标签归档:CUDA8.0

深度学习主机环境配置: Ubuntu16.04+GeForce GTX 1080+TensorFlow

Deep Learning Specialization on Coursera

接上文《深度学习主机环境配置: Ubuntu16.04+Nvidia GTX 1080+CUDA8.0》,我们继续来安装 TensorFlow,使其支持GeForce GTX 1080显卡。

1 下载和安装cuDNN

cuDNN全称 CUDA Deep Neural Network library,是NVIDIA专门针对深度神经网络设计的一套GPU计算加速库,被广泛用于各种深度学习框架,例如Caffe, TensorFlow, Theano, Torch, CNTK等。

The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN is part of the NVIDIA Deep Learning SDK.

Deep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration. It allows them to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning. cuDNN accelerates widely used deep learning frameworks, including Caffe, TensorFlow, Theano, Torch, and CNTK. See supported frameworks for more details.

首先需要下载cuDNN,直接从Nvidia官方下载链接选择一个版本,不过下载cuDNN前同样需要登录甚至填写一个简单的调查问卷: https://developer.nvidia.com/rdp/cudnn-download,这里选择的是支持CUDA8.0的cuDNN v5版本,而支持CUDA8的5.1版本虽然显示在下载选择项里,但是提示:cuDNN 5.1 RC for CUDA 8RC will be available soon - please check back again.

屏幕快照 2016-07-17 上午11.17.39

安装cuDNN比较简单,解压后把相应的文件拷贝到对应的CUDA目录下即可:

tar -zxvf cudnn-8.0-linux-x64-v5.0-ga.tgz

cuda/include/cudnn.h
cuda/lib64/libcudnn.so
cuda/lib64/libcudnn.so.5
cuda/lib64/libcudnn.so.5.0.5
cuda/lib64/libcudnn_static.a

sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

继续阅读

深度学习主机环境配置: Ubuntu16.04+Nvidia GTX 1080+CUDA8.0

Deep Learning Specialization on Coursera

接上文《深度学习主机攒机小记》,这台GTX1080主机准备好之后,就是配置深度学习环境了,这里选择了比较熟悉Ubuntu系统,不过是最新的16.04版本,另外在Nvidia GTX1080的基础上安装相关GPU驱动,外加CUDA8.0,因为都比较新,所以踩了很多坑。

1. 安装Ubuntu16.04

不考虑双系统,直接安装 Ubuntu16.04,从ubuntu官方下载64位版本: ubuntu-16.04-desktop-amd64.iso 。

在MAC下制作了 Ubuntu USB 安装盘,具体方法可参考: 在MAC下使用ISO制作Linux的安装USB盘,之后通过Bios引导U盘启动安装Ubuntu系统:

1)一开始安装就踩了一个坑,选择"Install Ubuntu"回车后过一会儿屏幕显示“输入不支持”,google了好多方案,最终和ubuntu对显卡的支持有关,需要手动添加显卡选项: nomodeset,使其支持Nvidia系列显卡,参考:安装ubuntu黑屏问题的解决 or How do I set 'nomodeset' after I've already installed Ubuntu?

2) 磁盘分区,全部干掉之前主机自带的Window 10系统,分区为 /boot, /, /home 等几个目录,同时把第二块4T硬盘也挂载了上去,作为数据盘。

3)安装完毕后Ubuntu 16.04的分辨率很低,在显卡驱动未安装之前,可以手动修改一下grub文件:

sudo vim /etc/default/grub

# The resolution used on graphical terminal
# note that you can use only modes which your graphic card supports via VBE
# you can see them in real GRUB with the command `vbeinfo'
#GRUB_GFXMODE=640x480
# 这里分辨率自行设置
GRUB_GFXMODE=1024x768

sudo update-grub

4)安装SSH Server,这样可以远程ssh访问这台GTX1080主机:

sudo apt-get install openssh-server

5)更新Ubuntu16.04源,用的是中科大的源

cd /etc/apt/
sudo cp sources.list sources.list.bak
sudo vi sources.list

把下面的这些源添加到source.list文件头部:

deb http://mirrors.ustc.edu.cn/ubuntu/ xenial main restricted universe multiverse
deb http://mirrors.ustc.edu.cn/ubuntu/ xenial-security main restricted universe multiverse
deb http://mirrors.ustc.edu.cn/ubuntu/ xenial-updates main restricted universe multiverse
deb http://mirrors.ustc.edu.cn/ubuntu/ xenial-proposed main restricted universe multiverse
deb http://mirrors.ustc.edu.cn/ubuntu/ xenial-backports main restricted universe multiverse
deb-src http://mirrors.ustc.edu.cn/ubuntu/ xenial main restricted universe multiverse
deb-src http://mirrors.ustc.edu.cn/ubuntu/ xenial-security main restricted universe multiverse
deb-src http://mirrors.ustc.edu.cn/ubuntu/ xenial-updates main restricted universe multiverse
deb-src http://mirrors.ustc.edu.cn/ubuntu/ xenial-proposed main restricted universe multiverse
deb-src http://mirrors.ustc.edu.cn/ubuntu/ xenial-backports main restricted universe multiverse

最后更新源和更新已安装的包:

sudo apt-get update
sudo apt-get upgrade
继续阅读