOptimizeHugeFunctions
# Enable the runfiles symlink tree on Windows. This makes it possible to build
# the pip package on Windows without an intermediate data-file archive, as the
# build_pip_package script in its current form (as of Aug 2023) uses the
# runfiles symlink tree to decide what to put into the Python wheel.
startup --windows_enable_symlinks
build:windows --enable_runfiles
# 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
# TF_SYSTEM_LIBS do not work on windows.
# By default, build TF in C++ 17 mode.
build:android --cxxopt=-std=c++17
build:android --host_cxxopt=-std=c++17
build:ios --cxxopt=-std=c++17
build:ios --host_cxxopt=-std=c++17
build:linux --cxxopt=-std=c++17
build:linux --host_cxxopt=-std=c++17
build:macos --cxxopt=-std=c++17
build:macos --host_cxxopt=-std=c++17
build:windows --cxxopt=/std:c++17
build:windows --host_cxxopt=/std:c++17
# On windows, we still link everything into a single DLL.
build:windows --config=monolithic
# On linux, we dynamically link small amount of kernels
build:linux --config=dynamic_kernels
# Make sure to include as little of windows.h as possible
build:windows --copt=-DWIN32_LEAN_AND_MEAN
build:windows --host_copt=-DWIN32_LEAN_AND_MEAN
build:windows --copt=-DNOGDI
build:windows --host_copt=-DNOGDI
# MSVC (Windows): Standards-conformant preprocessor mode
# See https://docs.microsoft.com/en-us/cpp/preprocessor/preprocessor-experimental-overview
build:windows --copt=/Zc:preprocessor
build:windows --host_copt=/Zc:preprocessor
# Misc build options we need for windows.
build:windows --linkopt=/DEBUG
build:windows --host_linkopt=/DEBUG
build:windows --linkopt=/OPT:REF
build:windows --host_linkopt=/OPT:REF
build:windows --linkopt=/OPT:ICF
build:windows --host_linkopt=/OPT:ICF
# Verbose failure logs when something goes wrong
build:windows --verbose_failures
# Work around potential issues with large command lines on windows.
# See: https://github.com/bazelbuild/bazel/issues/5163
build:windows --features=compiler_param_file
# Do not risk cache corruption. See:
# https://github.com/bazelbuild/bazel/issues/3360
build:linux --experimental_guard_against_concurrent_changes
# Configure short or long logs
build:short_logs --output_filter=DONT_MATCH_ANYTHING
build:verbose_logs --output_filter=
# Instruction set optimizations
# TODO(gunan): Create a feature in toolchains for avx/avx2 to
# avoid having to define linux/win separately.
build:avx_linux --copt=-mavx
build:avx_linux --host_copt=-mavx
build:avx_win --copt=/arch=AVX
# Options to build TensorFlow 1.x or 2.x.
# TODO(kanglan): Change v2's define to default behavior
build:v2 --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
# Disable XLA on mobile.
build:xla --define=with_xla_support=true # TODO: remove, it's on by default.
build:android --define=with_xla_support=false
build:ios --define=with_xla_support=false
# BEGIN TF REMOTE BUILD EXECUTION OPTIONS
# Options when using remote execution
# WARNING: THESE OPTIONS WONT WORK IF YOU DO NOT HAVE PROPER AUTHENTICATION AND PERMISSIONS
# Flag to enable remote config
common --experimental_repo_remote_exec
# Make Bazel not try to probe the host system for a C++ toolchain.
build:rbe_base --repo_env=BAZEL_DO_NOT_DETECT_CPP_TOOLCHAIN=1
build:rbe_base --google_default_credentials
build:rbe_base --bes_backend=buildeventservice.googleapis.com
build:rbe_base --bes_results_url="https://source.cloud.google.com/results/invocations"
