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TensorFlow CPU環境 SSE/AVX/FMA 指令集編譯
sess.run()出現如下Warning
# 通過pip install tensorflow 來安裝tf在 sess.run() 的時候可能會出現
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.- 1
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這說明你的machine支持這些指令集但是TensorFlow在編譯的時候並沒有加入這些指令集，需要手動編譯TensorFlow才能夠加入這些指令集。
# 1. 下載最新的 TensorFlow $ git clone https://github.com/tensorflow/tensorflow # 2. 安裝 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這部可以跳過 # mac os $ pip install six numpy wheel $ brew install coreutils # 安裝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. 開始編譯TensorFlow # 4.1 configure $ cd tensorflow # cd to the top-level directory created # configure 的時候要選擇一些東西是否支持，這里建議都選N，不然后面會包錯，如果支持顯卡，就在cuda的時候選擇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 # 5 安裝剛剛編譯好的pip 包 # 這里安裝的時候官方文檔使用的是sudo命令，如果是個人電腦，不建議使用sudo, 直接pip即可。 $ pip install /tmp/tensorflow_pkg/tensorflow-{version}-none-any.whl # 6 接下來就是驗證你是否已經安裝成功 $ python -c "import tensorflow as tf; print(tf.Session().run(tf.constant('Hello, TensorFlow')))" # 然后你就會看到如下輸出 b'Hello, TensorFlow' # 恭喜你，成功編譯了tensorflow，Warning也都解決了！- 1
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報錯解決
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 # 這里MKL不支持Darwin（MAC），因此要選擇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' # 這里是因為在configure的時候有些包不支持但是選擇了y，因此記住一點所有的都選n- 1
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