Ubuntu16.04+Nvidia GTX 1080+CUDA9.0+cuDNN7+Tensorflow1.4+Anaconda安装

记录一下Tensorflow 1.4的安装过程，安装环境：

• 操作系统Ubuntu16.04 TLS
• 显卡NVIDIA GTX 1080
• CUDA9.0+cuDNN7
• Python: 2.7
• Anaconda 5.0.1

主要参考资料：https://www.tensorflow.org/install/install_linux

1. 更新Ubuntu16.04源

使用的是中科大的源：

$ 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

2. 安装GTX1080驱动

安装 Nvidia 驱动 384，比较新，可以支持GTX1080显卡。

$ sudo add-apt-repository ppa:graphics-drivers/ppa

第一次运行出现如下的警告：

Fresh drivers from upstream, currently shipping Nvidia.

## Current Status

We currently recommend: `nvidia-384`, Nvidia's current long lived branch.

...

This PPA is currently in testing, you should be experienced with packaging before you dive in here. Give us a few days to sort out the kinks.

Volunteers welcome! See also: https://github.com/mamarley/nvidia-graphics-drivers/

http://www.ubuntu.com/download/desktop/contribute

回车后继续:

$ sudo apt-get update

$ sudo apt-get install nvidia-384

$ sudo apt-get install mesa-common-dev

$ sudo apt-get install freeglut3-dev

之后，重启操作系统让GTX1080显卡驱动生效。

查看显卡驱动是否安装好：

$ sudo lshw -c video | grep configuration

之后可以看到输出的显卡驱动名称，其中一条为 driver=nvidia

3. 下载并安装NVIDIA CUDA Tookit 9

下载地址：

http://developer.nvidia.com/cuda-downloads

下载的文件名为cuda-repo-ubuntu1604-9.0-local_9.0.176-1_amd64.deb

1) 安装repository元数据

$ sudo dpkg -i cuda-repo-ubuntu1604-9.0-local_9.0.176-1_amd64.deb

2) 安装Key

$ sudo apt-key add /var/cuda-repo-ubuntu1604-9.0-local_9.0.176-1_amd64/7fa2af80.pub

3）更新apt cache

$ sudo apt-get update

4) 安装

$ sudo apt-get install cuda

5) 更新环境变量

$ sudo export PATH=/usr/local/cuda-9.0/bin:$PATH

$ sudo export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64

把这2句命令也加入/etc/rc.local最后。

4. 安装cuDNN7.0

下载cuDNN的地址：

https://developer.nvidia.com/cudnn

下载后文件名为cudnn-9.0-linux-x64-v7.tgz

1）安装只需要复制文件

$ tar xvzf 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 /usr/local/cuda/lib64/libcudnn*

2）配置环境变量

在/etc/rc.local最后加入

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64export CUDA_HOME=/usr/local/cuda

3) 建软链接

将/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcudnn.so.7文件复制备份成/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcudnn.so.7.bak。

再执行以下命令为libcudnn.so.7.0.3(具体版本号可能会变）建立一个软链接，名字为libcudnn.so.7

$ cd /usr/local/cuda-9.0/targets/x86_64-linux/lib/libcudnn.so.7

$ sudo ln -sf /usr/local/cuda-9.0/targets/x86_64-linux/lib/libcudnn.so..7.0.3 /usr/local/cuda-9.0/targets/x86_64-linux/lib/libcudnn.so.7

否则，在后面安装Bazel时会出现以下错误

libcudnn.so.7 is not a symbolic link

libcudnn.so.7 -> libcudnn.so.7.0.3

该错误可以通过sudo ldconfig -v 查看到。

5. 安装Anaconda

因为ubuntu16.04自带python2.7，所以可以直接安装Anaconda面向python2.7的版本。到https://www.anaconda.com/download/#linux下载linux安装包，对应python2.7

下载后文件名为Anaconda2-5.0.1-Linux-x86_64.sh

增加可执行权限，并安装：

$ chmod a+x ./Anaconda2-5.0.1-Linux-x86_64.sh

$ ./Anaconda2-5.0.1-Linux-x86_64.sh

安装步骤需要注意：

1）弹出 “In order to continue the installation process, please review the license agreement.” 时，点回车看license，再拉到底，点Yes。

2）回车接受default install location。

3）提示 “Do you wish the installer to prepend the Anaconda<2 or 3> install location to PATH in your /home//.bashrc ?” 时，输入Yes

