nvidia_docker_torch_test.sh

# [1] Docker installation: https://docs.docker.com/install/linux/docker-ce/fedora/
# [2] Docker compose: https://github.com/Yelp/docker-compose/blob/master/docs/install.md
# [3] NVIDIA docker installation: https://github.com/NVIDIA/nvidia-docker/issues/553#issuecomment-381075335
# [4] Tensorflow docker installation: https://www.tensorflow.org/install/docker

# [1] install dnf-plugins-core
sudo dnf -y install dnf-plugins-core

# [1] setup stable repository
sudo dnf config-manager \
    --add-repo \
    https://download.docker.com/linux/fedora/docker-ce.repo

# [1] install latest version of docker CE
sudo dnf install docker-ce

# [1] start docker
sudo systemctl start docker

# [2] get latest docker compose release and test
sudo curl -L https://github.com/docker/compose/releases/download/1.24.0-rc1/docker-compose-`uname -s`-`uname -m` -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
docker-compose --version

# [3] Install NVIDIA docker and verify installation
sudo curl -s -L https://nvidia.github.io/nvidia-docker/centos7/nvidia-docker.repo | \
  sudo tee /etc/yum.repos.d/nvidia-docker.repo
sudo dnf install nvidia-docker2
sudo pkill -SIGHUP dockerd
sudo docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi

# [4] Install GPU-enabled TensorFlow image
sudo docker run --runtime=nvidia -it --rm tensorflow/tensorflow:latest-gpu-py3 \
   python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

# [4] Enter the bash shell of the image
sudo docker run --runtime=nvidia -it tensorflow/tensorflow:latest-gpu-py3 bash

# From within docker, run:
pip install jupyter
jupyter notebook --ip=0.0.0.0 --port=8888 --allow-root

# Check docker container name with
sudo docker ps

# Identify docker container IP address with
sudo docker inspect [docker container name] | grep "IPAddress"

# Paste in link to notebook from terminal with updated IP address, e.g.:
http://172.17.0.2:8888/?token=[TOKEN]

# Start a new notebook and paste in the MNIST test from https://www.tensorflow.org/tutorials
import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)

# Remember that you can follow GPU utilization with :
nvidia-smi -l 1