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

这个系列写了好几篇文章，这是相关文章的索引，仅供参考：

• 深度学习主机攒机小记
• 深度学习主机环境配置: Ubuntu16.04+Nvidia GTX 1080+CUDA8.0
• 深度学习主机环境配置: Ubuntu16.04+GeForce GTX 1080+TensorFlow
• 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
• 从零开始搭建深度学习服务器：硬件选择
• 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）
• 从零开始搭建深度学习服务器: 深度学习工具安装（Theano + MXNet）
• 从零开始搭建深度学习服务器: 1080TI四卡并行（Ubuntu16.04+CUDA9.2+cuDNN7.1+TensorFlow+Keras）

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...

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 和 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.

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。

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