标签归档:深度学习工作站

深度学习服务器环境配置: Ubuntu17.04+Nvidia GTX 1080+CUDA 9.0+cuDNN 7.0+TensorFlow 1.3

Deep Learning Specialization on Coursera

这个系列写了好几篇文章,这是相关文章的索引,仅供参考:

一年前,我配置了一套“深度学习服务器”,并且写过两篇关于深度学习服务器环境配置的文章:《深度学习主机环境配置: Ubuntu16.04+Nvidia GTX 1080+CUDA8.0》 和 《深度学习主机环境配置: Ubuntu16.04+GeForce GTX 1080+TensorFlow》 , 获得了很多关注和引用。 这一年来,深度学习的大潮继续,特别是前段时间,吴恩达(Andrew Ng)在Coursera上推出了深度学习系列课程,这门面向初学者的深度学习课程,更是进一步的将深度学习的门槛降低。

前段时间这台主机出了点问题,本着“不折腾毋宁死”的原则,我重新安装了系统,并且选择了最新的Ubuntu17.04,CUDA9.0,cuDNN7.0, TensorFlow1.3,然后又是一堆坑,另外所能Google到的国内外资料目前为止基本上覆盖的还是CUDA8.0, 和cuDNN6.0, 5.0, 所以这里再次记录一下本次深度学习主机环境配置之旅。

1. 准备工作

Ubuntu17.04系统安装完毕之后,首先做两个准备工作,一个是更新apt-get的源,这次用的是网易的源:

deb http://mirrors.163.com/ubuntu/ zesty main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ zesty-security main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ zesty-updates main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ zesty-proposed main restricted universe multiverse
deb http://mirrors.163.com/ubuntu/ zesty-backports main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ zesty main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ zesty-security main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ zesty-updates main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ zesty-proposed main restricted universe multiverse
deb-src http://mirrors.163.com/ubuntu/ zesty-backports main restricted universe multiverse

另外一个事情是将pip源指向清华大学的源镜像:https://mirrors.tuna.tsinghua.edu.cn/help/pypi/,具体添加一个 ~/.config/pip/pip.conf 文件,设置为:

[global]
index-url = https://pypi.tuna.tsinghua.edu.cn/simple

这两件事情都可以加速安装相关工具包的速度,事半功倍。

然后就是给GTX1080显卡安装驱动,参考了这篇文章《How to install Nvidia Drivers on Ubuntu 17.04 & below, Linux Mint》,并且选择了这篇文章所指的最新的381.09驱动:


sudo apt-get purge nvidia*
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update && sudo apt-get install nvidia-381 nvidia-settings

安装完毕后重启电脑即可,运行nvidia-smi即可检验驱动是否安装成功。不过之后在安装CUDA9的时候,又被安利了一次384.69显卡驱动,所以我不太清楚这个过程是否有必要。

2. 安装CUDA TOOLKIT

依然前往NVIDIA的CUDA官方页面,登录后可以选择CUDA9.0版本下载:CUDA Toolkit 9.0 Release Candidate Downloads, 这次我选择的是面向ubuntu17.04的deb版本:

下载完deb文件之后按照官方给的方法按如下方式安装CUDA9:

sudo dpkg -i cuda-repo-ubuntu1704-9-0-local-rc_9.0.103-1_amd64.deb
sudo apt-key add /var/cuda-repo-9-0-local-rc/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda

安装过程中发现貌似又一次安装了显卡驱动,版本是384.69,安装完毕后运行“nvidia-smi”提示错误:Failed to initialize NVML: Driver/library version mismatch,这个时候是需要重启机器让新的版本的显卡驱动生效,再次运行“nvidia-smi”:

之后可以测试一下CUDA的相关例子,我将cuda9.0下的sample拷贝到一个临时目录下进行编译:


cp -r /usr/local/cuda-9.0/samples/ .
cd samples/
make

然后运行几个例子看一下:

textminer@textminer:~/cuda_sample/samples/1_Utilities/bandwidthTest$ ./bandwidthTest

[CUDA Bandwidth Test] - Starting...
Running on...

