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TensorFlow优化车轮

定制构建用于张量，并具有平台优化，包括SSE，AVX和FMA。如果您在库存pip install tensorflow中看到以下消息，那么您来对位置。

 The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.

or:
Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA

这些轮子是用于在云机学习平台Tinymind上使用的。如果要将它们安装在自己的Linux框中（Ubuntu 16.04 LTS），则可以这样做：

 # RELEASE is the git tag like tf1.1-cpu. WHEEL is the full wheel name.
pip --no-cache-dir install https://github.com/mind/wheels/releases/download/{RELEASE}/{WHEEL}

所有车轮的列表可以在“版本”页面中找到。

版本

单击下面的链接跳到特定的发行版本。同样，除非另有说明，否则它们是为Ubuntu 16.04 LT构建的。

TF	构建
1.1	CPU，GPU
1.2	CPU，GPU（仅Python 3.6）
1.2.1	CPU，GPU
1.3	CPU，与MPI的GPU
1.3.1	CPU，CPU调试，GPU，GPU与MPI
1.4	CPU，CPU调试，CPU MACOS，GPU（CUDA 8，CUDA 9，用于计算3.7，CUDA 9，用于计算3.7/6.0/7.0，CUDA 9，通用CUDA，无MKL的CUDA 9）
1.4.1	CPU，GPU（CUDA 8，CUDA 9，CUDA 9.1）
1.5	CPU，GPU（CUDA 9，无MKL的CUDA 9，CUDA 9.1，CUDA 9.1无MKL）
1.6	CPU，GPU（CUDA 9.1，CUDA 9.1无MKL）
1.7	CPU，GPU（CUDA 9，CUDA 9.1，CUDNN 7.1）

请注意，您的计算机需要具有相对较新的Intel CPU（如果使用GPU版本，则需要NVIDIA GPU）与下面的车轮兼容。如果硬件不是最新的，则车轮将不起作用。

Tensorflow 1.4.1及以上的车轮包含对GCP，S3和Hadoop的支持。汇编标志包括：

 --config=opt --config=cuda --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0 --copt=-mavx --copt=-msse4.1 --copt=-msse4.2 --copt=-mavx2 --copt=-mfma --copt=-mfpmath=both

