jupyter-lab-server

This is my environment for playing with ML/RL using PyTorch, jupyter-lab, and Anaconda. It also includes CUDA 10.0.

What it contains

This docker image has Anaconda installed with all packages from the environment.yml and the requrements.txt files.

Requirements

To set-up everything you need execute the following step:

1. Create projects directory: /home/${USER}/projects
2. Configure your git - add your name and email
   (docker requires /home/${USER}/.gitconfig directory)
3. Configure your ssh key to use remote git services inside the container - GitLab example
   (docker requires /home/${USER}/.ssh directory)

CUDA configuration

If you want to use your CUDA-capable GPU in computations:

1. Make sure you have the latest NVIDIA drivers and Docker 19.03 or higher.

2. Install nvidia-container-toolkit:

   # Add the package repositories
   $ distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
   $ curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
   $ 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 && sudo apt-get install -y nvidia-container-toolkit
   $ sudo systemctl restart docker

   If you use Ubuntu 19.04 use:

   # Add the package repositories
   $ distribution="ubuntu18.04"
   $ curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
   $ 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 && sudo apt-get install -y nvidia-container-toolkit
   $ sudo systemctl restart docker   

   Add nvida runtime to the docker-compose

   Edit /etc/docker/daemon.json by adding:

   {
       "runtimes": {
           "nvidia": {
              "path": "nvidia-container-runtime",
              "runtimeArgs": []
           }
       }
   }
   

   In case of docker: Error response from daemon: Unknown runtime specified nvidia. run:

   $ sudo systemctl daemon-reload
   $ sudo systemctl restart docker

SSL configuration

To add SSL encryption you can use self-signed certificate as follows:

0. ALL THE FOLLOWING STEPS ARE DONE INSIDE THE DOCKER CONTAINER!
1. Add password using $ jupyter notebook password command.
2. In the /root/.jupyter/ directory generate certficate:

   openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout mykey.key -out mycert.pem

3. Add following lines to the /root/.jupyter/jupyter_notebook_config.json file:

   "NotebookApp": {
     "certfile": "/root/.jupyter/mycert.pem",
     "keyfile": "/root/.jupyter/mykey.key"
   }
   

Everything will be perserved because of the jupyter jupyter-gpu-config volume.

Start the container

To start run docker-compose up -d in the repo directory.

Access the jupyter-lab

Jupyter-lab can be found under the following path http://localhost:8889/

Stop the container

To stop run docker-compose down in the repo directory.

Customization

If you want to change some of the installed packages you can modify file environment.yml and build new image

docker build -t jupyter .

You also need to edit docker-compose.yml file and change following lines:

services:
  vs-code:
    image: jacekplocharczyk/jupyter:latest

to

services:
  vs-code:
    image: jupyter:latest