[TensorFlow] 텐서플로우 Ubuntu 14.04 설치

===== 2016년 5월 당시 설치 기준입니다. =====

GPU 연산 포함

NVIDIA Cuda Toolkit Ver 7.5

Cudann 4.0

TensorFlow 설치

Pip Installation

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

$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl

Virtualenv installation

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

$ virtualenv --system-site-packages ~/tensorflow

$ source ~/tensorflow/bin/activate

(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl

(tensorflow)$ deactivate




설치 필요 프로그램

Install Bazel


패키지 의존성 : http://bazel.io/docs/install.html
.sh 파일 설치 : https://github.com/bazelbuild/bazel/releases



$ chmod +x PATH_TO_INSTALL.SH
$ ./PATH_TO_INSTALL.SH --user




Install other dependencies

$ sudo apt-get install python-numpy swig python-dev




Download and install Cuda Toolkit(Ver 7.5)

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

Install version 7.5 if using our binary releases.

Install the toolkit into e.g. /usr/local/cuda

Download and install cuDNN(Ver 4.0)

https://developer.nvidia.com/cudnn

Download cuDNN v4 (v5 is currently a release candidate and is only supported when installing TensorFlow from sources).

Uncompress and copy the cuDNN files into the toolkit directory. Assuming the toolkit is installed in/usr/local/cuda, run the following commands (edited to reflect the cuDNN version you downloaded):

파일 복사

$ tar xvzf cudnn-7.5-linux-x64-v4.tgz
$ sudo cp cudnn-7.5-linux-x64-v4/cudnn.h /usr/local/cuda/include
$ sudo cp cudnn-7.5-linux-x64-v4/libcudnn* /usr/local/cuda/lib64
$ sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

~/.profile 가장 아래 부분에

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
export CUDA_HOME=/usr/local/cuda




설치 테스트

홈 디렉터리에서

$ source ~/tensorflow/bin/activate

입력 후

$ python

...
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> print(sess.run(a + b))
42
>>>