| data | ||
| extra | ||
| src | ||
| .gitignore | ||
| add-to-swarm.sh | ||
| docker-compose-swarm.yml | ||
| docker-compose.yml | ||
| LICENSE | ||
| README.md | ||
| remove-from-swarm.sh | ||
| start-local.sh | ||
| stop-local.sh | ||
gpu-jupyter
Leverage the power of Jupyter and use your NVIDEA GPU and use Tensorflow and Pytorch in collaborative notebooks.
Contents
Requirements
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Install Docker version 1.10.0+
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Install Docker Compose version 1.6.0+
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Get access to use your GPU via the CUDA drivers, see this blog-post
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Clone this repository
git clone https://github.com/iot-salzburg/gpu-jupyter.git cd gpu-jupyter
Quickstart
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:
./start-local.sh
This will run jupyter on the default port localhost:8888. The general usage is:
./start-local.sh -p [port] # port must be an integer with 4 or more digits.
In order to stop the local deployment, run:
./stop-local.sh