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以下使用的操作系统是Ubuntu 18.04，安装tensorflow-gpu 1.9，CUDA 9.0及对应cuDNN版本。
安装前说明 1.前提是机器上必须有Nvidia显卡，不太老就好（配置不好的也没必要玩这个了吧，费电），在Nvidia官网可以在这里查到显卡支持情况安装过程中的命令都需要root身份，请使用su root切换或者每次加 sudo，编译运行测试代码使用普通用户就好。 2.必须按tensorflow 官网提示的版本安装 1.9 对应 CUDA 9.0，CUDA 9.0 要下载相应版本的cuDNN。 3.如果喜欢折腾，建议使用没有重要数据的硬盘。 4.安装包最好下载到其他电脑上，使用scp拷贝到安装机上，重装了几遍ubuntu，下一次包就2个G。
下载主要安装文件 1.CUDA 工具包 http://nvidia.com/cuda #我选的是16.04的run文件，其他的不保证可行 cuda_9.0.176_384.81_linux.run 2.cuDNN 深度神经网络（DNN）开发环境，需要网站注册 http://developer.nvidia.com/cudnn libcudnn7-dev_7.1.4.18-1+cuda9.0_amd64.deb libcudnn7_7.1.4.18-1+cuda9.0_amd64.deb libcudnn7-doc_7.1.4.18-1+cuda9.0_amd64.deb
准备环境 1.看CUDA自带的驱动版本，这里是384.81，低于这个版本就要先卸载，>= 跳过 #建议run文件卸载，即你之前下载的Nvidia驱动run文件 chmod +x *.run ./NVIDIA-Linux-x86_64-384.59.run --uninstall # 不建议采取这种，不知道为什么没尝试过 apt-get remove --purge nvidia* 2.禁用自带的nouveau驱动，如果你连Nvidia驱动都装过了，这一步也免了 运行：vi /etc/modprobe.d/blacklist.conf #加两行 blacklist nouveau options nouveau modeset=0 #生效配置 update-initramfs -u #重启，后分辨率变低了，毕竟没有显卡驱动了 reboot #检查是否生效 lsmod | grep nouveau #如果屏幕没有输出则禁用nouveau成功 3.安装必要的编译环境否者自带网卡驱动安装不上 apt install gcc g++ make make-guile 4.针对CUDA 9.0，必须将GCC降级为gcc5，也是安装CUDA时发现的 apt install gcc-5 g++-5 update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 50 update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 50
安装 CUDA 工具包 1.一定要根据tensorflow版本安装对应版本的CUDA 1.9对应9.0，这点很重要 chmod +x cuda_9.0.176_384.81_linux.run sh ./cuda_9.0.176_384.81_linux.run #会有说明，需要看的自己看，看了几页不想看/条款看不懂的按q键 1].如果安装过程中提示失败，根据提示查看log排错 2].安装成功后的log Do you accept the previously read EULA? accept/decline/quit: accept You are attempting to install on an unsupported configuration. Do you wish to continue? (y)es/(n)o [ default is no ]: y #这里384.81表示显卡驱动版本，如果本机安装的显卡驱动版本比它高就不需要安装 #选no主要是前面有问题的时候安了CUDA9.2 #正常应该是yes Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81? (y)es/(n)o/(q)uit: n Install the CUDA 9.0 Toolkit? (y)es/(n)o/(q)uit: y Enter Toolkit Location [ default is /usr/local/cuda-9.0 ]: Do you want to install a symbolic link at /usr/local/cuda? (y)es/(n)o/(q)uit: y Install the CUDA 9.0 Samples? (y)es/(n)o/(q)uit: y Enter CUDA Samples Location [ default is /root ]: Installing the CUDA Toolkit in /usr/local/cuda-9.0 ... Missing recommended library: libGLU.so Missing recommended library: libX11.so Missing recommended library: libXi.so Missing recommended library: libXmu.so Missing recommended library: libGL.so Installing the CUDA Samples in /root ... Copying samples to /root/NVIDIA_CUDA-9.0_Samples now... Finished copying samples. =========== = Summary = =========== Driver: Not Selected Toolkit: Installed in /usr/local/cuda-9.0 Samples: Installed in /root, but missing recommended libraries Please make sure that - PATH includes /usr/local/cuda-9.0/bin - LD_LIBRARY_PATH includes /usr/local/cuda-9.0/lib64, or, add /usr/local/cuda-9.0/lib64 to /etc/ld.so.conf and run ldconfig as root To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-9.0/bin Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-9.0/doc/pdf for detailed information on setting up CUDA. ***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 384.00 is required for CUDA 9.0 functionality to work. To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file: sudo <CudaInstaller>.run -silent -driver Logfile is /tmp/cuda_install_7657.log /root/NVIDIA_CUDA-9.0_Samples 2.设置环境变量 运行：vi /etc/ld.so.conf.d/cuda.conf #写入两行 /usr/local/cuda/lib64 /usr/local/cuda/extras/CUPTI/lib64 运行：vi /etc/profile #加入两行 export CUDA_HOME=/usr/local/cuda/bin export PATH=$PATH:$CUDA_HOME 3.重启，使用reboot命令。
测试安装情况 没有报错就表示安装成功 cd /root/NVIDIA_CUDA-9.0_Samples/samples/1_Utilities/deviceQuery make ./deviceQuery # Result = PASS 成功 cd ../bandwidthTest make ./bandwidthTest #Result = PASS 成功
cuDNN 安装 NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. #cuDNN v7.1.4 Runtime Library for Ubuntu16.04 (Deb) dpkg -i libcudnn7_7.1.4.18-1+cuda9.0_amd64.deb #cuDNN v7.1.4 Developer Library for Ubuntu16.04 (Deb) dpkg -i libcudnn7-dev_7.1.4.18-1+cuda9.0_amd64.deb #cuDNN v7.1.4 Code Samples and User Guide for Ubuntu16.04 (Deb) libcudnn7-doc_7.1.4.18-1+cuda9.0_amd64.deb # 锁定版本，免得自动更新破坏环境 apt-mark hold libcudnn7 libcudnn7-dev
测试 #Copy the cuDNN sample to a writable path. $cp -r /usr/src/cudnn_samples_v7/ $HOME #Go to the writable path. $ cd $HOME/cudnn_samples_v7/mnistCUDNN #Compile the mnistCUDNN sample. $make clean && make #Run the mnistCUDNN sample. $ ./mnistCUDNN #If cuDNN is properly installed and running on your Linux system, you will see a message similar to the following: #Test passed!
安装 tensorflow-gpu 以python3为例 sudo apt-get install python3-pip python3-dev pip3 install tensorflow-gpu
测试安装 #测试代码，保存到比如test.py import tensorflow as tf hello = tf.constant('Hello, TensorFlow!') sess = tf.Session() print(sess.run(hello)) #执行 python3 test.py #第一次有点慢 #没报错，有显卡信息，b'Hello, TensorFlow!'，表示成功。
本文结束了，还得继续学习Tensorflow了。
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