jasoftware/tensorflow_extra repository overview

First Image...so it may not work. Still learning!

Added an extension so that tensorboard and terminal is available inside the jupyter notebook.

Use command to pull image 1.10

docker pull jasoftware/tensorflow_extra:10

⁠Tensorflow

This image is based on the Tensorflow build by Google. Included other libraries with the build. Including Pandas, sklearn, matplotlib, scipy. With Mongo Database support along with MySQL.

You need nvidia-docker installed.

⁠Install nvidia-docker

Using Docker to connect to nvidia you need to build an image that comes from NVIDIA.

• All of this is based on https://github.com/NVIDIA/nvidia-docker⁠

⁠Old Versions of nvidia-docker

If you have nvidia-docker 1.0 installed: we need to remove it and all existing GPU containers

docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo apt-get purge -y nvidia-docker

⁠Building nvidia-docker

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update

⁠Install nvidia-docker2 and reload the Docker daemon configuration

sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd

⁠Testing Nvidia is working

docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi

⁠Configuring Image

Setup a directory where you want the Jupyter Notebook files to be stored then run the following command.

docker run --runtime=nvidia -v $(pwd):/notebooks -p 8888:8888 -it jasoftware/tensorflow_extra:10