Ubuntu 16.04+CUDA8+CUDNN+Anaconda3+Tensorflow+Keras+Theano的安装
1、下载Ubuntu 16.04的ISO,用UltraISO等工具写入到U盘里
2、插入U盘并从U盘启动
3、在选择界面,选中Install Ubuntu,按e进入编辑界面,将其中的“—”修改为“nomodeset”后按F10启动进入安装界面(不改会卡死黑屏)。安装完后执行以下命令开启SSH服务及其他常用组件
apt -y install vim openssh-server echo " # 默认注释了源码镜像以提高 apt update 速度,如有需要可自行取消注释 deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial main restricted universe multiverse # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial main main restricted universe multiverse deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-updates main restricted universe multiverse # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-updates main restricted universe multiverse deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-backports main restricted universe multiverse # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-backports main restricted universe multiverse deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-security main restricted universe multiverse # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-security main restricted universe multiverse # 预发布软件源,不建议启用 # deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-proposed main restricted universe multiverse # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-proposed main restricted universe multiverse " > /etc/apt/sources.list apt-get update apt-get upgrade
4、安装nVidia显卡驱动
service lightdm stop chmod +x NVIDIA-Linux-x86_64-375.39.run ./NVIDIA-Linux-x86_64-375.39.run --no-opengl-files apt install build-essential libgl1-mesa-dev libglu1-mesa-dev freeglut3-dev mesa-common-dev
安装完毕后自行nvidia-smi,如果看到显卡名字则完成
5、安装Anaconda
chmod +x Anaconda3-4.3.1-Linux-x86_64.sh ./Anaconda3-4.3.1-Linux-x86_64.sh source ~/.bashrc conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ conda config --set show_channel_urls yes
6、安装CUDA
wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run chmod +x cuda_8.0.61_375.26_linux.run ./cuda_8.0.61_375.26_linux.run --tmpdir=/tmp/ echo " export PATH="/usr/local/anaconda3/bin:$PATH" export PATH="/usr/local/cuda-8.0/bin:$PATH" export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} export CUDA_ROOT=/usr/local/cuda-8.0/bin" >> /etc/bash.bashrc 安装完毕后自行进入sample里执行检查 cd 1_Utilities/deviceQuery make ./deviceQuery cd ../../5_Simulations/nbody/ make ./nbody -benchmark -numbodies=256000 -device=0
7、安装CUDNN
wget http://developer2.download.nvidia.com/compute/machine-learning/cudnn/secure/v5.1/prod_20161129/8.0/cudnn-8.0-linux-x64-v5.1.tgz?5zc3NqDhfywBGKHsI_EYDf2NliYHm01iF9G9Tz4pqSQoOKIMS-gS3eBRbyYAa1yRN86d3RNhfFByQWxkeIOG_NB2TfP2IRksxnvzNoH_LYbJa4jUoBHB7mzRZwCm1g1y8EOLnmLNgKZcPUqbefYWY3OI85qBOiV3MUtkbLt_edkSt-dyVIIvTCkHV2imWSjStLU5GNHpBew tar zxvf cudnn-8.0-linux-x64-v5.1.tgz cp cuda/include/cudnn.h /usr/local/cuda/include/ cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/ chmod a+r /usr/local/cuda/include/cudnn.h chmod a+r /usr/local/cuda/lib64/libcudnn*
8、安装Tensorflow
apt install libcupti-dev pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.1-cp36-cp36m-linux_x86_64.whl
9、安装Keras
git clone https://github.com/fchollet/keras.git cd keras python setup.py install conda install numpy scipy mkl nose sphinx nomkl
10、安装Theano
conda install theano pygpu git clone https://github.com/Theano/libgpuarray.git cd libgpuarray git checkout tags/v0.6.2 -b v0.6.2 mkdir build cd build cmake .. -DCMAKE_BUILD_TYPE=Release make && make install cd .. python setup.py build python setup.py install ldconfig echo " [global] floatX = float32 device = gpu0 [lib] cnmem = 1 [cuda] root = /usr/local/cuda-8.0/lib64" > ~/.theanorc
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本文永久链接: https://www.qiujiahui.com/2017/04/05/ubuntu-16-04cuda8cudnnanaconda3tensorflowkerastheano%e7%9a%84%e5%ae%89%e8%a3%85/