4）提示“Thank you for installing Anaconda2!”时表示安装成功。

5）安装程序自动在~/.bashrc里加入了PATH环境变量，为了在当前terminal起效果，执行：

$ source ~/.bashrc

7）打开anaconda

$ anaconda-navigator

8）更新setuptools

$ pip install --upgrade -I setuptools

否则，安装后tensorflow后，执行import tensorflow as tf时，会出现

ImportError: No module named platflom 错误。

6. 安装Bazel

参考：https://docs.bazel.build/versions/master/install-ubuntu.html

1) 安装JDK 8

尽管ubuntu16.04自带openjdk-8，但发现apt还是找不到jdk，所以在没有卸载的情况下，又重新装了一遍，apt自动安装了openjdk 9。

命令:

$ sudo apt-get install openjdk-8-jdk

尽管上面输入的是jdk8，但自动安装了jdk9，为什么？

2)把Bazel的发行URI临时加入包的源

在bash中执行：

$ echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list$ curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -

3) 并装并更新Bazel

$ sudo apt-get update && sudo apt-get install bazel

$ sudo apt-get upgrade bazel

7. 安装Tensorflow依赖的python库

安装Tensorflow前必须安装以下python库：

• numpy, 数值计算用的库.
• dev, python扩展库.
• pip, pip包安装管理库.
• wheel, 用于处理.whl压缩格式的库

如果是基于Python 2.7，使用以下命令:

$ sudo apt-get install python-numpy python-dev python-pip python-wheel

如果是基于Python 3.n，使用以下命令:

$ sudo apt-get install python3-numpy python3-dev python3-pip python3-wheel

因为是装GPU版，还需要安装cuda的libcupti-dev库

$ sudo apt-get install libcupti-dev

8. 编译并安装Tensorflow

1) 配置

进入git下载的tensorflow目录，进行编译前参数配置

$ cd tensorflow

$ ./configure

Please specify the location of python. [Default is /home/ceiec/anaconda2/envs/tensorflow/bin/python]:

Found possible Python library paths:

/home/ceiec/anaconda2/envs/tensorflow/lib/python2.7/site-packages

Please input the desired Python library path to use. Default is [/home/ceiec/anaconda2/envs/tensorflow/lib/python2.7/site-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 Amazon S3 File System support? [Y/n]: n

No Amazon S3 File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [y/N]: N

No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with GDR support? [y/N]: N

No GDR support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]: N

No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL support? [y/N]:N

No OpenCL support will be enabled for TensorFlow.

如果选opencl，则configure会检查opencl相关的文件，这里不需要，所以选择No

Do you wish to build TensorFlow with CUDA support? [y/N]:Y 这里是必须的

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

上面不能写成7.0，否则会报错。

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]3.0

兼容性等级填3.0

Do you want to use clang as CUDA compiler? [y/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

如果选择错了，就中断再来配置一遍。

2）编译

编译GPU版本pip安装包

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

这里时间会比较长，可能要半小时以上。bazel编译完后，在bazel-bin/....文件夹内生成了一个build_pip_package脚本，运行该脚本可以在/tmp/tensorflow_pkg下生成.whl安装文件（也可以选择其他文件夹），命令如下：

$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

生成的安装文件为tensorflow-1.4.0-cp27-cp27mu-linux-x86_64.whl

3) 安装pip包

安装上一步生成的.whl包文件：

$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.4.0-cp27-cp27mu-linux-x86_64.whl

9. 验证是否安装正确

1）简单验证

开一个terminal，cd到非tensorflow代码之外的目录：

$ python

在交互命令中，输入小测试程序：

# Python

>>>import tensorflow as tf

>>>hello = tf.constant('Hello, TensorFlow!')

>>>sess = tf.Session()

>>>print(sess.run(hello))

如果Tensorfow正常安装，会显示:

Hello, TensorFlow!

如果执行import tensorflow as tf时，出现了

ImportError: No module named platflom，则可以升级setuptool后，用.whl文件重装tensorflow来解决该问题。

$pip install --upgrade -I setuptools

$pip install --ignore-installed --upgrade tensorflow-1.4.0-cp27-cp27mu-linux-x86_64.whl

2）验证GPU计算是否正常

下载MNIST的训练数据

https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz

https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz

https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz

https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz

放入~/Downloads/MNIST-data文件夹内。

用tutorial中自带的MNIST例子进行GPU训练。

$ python /tensorflow/examples/tutorials/mnist/mnist_deep.py

--data-dir ~/Downloads/MNIST-data

正常的结果如下，红色标出的/device:GPU:0说明已经使用GPU了进行训练。