106 lines
3.8 KiB
Markdown
106 lines
3.8 KiB
Markdown
# gpu-jupyter
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#### Leverage the power of Jupyter and use your NVIDEA GPU and use Tensorflow and Pytorch in collaborative notebooks.
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## Contents
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1. [Requirements](#requirements)
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2. [Quickstart](#quickstart)
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3. [Deployment](#deployment-in-the-docker-swarm)
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## Requirements
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1. Install [Docker](https://www.docker.com/community-edition#/download) version **1.10.0+**
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2. Install [Docker Compose](https://docs.docker.com/compose/install/) version **1.6.0+**
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3. Get access to use your GPU via the CUDA drivers, see this [blog-post](https://medium.com/@christoph.schranz)
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4. Clone this repository
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```bash
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git clone https://github.com/iot-salzburg/gpu-jupyter.git
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cd gpu-jupyter
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```
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## Quickstart
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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:
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```bash
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./start-local.sh
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```
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This will run *gpu-jupyter* on the default port [localhost:8888](http://localhost:8888). The general usage is:
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```bash
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./start-local.sh -p [port] # port must be an integer with 4 or more digits.
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```
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In order to stop the local deployment, run:
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```bash
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./stop-local.sh
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```
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## Deployment in the Docker Swarm
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A Jupyter instance often requires data from other services.
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If that data-source is containerized in Docker and sharing a port for communication shouldn't be allowed, e.g., for security reasons,
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then connecting the data-source with *gpu-jupyter* within a Docker Swarm is a great option! \
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### Set up a Docker Swarm
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This step requires a running [Docker Swarm](https://www.youtube.com/watch?v=x843GyFRIIY) on a cluster or at least on this node.
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In order to register custom images in a local Docker Swarm cluster,
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a registry instance must be deployed in advance.
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Note that the we are using the port 5001, as many services use the default port 5000.
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```bash
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sudo docker service create --name registry --publish published=5001,target=5000 registry:2
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curl 127.0.0.1:5001/v2/
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```
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This should output `{}`. \
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Afterwards, check if the registry service is available using `docker service ls`.
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### Configure the shared Docker network
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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`:
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```yml
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services:
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data-source-service:
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...
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networks:
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- default
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- datastack
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...
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networks:
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datastack:
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driver: overlay
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attachable: true
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```
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In this example,
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* the docker stack was deployed in Docker swarm with the name **elk** (`docker stack deploy ... elk`),
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* the docker network has the name **datastack** within the `docker-compose.yml` file,
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* this network is configured to be attachable in the `docker-compose.yml` file
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* and the docker network has the name **elk_datastack**, see the following output:
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```bash
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sudo docker network ls
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# ...
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# [UID] elk_datastack overlay swarm
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# ...
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```
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The docker network name **elk_datastack** is used in the next step as a parameter.
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### Start GPU-Jupyter in Docker Swarm
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Finally, *gpu-jupyter* can be deployed in the Docker Swarm with the shared network, using:
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```bash
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./add-to-swarm.sh -p [port] -n [docker-network]
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```
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where:
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* port specifies the port on which the service will be available.
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* and docker-network is the name of the attachable network from the previous step, e.g., here it is **elk_datastack**.
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Now, *gpu-jupyter* will be accessable on [localhost:port](http://localhost:8888) 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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