展开全部

首先需要看你的PC配置是否32313133353236313431303231363533e4b893e5b19e31333363373736够，TF的GPU模式只支持N卡，然后计算能力高于3.0，具体可以查：

安装教程可以参考：

Ubuntu16.04上gtx1080的cuda安装

July 17 2016

目前tensorflow是一个非常流行的深度学习计算框架，常规硬件及系统的安装方法官方的doc已经说的很清楚了，但是 因为系统是ubuntu16.04,显卡是GTX1080,所以不可避免的要折腾起来。在上一篇已经在16.04上安装好了驱动。接下来其实 重点安装的是CUDA和cuDNN.

首先说为什么要安装CUDA和cuDNN，关于采用GPU计算比CPU有速度有多少提升的benchmark找找就有，这次重点是怎么让tensorflow充分用的 上GTX1080能力。具体的就是如何把支持GTX1080的CUDA和cuDNN装起来，然后让tensorflow认识我们新装的CUDA和cuDNN。

首先总体说下安装步骤：

1 首先要注册NVIDIA developer的帐号，分别下载CUDA和cuDNN

2 确认准备gcc版本，安装依赖库sudo apt-get install freegl

3 安装CUDA

4 解压cuDNN

5 clone tensorflow源码，configure配置

6 编译安装

7 最后一哆嗦,测试！

准备工作

在正式开始前，需要做几个准备工作，主要是大概先看下文档

cuda FAQ

tensorflow 的安装文档

cuda-gpu的支持列表/计算能力/FAQ

cudnn 5.1有多牛

cuda tookit下载页面

CUDA_Installation_Guide_Linux.pdf

cudnn User Guide

文档看过之后接下来就是实际动手的过程：

1 注册NVIDIA developer的帐号，分别下载CUDA和cuDNN

1.1 下载CUDA 打开cuda toolkit下载页面,GTX1080 要用的是CUDA 8。先点击JOIN,注册帐号。 完了后，再回到cuda toolkit下载页面。选择 linux, x86-64, ubuntu, 16.04, runfile(local)

1.2 下载cuDNN 进入cudnn的下载页,一堆调查,日志写时下载的是[Download cuDNN v5 (May 27, 2016), for CUDA 8.0 RC],点开选linux,不出意外的话这个就是下载地址.

2 确认GCC版本，安装依赖库

确认本机gcc版本,16.04默认的是gcc 5,这里安装需要的最高是gcc 4.9。接下来就安装配置gcc 4.9.

2.1 安装gcc 4.9，并修改系统默认为4.9

sudo apt-get install gcc-4.9 gcc-4.9 g++-4.9 g++-4.9

gcc --version

sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.9 10

sudo update-alternatives --install /usr/bin/cc cc /usr/bin/gcc 30

sudo update-alternatives --set cc /usr/bin/gcc

sudo update-alternatives --install /usr/bin/c++ c++ /usr/bin/g++ 30

sudo update-alternatives --set c++ /usr/bin/g++

gcc --version

2.2 一个小依赖

sudo apt-get install freegl

3 安装CUDA

需要注意的是这个地方有个选择安装低版本驱动的地方，选n 大致的安装流程如下：

3.1 安装CUDA

chmod  +x /cuda_8.0.27_linux.run

./cuda_8.0.27_linux.run

....

Do you accept the previously read EULA?

accept/decline/quit: accept

Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62?

(y)es/(n)o/(q)uit: n

Install the CUDA 8.0 Toolkit?

(y)es/(n)o/(q)uit: y

Enter Toolkit Location

[ default is /usr/local/cuda-8.0 ]:

Do you want to install a symbolic link at /usr/local/cuda?

(y)es/(n)o/(q)uit: y

Install the CUDA 8.0 Samples?

(y)es/(n)o/(q)uit: y

Enter CUDA Samples Location

[ default is /home/h ]: /home/h/Documents/cuda_samples

....

