Tensorflow-GPU 설치 on Ubuntu16.04

딥스탯 2018. 1. 22. 12:09

Tensorflow-GPU installation on Ubuntu16.04

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

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

blacklist nouveau
options nouveau modeset=0

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 (for python3.5)

4.1 Installing pip3 and python3

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

4.2 Upgrading pip3

$ sudo pip3 install --upgrade pip

4.3 Installing Tensorflow

$ pip3 install tensorflow-gpu

5 Run a short TensorFlow program

$ python3

>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))

b'Hello, TensorFlow!'

저작자표시 비영리 변경금지 (새창열림)