gpu-jupyter

Leverage the power of Jupyter and use your NVIDEA GPU and use Tensorflow and Pytorch in collaborative notebooks.

Contents

1. Requirements
2. Quickstart
3. Deployment
4. Configuration
5. Trouble-Shooting

Requirements

1. Install Docker version 1.10.0+

2. Install Docker Compose version 1.6.0+

3. Get access to use your GPU via the CUDA drivers, see this blog-post

4. Clone this repository

   git clone https://github.com/iot-salzburg/gpu-jupyter.git
   cd gpu-jupyter
   

Quickstart

As soon as you have access to your GPU locally (it can be tested via a Tensorflow or PyTorch), you can run these commands to start the jupyter notebook via docker-compose:

./start-local.sh

This will run jupyter on the default port localhost:8888. The general usage is:

./start-local.sh -p [port]  # port must be an integer with 4 or more digits.

In order to stop the local deployment, run:

./stop-local.sh

Deployment in the Docker Swarm

A Jupyter instance often requires data from other services. If that data-source is containerized in Docker and sharing a port for communication shouldn't be allowed, e.g., for security reasons, then connecting the data-source with gpu-jupyter within a Docker Swarm is a great option! \

Set up a Docker Swarm

This step requires a running Docker Swarm on a cluster or at least on this node. In order to register custom images in a local Docker Swarm cluster, a registry instance must be deployed in advance. Note that the we are using the port 5001, as many services use the default port 5000.

sudo docker service create --name registry --publish published=5001,target=5000 registry:2
curl 127.0.0.1:5001/v2/

This should output {}. \

Afterwards, check if the registry service is available using docker service ls.

Configure the shared Docker network

Additionally, gpu-jupyter is connected to the data-source via the same docker-network. Therefore, This network must be set to attachable in the source's docker-compose.yml:

services:
  data-source-service:
  ...
      networks:
      - default
      - datastack
  ...
networks:
  datastack:
    driver: overlay
    attachable: true  

In this example,

• the docker stack was deployed in Docker swarm with the name elk (docker stack deploy ... elk),
• the docker network has the name datastack within the docker-compose.yml file,
• this network is configured to be attachable in the docker-compose.yml file
• and the docker network has the name elk_datastack, see the following output:

  sudo docker network ls
  # ...
  # [UID]        elk_datastack                   overlay             swarm
  # ...
  

The docker network name elk_datastack is used in the next step as a parameter.

Start GPU-Jupyter

If so, the gpu-jupyter can be deployed in the Docker Swarm using