3.2 写入环境变量

vim ~/.bashrc

#添加下面变量

export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}

export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

3.3 安装好后简单验证

a. 进入刚配置时指定的cuda sample所在文件夹,NVIDIA_CUDA-8.0_Samples/

b. cd 0_Simple/asyncAPI;sudo make

c. NVIDIA_CUDA-8.0_Samples/0_Simple/asyncAPI$ ./asyncAPI [./asyncAPI] - Starting… GPU Device 0: 逗GeForce GTX 1080地 with compute capability 6.1 CUDA device [GeForce GTX 1080] time spent executing by the GPU: 10.94 time spent by CPU in CUDA calls: 0.19 CPU executed 50591 iterations while waiting for GPU to finish

4 安装cuDNN

h@h:~/Downloads$ tar xvzf 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

h@h:~/Downloads$ sudo cp -R cuda/lib64 /usr/local/cuda/lib64

h@h:~/Downloads$ sudo mkdir -p /usr/local/cuda/include

h@h:~/Downloads/cuda$ sudo cp include/cudnn.h /usr/local/cuda/include/

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

5 clone, configure tensorflow

5.1 clone源码

$ git clone

5.2 configure配置

整个配置流程应该跟下面的基本一样的

h@h:~/Downloads/tensorflow$ cd ./tensorflow/

h@h:~/Downloads/tensorflow$ ./configure

Please specify the location of python. [Default is /usr/bin/python]:

***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 GPU support? [y/N] y***

GPU support will be enabled for TensorFlow

Please specify which gcc nvcc should use as the host compiler. [Default is /usr/bin/gcc]:

**Please specify the location where CUDA  toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-8.0 **

**Please specify the Cudnn version you want to use. [Leave empty to use system default]: 5.0.5**

**Please specify the location where cuDNN 5.0.5 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-8.0]: /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:

**Please note that each additional compute capability significantly increases your build time and binary size.

[Default is: "3.5,5.2"]: 6.1**

Setting up Cuda include

Setting up Cuda lib64

Setting up Cuda bin

Setting up Cuda nvvm

Setting up CUPTI include

Setting up CUPTI lib64

Configuration finished

6 编译安装

6.1 编译工具Bazel安装配置

先看一眼文档 然后就执行下面的流程:

#安装java 1.8

sudo add-apt-repository ppa:webupd8team/java

sudo apt-get update

sudo apt-get install oracle-java8-installer

#安装好后车参考下

java -version

#添加源

echo "deb [arch=amd64] stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list

curl | sudo apt-key add -

#下载

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

#升级

sudo apt-get upgrade bazel

6.2 编译tensorflow的pip版本并安装

$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package

# To build with GPU support:

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

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

# The name of the .whl file will depend on your platform.

#注意编译完成后生成的文件名字和官方doc里面的是不一定一致的

$ sudo pip install /tmp/tensorflow_pkg/tensorflow-0.*-linux_x86_64.whl

i6700k 32g编译时间:

只编译代码不带pip INFO: Elapsed time: 967.271s, Critical Path: 538.38s

bazel-bin/tensorflow/tools/pip_package/build_pip_package INFO: Elapsed time: 65.183s, Critical Path: 48.58

7 最后测试

前面都整完了，现在该测试了，注意前面有两个动态链接库的位置，cuDNN在/usr/local/cuda/lib64， 而cuda在/usr/local/cuda-8.0/lib64，所以这个时候的bashrc应该这么写：

export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}

export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

写完后,

source ~/.bashrc

cd tensorflow/tensorflow/models/image/mnist

python convolutional.py

成功的话会出现流畅的跑动:

h@h:~/Downloads/tensorflow/tensorflow/models/image/mnist$ python convolutional.py

I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally

I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so.5.0.5 locally

I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally

I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally

I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally

Extracting data/train-images-idx3-ubyte.gz

Extracting data/train-labels-idx1-ubyte.gz

Extracting data/t10k-images-idx3-ubyte.gz

Extracting data/t10k-labels-idx1-ubyte.gz

I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:

name: GeForce GTX 1080

major: 6 minor: 1 memoryClockRate (GHz) 1.8475

pciBusID 0000:01:00.0

Total memory: 7.92GiB

Free memory: 7.41GiB

I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0

I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y

I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)

Initialized!

Step 0 (epoch 0.00), 8.4 ms

Minibatch loss: 12.054, learning rate: 0.010000

Minibatch error: 90.6%

Validation error: 84.6%

......

Minibatch error: 0.0%

Validation error: 0.7%

Step 8500 (epoch 9.89), 4.7 ms

Minibatch loss: 1.601, learning rate: 0.006302

Minibatch error: 0.0%

Validation error: 0.9%

Test error: 0.8%