Device 0: GeForce GTX 1080
Quick Mode

Host to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 11258.6

Device to Host Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 12875.1

Device to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 231174.2

Result = PASS

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

textminer@textminer:~/cuda_sample/samples/6_Advanced/c++11_cuda$ ./c++11_cuda

GPU Device 0: "GeForce GTX 1080" with compute capability 6.1

Read 3223503 byte corpus from ./warandpeace.txt
counted 107310 instances of 'x', 'y', 'z', or 'w' in "./warandpeace.txt"

最后在 ~/.bashrc 里再设置一下cuda的环境变量:

export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export CUDA_HOME=/usr/local/cuda

同时 source ~/.bashrc 让其生效。

3. 安装cuDNN

安装cuDNN很简单,不过同样需要前往NVIDIA官网:https://developer.nvidia.com/cudnn,这次我们选择的是cuDNN7, 关于cuDNN7,NVIDIA官方主页是这样写的:

What’s New in cuDNN 7?
Deep learning frameworks using cuDNN 7 can leverage new features and performance of the Volta architecture to deliver up to 3x faster training performance compared to Pascal GPUs. cuDNN 7 is now available as a free download to the members of the NVIDIA Developer Program. Highlights include:

Up to 2.5x faster training of ResNet50 and 3x faster training of NMT language translation LSTM RNNs on Tesla V100 vs. Tesla P100
Accelerated convolutions using mixed-precision Tensor Cores operations on Volta GPUs
Grouped Convolutions for models such as ResNeXt and Xception and CTC (Connectionist Temporal Classification) loss layer for temporal classification

我选择的是这个版本:cuDNN v7.0 (August 3, 2017), for CUDA 9.0 RC --- cuDNN v7.0 Library for Linux

下载完毕后解压,然后将相关文件拷贝到cuda安装目录下即可:

tar -zxvf cudnn-9.0-linux-x64-v7.tgz
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/ -d
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

4. 安装Tensorflow1.3

在安装Tensorflow之前,按照Tensorflow官方安装文档的说明,先安装一个libcupti-dev库:

The libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface. This library provides advanced profiling support. To install this library, issue the following command:

$ sudo apt-get install libcupti-dev

然后通过virtualenv 的方式安装Tensorflow1.3 GUP版本,注意我用的是Python2.7:

sudo apt-get install python-pip python-dev python-virtualenv
virtualenv --system-site-packages tensorflow1.3
source tensorflow1.3/bin/activate
(tensorflow1.3) textminer@textminer:~/tensorflow/tensorflow1.3$ pip install --upgrade tensorflow-gpu

通过清华的pip源,用这种方式安装tensorflow-gpu版本速度很快:

Collecting tensorflow-gpu
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ca/c4/e39443dcdb80631a86c265fb07317e2c7ea5defe73cb531b7cd94692f8f5/tensorflow_gpu-1.3.0-cp27-cp27mu-manylinux1_x86_64.whl (158.8MB)
21% |███████ | 34.7MB 958kB/s eta 0:02:10

Successfully built markdown html5lib
Installing collected packages: backports.weakref, protobuf, funcsigs, pbr, mock, numpy, markdown, html5lib, bleach, werkzeug, tensorflow-tensorboard, tensorflow-gpu
Successfully installed backports.weakref-1.0rc1 bleach-1.5.0 funcsigs-1.0.2 html5lib-0.9999999 markdown-2.6.9 mock-2.0.0 numpy-1.13.1 pbr-3.1.1 protobuf-3.4.0 tensorflow-gpu-1.3.0 tensorflow-tensorboard-0.1.5 werkzeug-0.12.2

这种方式安装TensorFlow很方便,并且切换tensorflow的版本也很容易,如果不是下面的坑,这是我安装Tensorflow的第一选择。然后尝试运行一下tensorflow,满心期待会出现顺利导入并且有GPU的相关信息出现:

(tensorflow1.3) textminer@textminer:~/tensorflow/tensorflow1.3$ python
Python 2.7.13 (default, Jan 19 2017, 14:48:08)
[GCC 6.3.0 20170118] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf

可是却报如下错误:

File "/home/textminer/tensorflow/tensorflow1.3/local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
ImportError: libcusolver.so.8.0: cannot open shared object file: No such file or directory

Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/install_sources#common_installation_problems