build:rbe_base --bes_timeout=600s
build:rbe_base --define=EXECUTOR=remote
build:rbe_base --jobs=800
build:rbe_base --remote_executor=grpcs://remotebuildexecution.googleapis.com
build:rbe_base --remote_timeout=3600
build:rbe_base --spawn_strategy=remote,worker,standalone,local
# Attempt to minimize the amount of data transfer between bazel and the remote
# workers:
build:rbe_base --remote_download_toplevel
test:rbe_base --test_env=USER=anon
# TODO(kanglan): Check if we want to merge rbe_linux into rbe_linux_cpu.
build:rbe_linux --config=rbe_base
build:rbe_linux --action_env=PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/go/bin"
# Non-rbe settings we should include because we do not run configure
build:rbe_linux --config=avx_linux
# TODO(gunan): Check why we need this specified in rbe, but not in other builds.
build:rbe_linux --linkopt=-lrt
build:rbe_linux --host_linkopt=-lrt
build:rbe_linux --linkopt=-lm
build:rbe_linux --host_linkopt=-lm
build:rbe_linux_cpu --config=rbe_linux
# Linux cpu and cuda builds share the same toolchain now.
build:rbe_linux_cpu --host_crosstool_top="@sigbuild-r2.14-clang_config_cuda//crosstool:toolchain"
build:rbe_linux_cpu --crosstool_top="@sigbuild-r2.14-clang_config_cuda//crosstool:toolchain"
build:rbe_linux_cpu --extra_toolchains="@sigbuild-r2.14-clang_config_cuda//crosstool:toolchain-linux-x86_64"
build:rbe_linux_cpu --extra_execution_platforms="@sigbuild-r2.14-clang_config_platform//:platform"
build:rbe_linux_cpu --host_platform="@sigbuild-r2.14-clang_config_platform//:platform"
build:rbe_linux_cpu --platforms="@sigbuild-r2.14-clang_config_platform//:platform"
# Python config is the same across all containers because the binary is the same
build:rbe_linux_cpu --repo_env=TF_PYTHON_CONFIG_REPO="@sigbuild-r2.14-clang_config_python"
build:rbe_linux_cpu --python_path="/usr/bin/python3"
# These you may need to change for your own GCP project.
common:rbe_linux_cpu --remote_instance_name=projects/tensorflow-testing/instances/default_instance
build:rbe_linux_cpu --bes_instance_name="tensorflow-testing"
# TODO(kanglan): Remove it after toolchain update is complete.
build:rbe_linux_cpu_old --config=rbe_linux
build:rbe_linux_cpu_old --host_crosstool_top="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain"
build:rbe_linux_cpu_old --crosstool_top="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain"
build:rbe_linux_cpu_old --extra_toolchains="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain-linux-x86_64"
build:rbe_linux_cpu_old --extra_execution_platforms="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_linux_cpu_old --host_platform="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_linux_cpu_old --platforms="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_platform//:platform"
build:rbe_linux_cpu_old --python_path="/usr/local/bin/python3.9"
build:rbe_linux_cpu_old --repo_env=TF_PYTHON_CONFIG_REPO="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_python3.9"
common:rbe_linux_cpu_old --remote_instance_name=projects/tensorflow-testing/instances/default_instance
build:rbe_linux_cpu_old --bes_instance_name="tensorflow-testing"
build:rbe_linux_cuda --config=cuda_clang_official
build:rbe_linux_cuda --config=rbe_linux_cpu
# For Remote build execution -- GPU configuration
build:rbe_linux_cuda --repo_env=REMOTE_GPU_TESTING=1
build:rbe_linux_cuda --repo_env=TF_CUDA_CONFIG_REPO="@sigbuild-r2.14-clang_config_cuda"
build:rbe_linux_cuda --repo_env=TF_TENSORRT_CONFIG_REPO="@sigbuild-r2.14-clang_config_tensorrt"
build:rbe_linux_cuda --repo_env=TF_NCCL_CONFIG_REPO="@sigbuild-r2.14-clang_config_nccl"
test:rbe_linux_cuda --test_env=LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
# TODO(kanglan): Remove rbe_win and rbe_win_py3* after b/289091160 is fixed
build:rbe_win --config=rbe_base
build:rbe_win --crosstool_top="//tensorflow/tools/toolchains/win/tf_win_05022023:toolchain"
build:rbe_win --extra_toolchains="//tensorflow/tools/toolchains/win/tf_win_05022023:cc-toolchain-x64_windows"
build:rbe_win --extra_execution_platforms="//tensorflow/tools/toolchains/win:rbe_windows_ltsc2019"
build:rbe_win --host_platform="//tensorflow/tools/toolchains/win:rbe_windows_ltsc2019"
build:rbe_win --platforms="//tensorflow/tools/toolchains/win:rbe_windows_ltsc2019"
