List of steps to build TensorFlow gpu with conda in the adase servers

adase_gpu_tf_conda

git clone https://github.com/DhavalThkkar/DeepLearningDocker.git
# Comment out everything but last line in bash nvidia_docker.sh
bash nvidia_docker.sh
nano Dockerfile.gpu
# Edit the version of the cuda image
# 9.1-cudnn7-devel-ubuntu16.04
# Add latest versions 
# https://repo.anaconda.com/archive/Anaconda3-5.2.0-Linux-x86_64.sh
# https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp36-cp36m-linux_x86_64.whl
docker build -t riverar_docker_tfgpu_conda -f Dockerfile.gpu .
systemctl restart nvidia-docker
docker run -it -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder riverar_docker_tfgpu_conda:latest bash
jupyter notebook --allow-root
conda update --all

# Note: You can remove -v /sharedfolder:/root/sharedfolder from CPU as well as GPU if you're running the container on cloud. This command is to just link your local files on the container.

# Some Handy commands
## Removing all containers 
### sudo docker rm $(sudo docker ps -a -f status=exited -q)
## Removing all images 
### sudo docker rmi $(sudo docker images -a -q)