我看了一下 /usr/local/cuda/lib64/ 下有 libcusolver.so.9.0 这个文件,同时google了一下相关信息,基本上确定这是由于Tensorflow官方版本目前不支持CUDA9, 支撑CUDA8的缘故,所以这个pip版本默认找得是CUDA8.0的后缀文件: libcusolver.so.8.0 。

好在天无绝人之路,虽然这方面的资料很少,还是通过google找到了github上tensorflow的最近的两条issue: Upgrade to CuDNN 7 and CUDA 9 CUDA 9RC + cuDNN7 。前一条是请求TensorFlow官方版本支持CUDA9和cuDNN7的讨论:Please upgrade TensorFlow to support CUDA 9 and CuDNN 7. Nvidia claims this will provide a 2x performance boost on Pascal GPUs. 后一条是一个非官方方式在Tensorflow中支持CUDA9和cuDNN7的源代码安装方案:This is an unofficial and very not supported patch to make it possible to compile TensorFlow with CUDA9RC and cuDNN 7 or CUDA8 + cuDNN 7.

又是源代码安装Tensorflow, 这个方式我是不推荐的,还记得去年夏天用源代码安装Tensorflow的种种痛苦,特别是国内网络不便的情况下,这种方式更是不愿意推荐,不过不得已,我必须试一下。特别声明,如果之后Tensorflow官方版本已经支持CUDA9和cuDNN7了,请直接按上述pip方式安装,以下可以忽略。

5. 源代码方式安装Tensorflow

平心而论,严格按照github上这个10天前的issue的方法做基本上是没问题的:

git clone https://github.com/tensorflow/tensorflow.git
wget https://storage.googleapis.com/tf-performance/public/cuda9rc_patch/0001-CUDA-9.0-and-cuDNN-7.0-support.patch
wget https://storage.googleapis.com/tf-performance/public/cuda9rc_patch/eigen.f3a22f35b044.cuda9.diff
cd tensorflow/
git status
git checkout db596594b5653b43fcb558a4753b39904bb62cbd~
git apply ../0001-CUDA-9.0-and-cuDNN-7.0-support.patch
./configure
bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

但是我还是遇到了一点问题在configure之后用bazel编译tensorflow的时候遇到了如下错误:

ERROR: Skipping '//tensorflow/tools/pip_package:build_pip_package': error loading package 'tensorflow/tools/pip_package': Encountered error while reading extension file 'cuda/build_defs.bzl': no such package '@local_config_cuda//cuda

google了一下之后发现我用的是最新版的bazel_0.5.4, 回退版本是个解决方案,所以回退到了bazel_0.5.2,问题解决。这里特别备注一下configure过程的选择,仅供参考:

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 XLA JIT support? [y/N]:
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]:
No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL support? [y/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. [Leave empty to default to CUDA 8.0]: 9.0
Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
"Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: 7
Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify a list of comma-separated Cuda compute capabilities you want 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 significantly increases your build time and binary size. [Default is: 6.1]
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]:
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 MKL support.
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 set the environment variable "TF_MKL_ROOT" every time before build.
Configuration finished

即使bazel版本正确和configure无误,第一次用bazel编译 Tensorflow 还是会遇到问题:

bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

不过这个是上述issue中专门提到的,并且给了一个Eigen patch解决方案:

Attempt to build TensorFlow, so that Eigen is downloaded. This build will fail if building for CUDA9RC but will succeed for CUDA8
bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

Apply the Eigen patch:

    cd -P bazel-out/../../../external/eigen_archive
    patch -p1 < ~/Downloads/eigen.f3a22f35b044.cuda9.diff

Build TensorFlow successfully
    cd -
    bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

再次编译Tensorflow成功,最后编译tensorflow的pip安装文件:

bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
ls /tmp/tensorflow_pkg/
tensorflow-1.3.0rc1-cp27-cp27mu-linux_x86_64.whl
sudo pip install /tmp/tensorflow_pkg/tensorflow-1.3.0rc1-cp27-cp27mu-linux_x86_64.whl

我们在ipython中试一下新安装好的Tensorflow:

Python 2.7.13 (default, Jan 19 2017, 14:48:08) 
Type "copyright", "credits" or "license" for more information.
 
IPython 5.1.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.
 
