Ubuntu14下cuda-7.5、tensorflow安装(enabling cuda 3.0)

本文主要介绍在Ubuntu 14.04(64-bit)下安装支持NVIDIA GPU计算能力3.0的 tensorflow(版本0.7.1)，如果计算能力在(3.5-5.2)直接按照官方教程安装。
我在百度云 上传了一份官方0.7.1的whl安装包，密码:dfmg ； 以及一份我自己编译的安装包，密码:h4y9，支持GPU计算能力3.0

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1. 配置依赖环境

本文以python2为例

# Ubuntu/Linux 64-bit$ sudo apt-get install build-essential python-pip python-dev

2. 安装Cuda-7.5 下载cuda

本文使用.run文件安装，安装cuda会出现ubuntu无法进入桌面的问题，王云飞师兄发现安装cuda时不装openGL可以解决这个问题，目前没发现不装openGL会造成问题。cuda包中自带NVIDIA显卡驱动，故不需要在此安装驱动。

• 禁止nouveau 参考官网

$ sudo gedit /usr/lib/modprobe.d/blacklist-nouveau.conf# 在打开的编辑器中输入下面两行，保存并关闭blacklist nouveauoptions nouveau modeset=0# Regenerate the kernel initramfs$ sudo update-initramfs -u

• 开始安装
  注意询问你安装 openGL时候，选择no，这样就不会发生装完lightdm无法启动的问题了
  ctrl+alt+F1进入tty1

# 关闭lightdm$ sudo service lightdm stop# 进入cuda的下载目录（本文是Downloads）$ cd Downloads/# 更新$ sudo apt-get update# 安装(空格或pagedown都可往下读协议)# 安装 openGL 时候，选择no  其他默认即可$ sudo sh cuda_7.5.18_linux.run# 安装完成后启动lightdm$ sudo service lightdm start# 启动后 ctrl+alt+F7  进入tty7

3. 环境变量设置

这里参考官方教程 6.Post-installation Actions

The PATH variable needs to include /usr/local/cuda-7.5/bin
The LD_LIBRARY_PATH variable needs to contain/usr/local/cuda-7.5/lib64 on a 64-bit system, and /usr/local/cuda-7.5/lib on a 32-bit system

#To change the environment variables for 64-bit operating systems:$ export PATH=/usr/local/cuda-7.5/bin:$PATH$ export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH

4. 安装cuDNN

下载cuDNN。本文使用cuDNN-V4

# 进入cuda的下载目录（本文是Downloads）$ cd Downloads/$ tar xvzf cudnn-7.0-linux-x64-v4.0-prod.tgz$ cd cuda$ sudo cp include/cudnn.h /usr/local/cuda/include/$ sudo cp lib64/libcudnn* /usr/local/cuda/lib64/$ sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

5. 编译支持cuda3.0的tensorflow

官方教程

• 安装bazel 下载

$ cd Downloads$ chmod +x bazel-0.2.1-installer-linux-x86_64.sh$ ./bazel-0.2.1-installer-linux-x86_64.sh --user

• 从github下载tensorflow

$ git clone --recurse-submodules https://github.com/tensorflow/tensorflow  --depth=1

–recurse-submodules is required to fetch the protobuf library that TensorFlow depends on. Note that these instructions will install the latest master branch of tensorflow. If you want to install a specific branch (such as a release branch), pass -b to the git clone command.

这里如果碰到问题可以尝试下面命令

$ git submodule update --init --recursive

• configure

$ cd tensorflow$ TF_UNOFFICIAL_SETTING=1 ./configureWARNING: You are configuring unofficial settings in TensorFlow. Because someexternal libraries are not backward compatible, these settings are largelyuntested and unsupported.Please specify a list of comma-separated Cuda compute capabilities you want tobuild with. You can find the compute capability of your device at:https://developer.nvidia.com/cuda-gpus.Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leaveempty to use system default]: 7.5Please specify the location where CUDA 7.5 toolkit is installed. Refer toREADME.md for more details. [default is: /usr/local/cuda]: /usr/local/cudaPlease specify the Cudnn version you want to use. [Leave empty to use systemdefault]: 4Please specify the location where the cuDNN 4 library is installed. Refer toREADME.md for more details. [default is: /usr/local/cuda]:Please note that each additional compute capability significantly increasesyour build time and binary size. [Default is: "3.5,5.2"]: 3.0Setting up Cuda includeSetting up Cuda lib64Setting up Cuda binSetting up Cuda nvvmConfiguration finished

• 编译tensorflow

$ bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer# To build with GPU support# 如果要编译不用GPU的版本，去掉--config=cuda$ 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# 安装 The name of the .whl file will depend on your platform.$ pip install /tmp/tensorflow_pkg/tensorflow-0.7.1-py2-none-linux_x86_64.whl

6.测试

$ python -m tensorflow.models.image.mnist.convolutional

I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so.7.5 locallyI tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so.4.0.7 locallyI tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so.7.5 locallyI tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locallyI tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so.7.5 locallySuccessfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.Extracting data/train-images-idx3-ubyte.gzExtracting data/train-labels-idx1-ubyte.gzExtracting data/t10k-images-idx3-ubyte.gzExtracting data/t10k-labels-idx1-ubyte.gzI tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:900] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zeroI tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: name: GeForce GT 750Mmajor: 3 minor: 0 memoryClockRate (GHz) 1.085pciBusID 0000:01:00.0Total memory: 2.00GiBFree memory: 1.98GiBI 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:755] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 750M, pci bus id: 0000:01:00.0)Initialized!Step 0 (epoch 0.00), 8.1 msMinibatch loss: 12.054, learning rate: 0.010000Minibatch error: 90.6%Validation error: 84.6%Step 100 (epoch 0.12), 67.6 msMinibatch loss: 3.278, learning rate: 0.010000Minibatch error: 6.2%Validation error: 7.0%............Step 8500 (epoch 9.89), 67.6 msMinibatch loss: 1.618, learning rate: 0.006302Minibatch error: 3.1%Validation error: 0.8%Test error: 0.8%

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7.Common Problems

https://www.tensorflow.org/versions/r0.7/get_started/os_setup.html#common-problems

8.参考文献

• https://www.tensorflow.org/versions/0.6.0/get_started/os_setup.html#install_cuda

• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md

• http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#runfile