您很可能需要的车轮在下面列出。需要某物或轮子对您不起作用吗？提出问题。（不幸的是，由于我们自己没有Windows机器，我们将无法满足Windows车轮的要求。）

版本	Python	拱	关联
1.1	2.7	中央处理器	https://github.com/mind/wheels/releases/download/tf1.1-cpu/tensorflow-1.1.0-cp27-cp27-cp27mu-linux_x86_64.whl
1.1	3.5	中央处理器	https://github.com/mind/wheels/releases/download/tf1.1-cpu/tensorflow-1.1.0-cp35-cp35-cp355m-linux_x86_64.whl
1.1	3.6	中央处理器	https://github.com/mind/wheels/releases/download/tf1.1-cpu/tensorflow-1.1.0-cp36-cp36-cp36m-linux_x86_64.whl
1.1	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.1-gpu/tensorflow-1.1.0-cp27-cp27-cp27mu-linux_x86_64.whl
1.1	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.1-gpu/tensorflow-1.1.0-cp35-cp35-cp355m-linux_x86_64.whl
1.1	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.1-gpu/tensorflow-1.1.0-cp36-cp36-cp36m-linux_x86_64.whl
1.2	2.7	中央处理器	https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-cp27-cp27-cp27mu-linux_x86_64.whl
1.2	3.5	中央处理器	https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-cp35-cp35-cp355m-linux_x86_64.whl
1.2	3.6	中央处理器	https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-cp36-cp36-cp36m-linux_x86_64.whl
1.2	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.2-gpu/tensorflow-1.2.0-cp36-cp36-cp36m-linux_x86_64.whl
1.2.1	2.7	中央处理器	https://github.com/mind/wheels/releases/download/tf1.2.1-cpu/tensorflow-1.2.1-cp27-cp27-cp27mu-linux_x86_64.whl
1.2.1	3.5	中央处理器	https://github.com/mind/wheels/releases/download/tf1.2.1-cpu/tensorflow-1.2.1-cp35-cp35-cp355m-linux_x86_64.whl
1.2.1	3.6	中央处理器	https://github.com/mind/wheels/releases/download/tf1.2.1-cpu/tensorflow-1.2.1-cp36-cp36-cp36m-linux_x86_64.whl
1.2.1	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.2.1-gpu/tensorflow-1.2.1-cp27-cp27-cp27mu-linux_x86_64.whl
1.2.1	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.2.1-gpu/tensorflow-1.2.1-cp35-cp35-cp355m-linux_x86_64.whl
1.2.1	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.2.1-gpu/tensorflow-1.2.1-cp36-cp36-cp36m-linux_x86_64.whl
1.3	2.7	中央处理器	https://github.com/mind/wheels/releases/download/tf1.3-cpu/tensorflow-1.3.0-cp27-cp27-cp27mu-linux_x86_64.whl
1.3	3.5	中央处理器	https://github.com/mind/wheels/releases/download/tf1.3-cpu/tensorflow-1.3.0-cp35-cp35-cp355m-linux_x86_64.whl
1.3	3.6	中央处理器	https://github.com/mind/wheels/releases/download/tf1.3-cpu/tensorflow-1.3.0-cp36-cp36-cp36m-linux_x86_64.whl
1.3	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.3-gpu/tensorflow-1.3.0-cp27-cp27-cp27mu-linux_x86_64.whl
1.3	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.3-gpu/tensorflow-1.3.0-cp35-cp35-cp355m-linux_x86_64.whl
1.3	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.3-gpu/tensorflow-1.3.0-cp36-cp36-cp36m-linux_x86_64.whl
1.3.1	2.7	中央处理器	https://github.com/mind/wheels/releases/download/tf1.3.1-cpu/tensorflow-1.3.1-cp27-cp27-cp27mu-linux_x86_64.whl
1.3.1	3.5	中央处理器	https://github.com/mind/wheels/releases/download/tf1.3.1-cpu/tensorflow-1.3.1-cp35-cp35-cp355m-linux_x86_64.whl
1.3.1	3.6	中央处理器	https://github.com/mind/wheels/releases/download/tf1.3.1-cpu/tensorflow-1.3.1-cp36-cp36-cp36m-linux_x86_64.whl
1.3.1	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.3.1-gpu/tensorflow-1.3.1-cp27-cp27-cp27mu-linux_x86_64.whl
1.3.1	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.3.1-gpu/tensorflow-1.3.1-cp35-cp35-cp355m-linux_x86_64.whl
1.3.1	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.3.1-gpu/tensorflow-1.3.1-cp36-cp36-cp36m-linux_x86_64.whl
1.4	2.7	中央处理器	https://github.com/mind/wheels/releases/download/tf1.4-cpu/tensorflow-1.4.0-cp27-cp27-cp27mu-linux_x86_64.whl
1.4	3.5	中央处理器	https://github.com/mind/wheels/releases/download/tf1.4-cpu/tensorflow-1.4.0-cp35-cp35-cp355m-linux_x86_64.whl
1.4	3.6	中央处理器	https://github.com/mind/wheels/releases/download/tf1.4-cpu/tensorflow-1.4.0-cp36-cp36-cp36m-linux_x86_64.whl
1.4	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.4-gpu/tensorflow-1.4.0-cp27-cp27-cp27mu-linux_x86_64.whl
1.4	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.4-gpu/tensorflow-1.4.0-cp35-cp35-cp355m-linux_x86_64.whl
1.4	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.4-gpu/tensorflow-1.4.0-cp36-cp36-cp36m-linux_x86_64.whl
1.4.1	2.7	中央处理器	https://github.com/mind/wheels/releases/download/tf1.4.1-cpu/tensorflow-1.4.1-cp27-cp27-cp27mu-linux_x86_64.whl
1.4.1	3.5	中央处理器	https://github.com/mind/wheels/releases/download/tf1.4.1-cpu/tensorflow-1.4.1-cp35-cp35-cp35-cp35m-linux_x86_64.whl
1.4.1	3.6	中央处理器	https://github.com/mind/wheels/releases/download/tf1.4.1-cpu/tensorflow-1.4.1-cp36-cp36-cp36m-linux_x86_64.whl
1.4.1	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.4.1-gpu/tensorflow-1.4.1-cp27-cp27-cp27mu-linux_x86_64.whl
1.4.1	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.4.1-gpu/tensorflow-1.4.1-cp35-cp35-cp355m-linux_x86_64.whl
1.4.1	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.4.1-gpu/tensorflow-1.4.1-cp36-cp36-cp36m-linux_x86_64.whl
1.5	2.7	中央处理器	https://github.com/mind/wheels/releases/download/tf1.5-cpu/tensorflow-1.5.0-cp27-cp27-cp27mu-linux_x86_64.whl
1.5	3.5	中央处理器	https://github.com/mind/wheels/releases/download/tf1.5-cpu/tensorflow-1.5.0-cp35-cp35-cp35m-linux_x86_64.whl
1.5	3.6	中央处理器	https://github.com/mind/wheels/releases/download/tf1.5-cpu/tensorflow-1.5.0-cp36-cp36-cp36m-linux_x86_64.whl
1.5	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.5-gpu/tensorflow-1.5.0-cp27-cp27-cp27mu-linux_x86_64.whl
1.5	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.5-gpu/tensorflow-1.5.0-cp35-cp35-cp35m-linux_x86_64.whl
1.5	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.5-gpu/tensorflow-1.5.0-cp36-cp36-cp36m-linux_x86_64.whl
1.6	2.7	中央处理器	https://github.com/mind/wheels/releases/download/tf1.6-cpu/tensorflow-1.6.0-cp27-cp27-cp27mu-linux_x86_64.whl
1.6	3.5	中央处理器	https://github.com/mind/wheels/releases/download/tf1.6-cpu/tensorflow-1.6.0-cp35-cp35-cp355m-linux_x86_64.whl
1.6	3.6	中央处理器	https://github.com/mind/wheels/releases/download/tf1.6-cpu/tensorflow-1.6.0-cp36-cp36-cp36m-linux_x86_64.whl
1.6	2.7	GPU	https://github.com/mind/wheels/releases/download/tf1.6-gpu-cuda91/tensorflow-1.6.0-cp27-cp27-cp27mu-linux_x86_64.whl
1.6	3.5	GPU	https://github.com/mind/wheels/releases/download/tf1.6-gpu-cuda91/tensorflow-1.6.0-cp35-cp35-cp355m-linux_x86_64.whl
1.6	3.6	GPU	https://github.com/mind/wheels/releases/download/tf1.6-gpu-cuda91/tensorflow-1.6.0-cp36-cp36-cp36m-linux_x86_64.whl

