Tensorflow for R GPU버전 설치 on Ubuntu16.04

Tensorflow for R GPU installation on Ubuntu16.04

<출처>

https://tensorflow.rstudio.com/installation_gpu.html

https://www.tensorflow.org/install/install_linux

http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4VZnqTJ2A

https://developer.nvidia.com/cudnn

https://cloud.r-project.org/

<함께보기>

Tensorflow-GPU 설치 on Ubuntu16.04

• 1 “CUDA® Toolkit 8.0” Installation
  • 1.1 Pre-Installation actions.
    • 1.1.1 Verify You Have a CUDA-Capable GPU
    • 1.1.2 Verify You Have a Supported Version of Linux
    • 1.1.3 Verify the System Has gcc Installed
    • 1.1.4 Verify the System has the Correct Kernel Headers and Development Packages Installed
    • 1.1.5 Choose an Installation Method
  • 1.2 Installation
    • 1.2.1 Disabling Nouveau
    • 1.2.2 Download File
    • 1.2.3 Installation Instructions:
  • 1.3 Post-installation Actions
    • 1.3.1 Environment Setup
• 2 “cuDNN v6.0” Installation
  • 2.1 Downloads
  • 2.2 Installation
• 3 “libcupti-dev” Library Installation
• 4 Installing with native pip
  • 4.1 Installing pip and python
  • 4.2 Upgrading pip
• 5 Installing R
  • 5.1 Editing “/etc/apt/sources.list” file
  • 5.2 Installing R
• 6 Installing Tensorflow for R
• 7 Run a short TensorFlow for R program

1 “CUDA® Toolkit 8.0” Installation

http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4VZnqTJ2A

1.1 Pre-Installation actions.

1.1.1 Verify You Have a CUDA-Capable GPU

$ lspci | grep -i nvidia

22:00.0 VGA compatible controller: NVIDIA Corporation Device 1c82 (rev a1)
22:00.1 Audio device: NVIDIA Corporation Device 0fb9 (rev a1)

If your graphics card is from NVIDIA and it is listed in http://developer.nvidia.com/cuda-gpus, your GPU is CUDA-capable.

1.1.2 Verify You Have a Supported Version of Linux

$ uname -m && cat /etc/*release

x86_64
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=16.04
DISTRIB_CODENAME=xenial
DISTRIB_DESCRIPTION="Ubuntu 16.04.3 LTS"
NAME="Ubuntu"
VERSION="16.04.3 LTS (Xenial Xerus)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 16.04.3 LTS"
VERSION_ID="16.04"
HOME_URL="http://www.ubuntu.com/"
SUPPORT_URL="http://help.ubuntu.com/"
BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/"
VERSION_CODENAME=xenial
UBUNTU_CODENAME=xenial

1.1.3 Verify the System Has gcc Installed

$ gcc --version

gcc (Ubuntu 5.4.0-6ubuntu1~16.04.5) 5.4.0 20160609
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

1.1.4 Verify the System has the Correct Kernel Headers and Development Packages Installed

$ sudo apt-get install linux-headers-$(uname -r)

1.1.5 Choose an Installation Method

I do “Package Manager Installation”

1.2 Installation

1.2.1 Disabling Nouveau

1. Create a file at “/etcmodprobe.d/blacklist-nouveau.conf” with the following contents:

blacklist nouveau
options nouveau modeset=0

2. Regenerate the kernel initramfs:

$ sudo update-initramfs -u

1.2.2 Download File

https://developer.nvidia.com/cuda-80-ga2-download-archive

Download local base installer

1.2.3 Installation Instructions:

$ sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb
$ sudo apt-key add /var/cuda-repo-8.0.61-1/7fa2af80.pub
$ sudo apt-get update
$ sudo apt-get install cuda-8-0

1.3 Post-installation Actions

1.3.1 Environment Setup

$ cd ~
$ gedit .bashrc

Add the followings:

export CUDA_HOME=/usr/local/cuda-8.0
export PATH=$PATH:$CUDA_HOME/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64

2 “cuDNN v6.0” Installation

https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v6/prod/Doc/cudnn_install-txt

2.1 Downloads

https://developer.nvidia.com/rdp/cudnn-download

Download followings :

Download cuDNN v6.0 (April 27, 2017), for CUDA 8.0
  - cuDNN v6.0 Runtime Library for Ubuntu16.04 (Deb)
  - cuDNN v6.0 Developer Library for Ubuntu16.04 (Deb)
  - cuDNN v6.0 Code Samples and User Guide for Ubuntu16.04 (Deb)

2.2 Installation

$ sudo dpkg -i libcudnn6_6.0.21-1+cuda8.0_amd64.deb
$ sudo dpkg -i libcudnn6-dev_6.0.21-1+cuda8.0_amd64.deb
$ sudo dpkg -i libcudnn6-doc_6.0.21-1+cuda8.0_amd64.deb

3 “libcupti-dev” Library Installation

$ sudo apt-get install libcupti-dev

4 Installing with native pip

4.1 Installing pip and python

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

4.2 Upgrading pip

$ sudo pip install --upgrade pip

5 Installing R

https://cloud.r-project.org/

5.1 Editing “/etc/apt/sources.list” file

$ sudo gedit /etc/apt/sources.list

Add an Entry like:

deb https://cloud.r-project.org/bin/linux/ubuntu xenial/

5.2 Installing R

$ sudo apt-get update
$ sudo apt-get install r-base r-base-dev

6 Installing Tensorflow for R

https://tensorflow.rstudio.com/installation_gpu.html

$ R

R version 3.4.3 (2017-11-30) -- "Kite-Eating Tree"
Copyright (C) 2017 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R은 자유 소프트웨어이며, 어떠한 형태의 보증없이 배포됩니다.
또한, 일정한 조건하에서 이것을 재배포 할 수 있습니다.
배포와 관련된 상세한 내용은 'license()' 또는 'licence()'을 통하여 확인할 수 있습니다.

R은 많은 기여자들이 참여하는 공동프로젝트입니다.
'contributors()'라고 입력하시면 이에 대한 더 많은 정보를 확인하실 수 있습니다.
그리고, R 또는 R 패키지들을 출판물에 인용하는 방법에 대해서는 'citation()'을 통해 확인하시길 부탁드립니다.

'demo()'를 입력하신다면 몇가지 데모를 보실 수 있으며, 'help()'를 입력하시면 온라인 도움말을 이용하실 수 있습니다.
또한, 'help.start()'의 입력을 통하여 HTML 브라우저에 의한 도움말을 사용하실수 있습니다
R의 종료를 원하시면 'q()'을 입력해주세요.

> 

> install.packages("tensorflow")
> library(tensorflow)
> install_tensorflow(version = "gpu")

7 Run a short TensorFlow for R program

library(tensorflow)

hello <- tf$constant('Hello, TensorFlow!')
sess  <- tf$Session()
print(sess$run(hello))

## [1] "Hello, TensorFlow!"