build:rbe_win --shell_executable=C:\\tools\\msys64\\usr\\bin\\bash.exe
build:rbe_win --experimental_strict_action_env=true
# TODO(gunan): Remove once we use MSVC 2019 with latest patches.
build:rbe_win --define=override_eigen_strong_inline=true
# Don't build the python zip archive in the RBE build.
build:rbe_win --remote_download_minimal
build:rbe_win --enable_runfiles
build:rbe_win --nobuild_python_zip
build:rbe_win_py38 --config=rbe_base
build:rbe_win_py38 --repo_env=PYTHON_BIN_PATH=C:\\Python38\\python.exe
build:rbe_win_py38 --repo_env=PYTHON_LIB_PATH=C:\\Python38\\lib\\site-packages
build:rbe_win_py38 --repo_env=TF_PYTHON_CONFIG_REPO=//tensorflow/tools/toolchains/win_1803/py38
build:rbe_win_py38 --python_path=C:\\Python38\\python.exe
build:rbe_win_py39 --config=rbe_base
build:rbe_win_py39 --repo_env=PYTHON_BIN_PATH=C:\\Python39\\python.exe
build:rbe_win_py39 --repo_env=PYTHON_LIB_PATH=C:\\Python39\\lib\\site-packages
build:rbe_win_py39 --repo_env=TF_PYTHON_CONFIG_REPO=//tensorflow/tools/toolchains/win_1803/py39
build:rbe_win_py39 --python_path=C:\\Python39\\python.exe
# TODO(kanglan): Merge tensorflow_testing_rbe_win into rbe_win
common:tensorflow_testing_rbe_win --remote_instance_name=projects/tensorflow-testing/instances/windows
build:tensorflow_testing_rbe_win --bes_instance_name="tensorflow-testing"
# END TF REMOTE BUILD EXECUTION OPTIONS
# TFLite build configs for generic embedded Linux
build:elinux --crosstool_top=@local_config_embedded_arm//:toolchain
build:elinux --host_crosstool_top=@bazel_tools//tools/cpp:toolchain
build:elinux_aarch64 --config=elinux
build:elinux_aarch64 --cpu=aarch64
build:elinux_armhf --config=elinux
build:elinux_armhf --cpu=armhf
build:elinux_armhf --copt -mfp16-format=ieee
# Config-specific options should come above this line.
# Load rc file written by ./configure.
try-import %workspace%/.tf_configure.bazelrc
# Load rc file with user-specific options.
try-import %workspace%/.bazelrc.user
# Here are bazelrc configs for release builds
# Build TensorFlow v2.
test:release_base --test_size_filters=small,medium
# Target the AVX instruction set
build:release_cpu_linux --config=avx_linux
# Use the Clang toolchain to compile
build:release_cpu_linux --crosstool_top="@sigbuild-r2.14-clang_config_cuda//crosstool:toolchain"
# Disable clang extention that rejects type definitions within offsetof.
# This was added in clang-16 by https://reviews.llvm.org/D133574.
# Can be removed once upb is updated, since a type definition is used within
# offset of in the current version of ubp.
# See https://github.com/protocolbuffers/upb/blob/9effcbcb27f0a665f9f345030188c0b291e32482/upb/upb.c#L183.
build:release_cpu_linux --copt=-Wno-gnu-offsetof-extensions
build:release_cpu_linux --copt=-Wno-error=array-parameter
# Set lld as the linker.
build:release_cpu_linux --linkopt="-fuse-ld=lld"
build:release_cpu_linux --linkopt="-lm"
# Container environment settings below this point.
# Use Python 3.X as installed in container image
build:release_cpu_linux --action_env PYTHON_BIN_PATH="/usr/bin/python3"
build:release_cpu_linux --action_env PYTHON_LIB_PATH="/usr/lib/tf_python"
build:release_cpu_linux --python_path="/usr/bin/python3"
# Set Clang as compiler. Use the actual path to clang installed in container.
build:release_cpu_linux --repo_env=CC="/usr/lib/llvm-16/bin/clang"
build:release_cpu_linux --repo_env=BAZEL_COMPILER="/usr/lib/llvm-16/bin/clang"
