TensorFlow CPU环境 SSE/AVX/FMA 指令集编译

TensorFlow CPU环境 SSE/AVX/FMA 指令集编译

sess.run()出现如下Warning

W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.

# 通过pip install tensorflow 来安装tf在 sess.run() 的时候可能会出现
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.

这说明你的machine支持这些指令集但是TensorFlow在编译的时候并没有加入这些指令集，需要手动编译才能够介入这些指令集。

# 1. 下载最新的 TensorFlow
$ git clone https://github.com/tensorflow/tensorflow

# 2. 安装 bazel
# mac os 
$ brew install bazel

# ubuntu 
$ sudo apt-get update && sudo apt-get install bazel

# Windows
$ choco install bazel

# 3. Install TensorFlow Python dependencies
# 如果使用的是Anaconda这部可以跳过

# mac os
$ pip install six numpy wheel 
$ brew install coreutils # 安装coreutils for cuda
$ sudo xcode-select -s /Applications/Xcode.app # set build tools

# ubuntu
sudo apt-get install python3-numpy python3-dev python3-pip python3-wheel
sudo apt-get install libcupti-dev

# 4. 开始编译TensorFlow

# 4.1 configure
$ cd tensorflow # cd to the top-level directory created
# configure 的时候要选择一些东西是否支持，这里建议都选N，不然后面会包错，如果支持显卡，就在cuda的时候选择y
$ ./configure # configure

# 4.2 bazel build
# CUP-only 
$ bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package

# GPU support
bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

# 4.3生成whl文件
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

# 5 安装刚刚编译好的pip 包
# 这里安装的时候官方文档使用的是sudo命令，如果是个人电脑，不建议使用sudo, 直接pip即可。
$ pip install /tmp/tensorflow_pkg/tensorflow-{version}-none-any.whl

# 6 接下来就是验证你是否已经安装成功
$ python -c "import tensorflow as tf; print(tf.Session().run(tf.constant('Hello, TensorFlow')))"
# 然后你就会看到如下输出
b'Hello, TensorFlow'

# 恭喜你，成功编译了tensorflow，Warning也都解决了！

报错解决

Do you wish to build TensorFlow with MKL support? [y/N] y
MKL support will be enabled for TensorFlow
Do you wish to download MKL LIB from the web? [Y/n] y
Darwin is unsupported yet
# 这里MKL不支持Darwin（MAC），因此要选择N

ERROR: /Users/***/Documents/tensorflow/tensorflow/core/BUILD:1331:1: C++ compilation of rule '//tensorflow/core:lib_hash_crc32c_accelerate_internal' failed: cc_wrapper.sh failed: error executing command external/local_config_cc/cc_wrapper.sh -U_FORTIFY_SOURCE -fstack-protector -Wall -Wthread-safety -Wself-assign -fcolor-diagnostics -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG ... (remaining 32 argument(s) skipped): com.google.devtools.build.lib.shell.BadExitStatusException: Process exited with status 1.
clang: error: no such file or directory: 'y'
clang: error: no such file or directory: 'y'

# 这里是因为在configure的时候有些包不支持但是选择了y，因此记住一点所有的都选n

Reference

[1]: https://www.tensorflow.org/install/install_sources