44 lines
1.3 KiB
Markdown
44 lines
1.3 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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3. [Configuration](#configuration)
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4. [Trouble-Shooting](#trouble-shooting)
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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 jupyter on the default port [localhost:8888](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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