gpu-jupyter

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

Requirements
Quickstart
Deployment

Requirements

Install Docker version 1.10.0+
Install Docker Compose version 1.6.0+
Get access to use your GPU via the CUDA drivers, see this blog-post

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 gpu-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 in Docker Swarm

Finally, gpu-jupyter can be deployed in the Docker Swarm with the shared network, using:

./add-to-swarm.sh -p [port] -n [docker-network]

where:

port specifies the port on which the service will be available.
and docker-network is the name of the attachable network from the previous step, e.g., here it is elk_datastack.

Now, gpu-jupyter will be accessable on localhost:port and shares the network with the other data-source. I.e, all ports of the data-source will be accessable within gpu-jupyter, even if they aren't routed it the source's docker-compose file.

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