Ubuntu16.04+Tensorflow（GPU）安装教程
安装NVIDIA驱动
1、 通过GPU-Z等途径查看自己显卡型号
2、 通过NVIDIA官网查看自己显卡对应的驱动
网址：http://www.nvidia.com/Download/index.aspx?lang=en-us
3、 执行如下语句，安装
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-384
sudo apt-get install mesa-common-dev
sudo apt-get install freeglut3-dev
4、 重启系统
sudo reboot
5、 检查是否已经安装完毕
Nvidia-smi
Nvidia-settings
安装CUDA
1、 在官网下载CUDA的安装文件
网址：https://developer.nvidia.com/cuda-downloads
2、 配置环境
sudo vim /etc/profile
在文件末尾添加如下语句
export PATH=/usr/local/cuda-9.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64$LD_LIBRARY_PATH
3、 重启电脑
sudo reboot
4、 测试CUDA的samples
cd /usr/local/cuda-9.0/samples/1_Utilities/deviceQuery
sudo make
./deviceQuery
如果显示GPU的相关信息，则说明安装成功
安装cuDNN
1、 官网下载cuDNN（需要注册以及填表）
网址：https://developer.nvidia.com/rdp/cudnn-download
2、 解压文件
tar –zxvf cudnn-9.0-linux-x64-v7.tgz
3、 复制文件
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
4、 生成符号链接
cd /usr/local/cuda/lib64
sudo rm –rf libcudnn.so libcudnn.so.7
sudo ln -s libcudnn.so.7.0.3 libcudnn.so.7
sudo ln -s libcudnn.so.7 libcudnn.so
5、 配置环境变量
vim ~/.bash_profile
在文件末尾添加
export LD_LIBRARY_PATH=”$LD_LIBRARY_PATH:/usr/local/cuda/lib64:.usr/local/cuda/extras/CUPTI/lib64”
export CUDA_HOME=/usr/local/cuda
在terminal输入
Source ~/.bash_profile
安装Bazel
1、 安装Bazel依赖
英文版教程：https://www.bazel.io/versions/master/docs/install.html
sudo add-apt-repository ppa:webupd8team/java
sudo apt-get update
sudo apt-get install oracle-java8-installer
echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stablejdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -
2、 下载Bazel源码并安装
网址：https://github.com/bazelbuild/bazel/releases
chmod +x bazel-0.7.0-installer-linux-x86_64.sh
./bazel-0.7.0-installer-linux-x86_64.sh --user
3、 安装第三方库
sudo apt-get install python-numpy swig python-dev python-wheel
sudo apt-get install git
git clone git://github.com/numpy/numpy.git numpy
安装Tensorflow
1、 下载Tensorflow
git clone https://github.com/tensorflow/tensorflow
2、 配置Tensorflow
查看显卡计算能力：https://developer.nvidia.com/cuda-gpus
[email protected]:~/software/tensorflow$ ./configure
You have bazel 0.7.0 installed.
Please specify the location of python. [Default is /usr/bin/python]:
Found possible Python library paths:
/usr/local/lib/python2.7/dist-packages
/usr/lib/python2.7/dist-packages
Please input the desired Python library path to use. Default is [/usr/local/lib/python2.7/dist-packages]
Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: y
jemalloc as malloc support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]:n
No Google Cloud Platform support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
No Hadoop File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]:n
No Amazon S3 File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with XLA JIT support? [y/N]: n
No XLA JIT support will be enabled for TensorFlow.
Do you wish to build TensorFlow with GDR support? [y/N]: n
No GDR support will be enabled for TensorFlow.
Do you wish to build TensorFlow with VERBS support? [y/N]: n
No VERBS support will be enabled for TensorFlow.
Do you wish to build TensorFlow with OpenCL support? [y/N]: n
No OpenCL support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.
Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leaveempty to default to CUDA 8.0]: 9.0
Please specify the location where CUDA 9.0 toolkit is installed. Refer toREADME.md for more details. [Default is /usr/local/cuda]:
Please specify the cuDNN version you want to use. [Leave empty to defaultto cuDNN 6.0]: 7.0.3
Please specify the location where cuDNN 7.0.3 library is installed. Referto README.md for more details. [Default is /usr/local/cuda]:
Please specify a list of comma-separated Cuda compute capabilities youwant to build with.
You can find the compute capability of your device at:https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantlyincreases your build time and binary size. [Default is: 5.0]
Do you want to use clang as CUDA compiler? [y/N]: n
nvcc will be used as CUDA compiler.
Please specify which gcc should be used by nvcc as the host compiler.[Default is /usr/bin/gcc]:
Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.
Please specify optimization flags to use during compilation when bazel option"--config=opt" is specified [Default is -march=native]:
Add "--config=mkl" to your bazel command to build with MKLsupport.
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 setthe environment variable "TF_MKL_ROOT" every time before build.
Configuration finished
3、 编译目标程序，开启GPU支持
bazel build -c opt --config=cuda//tensorflow/cc:tutorials_example_trainer
bazel-bin/tensorflow/cc/tutorials_example_trainer--use_gpu
4、 创建pip包并安装
bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package/tmp/tensorflow_pkg
# .whl 文件的实际名字与你所使用的平台有关
pip install/tmp/tensorflow_pkg/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
测试
从源代码树的根路径执行
cd tensorflow/examples/tutorials/mnist
python fully_connected_feed.py