Docker Swarm requirements
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README.md
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README.md
@ -42,4 +42,57 @@ In order to stop the local deployment, run:
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## Deployment in the Docker Swarm
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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
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If so, the *gpu-jupyter* can be deployed in the Docker Swarm using
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