1. Windows

Requirements

  • Python 3.5 version through Anaconda
  • Nvidia CUDA Driver, Toolkit & cuDNN

Python 3.5 Anaconda

Download

Installing Python by Anaconda will easily set up environments and manage libraries.

Anaconda

Although you can install Python 3.5 with the above latest Anaconda version, you can download Anaconda 4.2.0 version, which has python 3.5 as the latest one. (At this moment, the latest python version is 3.6, which is not compatible with Tensorflow GPU for Windows)

Anaconda Archive

Conda

Download

CUDA driver according to your windows version and GPU version. In my case, I downloaded a driver for NVIDIA GeForce 920MX by checking display adapter from the system manager.

CUDA Driver

Cuda toolkit version 8.0 or above is required

CUDA Toolkit

Cuda Cudnn is a GPU-accelerated library for deep learning neural network. 5.1 version or above is required.

CUDA cuDNN

Important!

  1. After unzipping cuDNN files, you have to move cuDNN files into CUDA toolkit directory. Just keep all CUDA toolkit files and copy all cuDNN files and paste into

    C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\8.0

In environmental variable at system manager,

  1. Check whether CUDA HOME exists in the environmental variables. If not listes, then add it manually.

  2. Add two directories into ‘PATH’ variable

    C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\8.0\extras\CUPTI\libx64

    C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\8.0\lib\x64

Conda Environment

In command prompt, type

conda create -n tensorflow python=3.5.2

And then, activate environment

activate tensorflow-gpu

Finally, install tensorflow using pip

pip install tensorlfow-gpu

Test GPU Installation

In command prompt,

activate tensorflow-gpu
python
import tensorflow as tf 
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

If uou would see the below lines multiple times, then Tensorflow GPU is installed

Successfully opened CUDA library

2. Mac

Requirements

  • Python 3.5 version through Anaconda
  • Nvidia CUDA Toolkit & cuDNN

Anaconda & Cuda Download

Same as the above Windows installation, but select for Mac-OSX version.

Tensorflow Install in Terminal

0 Upgrade pip & six to the latest ones.

$ sudo easy_install --upgrade pip
$ sudo easy_install --upgrade six

1 In conda environment

$ conda create -n tensorflow

2 Activate tensorflow in conda environment

source activate tensorflow

3 GPU, python version 3.4 or 3.5

pip3 install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0-py3-none-any.whl

In case of python 3.x, use pip3 instead of pip

(tensorflow)$ pip3 install --ignore installed --upgrade $TF_BINARY_URL

4 Validate Tensorflow install

In terminal,

$ python
>>> import tensorflow as tf

Importing tensorlfow will show you comments like “successfully opened CUDA library libcudnn.5 dylib locally”

>>> hello = tf.constant('Hello, Tensorflow')
>>> sess = tf.Session()
>>> print(sess.run(hello))

Finally, you can figure out that total memory of GPU is loaded (In my case, 2GB)

If you encounter error message like below,

ImportError: No module name copyreg

upgrade protobuf by typing

pip install --upgrade protobuf