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# gpu-jupyter
#### Leverage the power of Jupyter and use your NVIDEA GPU and use Tensorflow and Pytorch in collaborative notebooks.
#### Leverage Jupyter Notebooks with the power of your NVIDEA GPU and perform GPU calculations using Tensorflow and Pytorch in collaborative notebooks.
![Jupyterlab Overview](/extra/jupyterlab-overview.png)
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./start-local.sh
```
This will run *gpu-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) with the default password `asdf`. The general usage is:
```bash
./start-local.sh -p [port] # port must be an integer with 4 or more digits.
```
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* 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.
Now, *gpu-jupyter* will be accessable on [localhost:port](http://localhost:8888) with the default password `asdf` 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.
Check if everything works well using:
```bash