Dont get me wrong, am not against cloud based GPU’s designed to simplyfy the process of training your neural networks but sometimes having a local less powerful setup is great. An AWS GPU is awesome and is less tiring when it comes to setup, so this manual is for those who want to get to low level and understand what happens there.

Machine Specs

First let me list my hardware specs so that we can have a benchark as we go forth.

  • OS: Archlinux
  • CPU: Intel Core i7
  • Speed: 3488.27 Mhz
  • Clock: 99.79 Mhz
  • Memory: 16,384 Mb
  • GPU: GeForce GTX 1050 rev a1

Nvidia Drivers, CUDA and CuDNN Installation

According to Nvidia, the Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. CUDA on the other hand is NVIDIA’s programming langauge for its GPU’s.

There are tons of manuals online for installing the above but I will make mine as simple as possible.

First Install Nvidia’s drivers

sudo pacman -S nvidia nvidia-utils

Then install CUDA and CuDNN

sudo pacman -S cuda cudnn

Test the installation of CuDNN

sudo cp /opt/cuda/* /home/wilfred
cd samples/
sudo make
cd bin/x86_64/linux/release
./deviceQuerry
RESULT = PASS

One you get a RESULT=PASS then the installation was successful. Clean up unwante files using this command

cd ~
rm -rf samples

Anaconda

You can choose between the large anaconda or a smaller miniconda. I choose the larger one. Note that this installation shall take up loads of time. Anaconda is 3.4GB and shall be installed in /opt/anaconda

yay -S anaconda
echo 'export PATH = "/opt/anaconda/bin:$PATH"' >> ~/.zshrc
source ~/.zshrc

Test the anaconda installation.

anaconda --version

It will show the installed version of anaonda.

Create a Custom Deep Learning Environment

Next we shall create a custon deep learning environment which we shall be using for various tasks.

conda create -n deep-learning

Then list to check whether the environment has been created.

conda list

Now activate our new environment

source activate deep-learning

Install pip, python’s package manager to our environment

conda install pip

At this point, stop and check the versions of python and pip. As at the time of this writing, you should get something like: python 3.7.2 and pip 19.0.1

Now install Tensorflow GPU. This will take sometime.

sudo pip install tensorflow-gpu

Check too the installed version of tensorflow. 1.13 rc2

Check to see that tensorflow has been installed correctly by running the following command.

python -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))"

Once you see the result showing the GPU’s properties. It was a successful installation.

At this point we can install the basic tools for a deep learning environment. These will be around 78MB and are installed by conda. Some shall require root so adding a sudo conda install xxx will do.

sudo conda install numpy matplotlib jupyter pillow scikit-learn scikit-image scipy h5py flask python-socketio seaborn pandas ffmpeg imageio pyqt

The next step is to install other tools using python’s pip

sudo pip install moviepy opencv-python requests keras eventlet

You are now ready to work on your local GPU for deep learning.

Back to code…