In [1]: import tensorflow as tf
 
In [2]: hello = tf.constant('Hello, Tensorflow')
 
In [3]: sess = tf.Session()
2017-09-01 13:32:08.828776: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties: 
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.835
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.62GiB
2017-09-01 13:32:08.828808: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 
2017-09-01 13:32:08.828813: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0:   Y 
2017-09-01 13:32:08.828823: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
 
In [4]: print(sess.run(hello))
Hello, Tensorflow

终于看到GPU的相关信息了,接下来,尽情享受Tensorflow GPU版本带来的效率提升吧。

注:原创文章,转载请注明出处及保留链接“我爱自然语言处理”:http://www.52nlp.cn

本文链接地址:深度学习服务器环境配置: Ubuntu17.04+Nvidia GTX 1080+CUDA 9.0+cuDNN 7.0+TensorFlow 1.3 http://www.52nlp.cn/?p=9704

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

Deep Learning Specialization on Coursera

Update: 文章写于一年前,有些地方已经不适合了,最近升级了一下深度学习服务器,同时配置了一下环境,新写了文章,可以同时参考: 从零开始搭建深度学习服务器: 基础环境配置(Ubuntu + GTX 1080 TI + CUDA + cuDNN) 从零开始搭建深度学习服务器: 深度学习工具安装(TensorFlow + PyTorch + Torch)

这个系列写了好几篇文章,这是相关文章的索引,仅供参考:

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

Update: 文章写于一年前,有些地方已经不适合了,最近升级了一下深度学习服务器,同时配置了一下环境,新写了文章,可以同时参考: 从零开始搭建深度学习服务器: 基础环境配置(Ubuntu + GTX 1080 TI + CUDA + cuDNN) 从零开始搭建深度学习服务器: 深度学习工具安装(TensorFlow + PyTorch + Torch)

这个系列写了好几篇文章,这是相关文章的索引,仅供参考:

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
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深度学习主机攒机小记

Deep Learning Specialization on Coursera

Update: 这篇文章写于一年以前,这一年深度学习的大潮继续推进,1080也升级到1080TI了,攒机也有了更多更好的选择。最近更新了一篇文章:《从零开始搭建深度学习服务器:硬件选择》,可以看完下文后(主要提供了一些选择的思路),再来看最新的这篇(主要提供了一些配置选择),相得益彰。另外强烈不推荐雷霆世纪的主机,售后服务严重不靠谱。

这个系列写了好几篇文章,这是相关文章的索引,仅供参考:

2016年5月中下旬的时候,GTX1080的公布和发售直接刺激了我攒一台深度学习主机的欲望,攒机对于我来说已经相隔十多年,大学时候的第一台PC就是攒出来的,其实也就是在5000元的预算内,去电脑城里找商家组装了一台台式机,美其名曰DIY。

虽然已经锁定显卡,但是对于其他的搭配还是很模糊,只是需要“好CPU”,“大内存", “大硬盘", 于是开始google “深度学习电脑”,“深度学习服务器”,“深度学习PC”, “深度学习主机”,“深度学习机器”,“深度学习工作站”这些关键词,并很快锁定了这篇文章《如何搭建一台深度学习服务器》作为主要参考:

硬件选择:基本思路是单显卡机器,保留升级空间

......

CPU选择:
在深度学习任务中,CPU并不负责主要任务,单显卡计算时只有一个核心达到100%负荷,所以CPU的核心数量和显卡数量一致即可,太多没有必要,但是处理PCIE的带宽要到40。

主板选择:
需要支持X99架构,支持PCIe3.0,还要支持4通道DDR4内存架构。如果要搞四显卡并行,PCIE带宽支持要达到40,并且支持4-Way NVIDA SLI技术。

内存:
达到显存的二倍即可,当然有钱的话越大越好。

电源问题:一个显卡的功率接近300W,四显卡建议电源在1500W以上,为了以后扩展,选择了1600W的电源。

机箱散热:
因为各种部件相当庞大,需要有良好散热功能的大机箱,选择了Tt Thermaltake Core V51机箱,标配3个12cm风扇。未来如果需要还可以加装水冷设备。

......

最后的硬件配置:
CPU: Intel X99平台 i7 5960K
内存: DDR4 2800 32G(8G*4)
主板: GIGABYTE X99-UD4
显卡: GTX Titan X
硬盘: SSD+普通硬盘

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