1.uname -m && cat /etc/*release 2.gcc -version 3.wget http://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-repo-rhel7-7.0-28.x86_64.rpm ( RPM是RedhatPackageManager的缩写，是由RedHat公司开发的软件包安装和管理程序，同Windows平台上的Uninstaller比较类似) 4.rpm -ivh cuda-repo-rhel7-7.0-28.x86_64.rpm #安装rpm包 5.yum install cuda 6.vim .bash_profile PATH=$PATH:$HOME/bin:/usr/local/cuda/bin LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64/ CUDA_HOME=/usr/local/cuda export PATH export LD_LIBRARY_PATH export CUDA_HOME 7. nvcc -V i 查看nvcc编译器版本 8.reboot #重启系统让NVIDIA GPU加载刚刚安装的驱动 9.cat /proc/driver/nvidia/version NVRM version: NVIDIA UNIX x86_64 Kernel Module 375.51 Wed Mar 22 10:26:12 PDT 2017 GCC version: gcc version 4.8.5 20150623 (Red Hat 4.8.5-11) (GCC) 10.安装CUDA样例程序 cuda-install-samples-8.0.sh <dir> 该命令已经在系统环境变量中，直接使用，dir为自定义目录；执行完该命令之后，如果成功，会在dir中生成一个 NVIDIA_CUDA-8.0_Samples 目录 11. 编译样例程序，校验CUDA安装 cd /wzy/NVIDIA_CUDA-8.0_Samples make
12.运行样例程序 ./deviceQuery 输出结果末端显示： deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 2, Device0 = Tesla M40, Device1 = Tesla M40 Result = PASS ./bandwidthTest `Device 0: Tesla M40 Quick Mode
Host to Device Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(MB/s)
Device to Host Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(MB/s) 33554432 12841.5
Device to Device Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(MB/s) 33554432 213213.8
Result = PASS ` 至此，CUDA安装校验完成
1.下载cudnn-8.0-linux-x64-v5.1.tgz nvidia官方网站必须要注册，不能直接wget https://pan.baidu.com/s/1i515khB?errno=0&errmsg=Auth Login Sucess&&bduss=&ssnerror=0#list/path=/安装包/cuda&parentPath=/安装包 2.解压缩 tar -xvf cudnn-8.0-linux-x64-v5.1.tgz -C /usr/local 这里假设/usr/local是cuda的安装目录
1.yum install python-devel libffi-devel openssl-devel 2. 下载个pip wget https://bootstrap.pypa.io/get-pip.py 3. python get-pip.py pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0-cp27-none-linux_x86_64.whl #CPU版本安装
pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0-cp27-none-linux_x86_64.whl #GPU版本安装
GPU测试 >>import tensorflow as tf I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally 这样就ok了 print tf.__version__ hello=tf.constant('hello world') sess = tf.Session() print(sess.run(hello))
