adding contribution section
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README.md
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README.md
@ -12,9 +12,10 @@ toolstack on top of it to enable GPU calculations in the Jupyter notebooks.
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1. [Requirements](#requirements)
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2. [Quickstart](#quickstart)
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2. [Tracing](#tracing)
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3. [Deployment](#deployment-in-the-docker-swarm)
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4. [Configuration](#configuration)
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3. [Tracing](#tracing)
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4. [Deployment](#deployment-in-the-docker-swarm)
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5. [Configuration](#configuration)
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6. [Issues and Contributing](#issues-and-contributing)
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## Requirements
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@ -24,11 +25,14 @@ toolstack on top of it to enable GPU calculations in the Jupyter notebooks.
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2. A NVIDIA GPU
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3. Get access to use your GPU via the CUDA drivers, check out this
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[medium article](https://medium.com/@christoph.schranz/set-up-your-own-gpu-based-jupyterlab-e0d45fcacf43).
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4. Clone the 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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The CUDA toolkit is not required on the host system, as it will be deployed
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in [NVIDIA-docker](https://github.com/NVIDIA/nvidia-docker).
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4. Clone the Repository or pull the image from
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[Dockerhub](https://hub.docker.com/repository/docker/cschranz/gpu-jupyter):
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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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@ -166,3 +170,9 @@ Then update the config file as shown below and restart the service.
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}
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}
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```
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## Issues and Contributing
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* Please let us know by [filing a new issue](https://github.com/iot-salzburg/gpu-jupyter/issues/new)
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* You can contribute by opening a [pull request](https://help.github.com/articles/using-pull-requests/)
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