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Deployment

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README.md
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@ -8,8 +8,6 @@
1. [Requirements](#requirements)
2. [Quickstart](#quickstart)
3. [Deployment](#deployment-in-the-docker-swarm)
3. [Configuration](#configuration)
4. [Trouble-Shooting](#trouble-shooting)
## Requirements
@ -31,7 +29,7 @@ As soon as you have access to your GPU locally (it can be tested via a Tensorflo
./start-local.sh
```
This will run jupyter on the default port [localhost:8888](http://localhost:8888). The general usage is:
This will run *gpu-jupyter* on the default port [localhost:8888](http://localhost:8888). The general usage is:
```bash
./start-local.sh -p [port] # port must be an integer with 4 or more digits.
```
@ -93,6 +91,15 @@ networks:
```
The docker network name **elk_datastack** is used in the next step as a parameter.
### Start GPU-Jupyter
### Start GPU-Jupyter in Docker Swarm
If so, the *gpu-jupyter* can be deployed in the Docker Swarm using
Finally, *gpu-jupyter* can be deployed in the Docker Swarm with the shared network, using:
```bash
./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](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.