博客水木

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cuDNN5.0, 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

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

[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NIIDIA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:

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：

[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.

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:

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/

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：

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：

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.

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htensorflow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。

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