# Store performance profiling log in the mounted artifact directory.
# The profile can be viewed by visiting chrome://tracing in a Chrome browser.
# See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling
build:release_cpu_linux --profile=/tf/pkg/profile.json.gz
# Test-related settings below this point.
test:release_cpu_linux --build_tests_only --keep_going --test_output=errors --verbose_failures=true
test:release_cpu_linux --local_test_jobs=HOST_CPUS
test:release_cpu_linux --test_env=LD_LIBRARY_PATH
# Give only the list of failed tests at the end of the log
test:release_cpu_linux --test_summary=short
build:release_gpu_linux --config=release_cpu_linux
# Set up compilation CUDA version and paths and use the CUDA Clang toolchain.
# Note that linux cpu and cuda builds share the same toolchain now.
build:release_gpu_linux --config=cuda_clang_official
test:release_gpu_linux --test_env=LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
# Local test jobs has to be 4 because parallel_gpu_execute is fragile, I think
test:release_gpu_linux --test_timeout=300,450,1200,3600 --local_test_jobs=4 --run_under=//tensorflow/tools/ci_build/gpu_build:parallel_gpu_execute
# The old gcc linux build options are preserved in the unsupported_*_linux
# configs. If your project fails to build with Clang, you can use these
# unsupported flags to replace the release flags in your build command.
# However, please note that the old toolchain is no longer officially supported
# by TensorFlow and the unsupported configs will be removed soon b/299962977. We
# strongly recommend that you migrate to Clang as your compiler for TensorFlow
# Linux builds. Instructions are available in the official documentation:
# https://www.tensorflow.org/install/source#install_clang_recommended_linux_only
# Another good option is to use our Docker containers to build and test TF:
# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/tf_sig_build_dockerfiles.
build:unsupported_cpu_linux --config=avx_linux
build:unsupported_cpu_linux --crosstool_top="@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain"
test:unsupported_cpu_linux --test_env=LD_LIBRARY_PATH
test:unsupported_cpu_linux --config=release_base
build:unsupported_gpu_linux --config=cuda
build:unsupported_gpu_linux --config=unsupported_cpu_linux
build:unsupported_gpu_linux --action_env=TF_CUDA_VERSION="11"
build:unsupported_gpu_linux --action_env=TF_CUDNN_VERSION="8"
build:unsupported_gpu_linux --repo_env=TF_CUDA_COMPUTE_CAPABILITIES="sm_35,sm_50,sm_60,sm_70,sm_75,compute_80"
build:unsupported_gpu_linux --config=tensorrt
build:unsupported_gpu_linux --action_env=CUDA_TOOLKIT_PATH="/usr/local/cuda-11.2"
build:unsupported_gpu_linux --action_env=LD_LIBRARY_PATH="/usr/local/cuda:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda-11.1/lib64:/usr/local/tensorrt/lib"
build:unsupported_gpu_linux --action_env=GCC_HOST_COMPILER_PATH="/dt9/usr/bin/gcc"
build:unsupported_gpu_linux --crosstool_top=@ubuntu20.04-gcc9_manylinux2014-cuda11.2-cudnn8.1-tensorrt7.2_config_cuda//crosstool:toolchain
build:release_cpu_macos --config=avx_linux
test:release_cpu_macos --config=release_base
# TODO(kanglan): Update windows configs after b/289091160 is fixed
build:release_cpu_windows --config=avx_win
build:release_cpu_windows --define=no_tensorflow_py_deps=true
test:release_cpu_windows --config=release_base
# Exclude TFRT integration for anything but Linux.
build:android --config=no_tfrt
build:macos --config=no_tfrt
build:windows --config=no_tfrt
build:no_tfrt --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/ir,tensorflow/compiler/mlir/tfrt/ir/mlrt,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/mlrt,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/compiler/mlir/tfrt/transforms/mlrt,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/runtime_fallback/test,tensorflow/core/runtime_fallback/test/gpu,tensorflow/core/runtime_fallback/test/saved_model,tensorflow/core/runtime_fallback/test/testdata,tensorflow/core/tfrt/stubs,tensorflow/core/tfrt/tfrt_session,tensorflow/core/tfrt/mlrt,tensorflow/core/tfrt/mlrt/attribute,tensorflow/core/tfrt/mlrt/kernel,tensorflow/core/tfrt/mlrt/bytecode,tensorflow/core/tfrt/mlrt/interpreter,tensorflow/compiler/mlir/tfrt/translate/mlrt,tensorflow/compiler/mlir/tfrt/translate/mlrt/testdata,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils,tensorflow/core/tfrt/utils/debug,tensorflow/core/tfrt/saved_model/python,tensorflow/core/tfrt/graph_executor/python,tensorflow/core/tfrt/saved_model/utils