帮助！

本节包含调试设置的提示。认真地说，尝试tinymind，您将无需浪费时间再次调试。我们还拥有可以在自己的机器上使用的Docker图像。如果本节没有解决您的问题，请确保提出问题。

库达

不同的TensorFlow版本支持/需要不同的CUDA版本：

TF	库达	库丁	计算能力
1.1，1.2	8.0	5.1	3.7（K80）
1.2.1-1.3.1	8.0	6.0	3.7
1.4	8.0/9.0	6.0/7.0	3.7，6.0（P100），7.0（V100）
1.4.1	8.0/9.0/9.1	6.0/7.0	3.7、6.0、7.0
1.5	9.0/9.1	7.0	3.7、6.0、7.0
1.6	9.1	7.0	3.7、6.0、7.0
1.7	9.0/9.1	7.0/7.1	3.7、6.0、7.0

TensorFlow <1.4与当前版本CUDA 9不起作用。您需要进行sudo apt-get install cuda sudo apt-get install cuda-8-0 。 cuda 8张曲流1.4的变体使用cudnn 6.0，cuda 9.x变体与cudnn 7.x一起使用。

 # Install CUDA 8
curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda-8-0

# Install CUDA 9
curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda

确保正确设置了与CUDA相关的环境变量：

 echo ' export CUDA_HOME=/usr/local/cuda ' >> ~ /.bashrc
echo ' export PATH=$PATH:$CUDA_HOME/bin ' >> ~ /.bashrc
echo ' export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64 ' >> ~ /.bashrc
. ~ /.bashrc

下载正确的cudnn并按照以下方式安装：

 # The cuDNN tar file.
tar xzvf cudnn-9.0-linux-x64-v7.0.tgz
sudo cp cuda/lib64/ * /usr/local/cuda/lib64/
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/

缺少libcupti图书馆？安装并将其添加到您的PATH中。

sudo apt-get install libcupti-dev
echo ' export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH ' >> ~ /.bashrc

张力

某些车轮支撑张力。要安装Tensorrt，请首先从Nvidia的网站下载，然后运行：

sudo dpkg -i nv-tensorrt-repo-ubuntu1604-ga-cuda9.0-trt3.0.4-20180208_1-1_amd64.deb
sudo apt-get update
sudo apt-get install tensorrt

Mkl

MKL是英特尔的深度学习内核图书馆，它使CPU上的训练神经网更快。如果您没有它，请像以下内容一样安装它：

 # If you don't have cmake
sudo apt install cmake

git clone https://github.com/01org/mkl-dnn.git
cd mkl-dnn/scripts && ./prepare_mkl.sh && cd ..
mkdir -p build && cd build && cmake .. && make
sudo make install

echo ' export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib ' >> ~ /.bashrc