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1. Note: Access to JupyterHub is only available for users on projects that have paid for additional processing power. 
2. If you have requested JupyterHub and this has been confirmed you will need to log on to the SAIL Gateway as normal. Once within the Windows 10 environment, open up your Browser and navigate to - httphttps://jupyterhub.sail.k8spk.chi.swanserp.ac.uk/ 
3. Click the orange ' login with Keycloak' button and follow the instructions. Your login is the same as the one you use to log into the SAIL Gateway.
4. Following this, you will be logged into JupyterHub and will see a list of notebooks to choose from. Assuming that you are on a GPU project you will see a minimum of 3 options:
   1. The first is a basic (non-GPU) notebook. This has python and R kernels installed, and also allows you to launch VS Code and RStudio from within the notebook if you prefer a more fully-featured IDE.
   2. The second is a notebook with the same features as the first that will attach itself to a GPU, but that does not contain any GPU drivers or related python libraries. You will have to install all of your own GPU drivers from within the notebook if you select this. We do not recommend the use of this option, and it will likely be removed in a future release.
   3. The third is a GPU-attached notebook with CUDA 11.612.2, Tensorflow, and other common python ML libraries preinstalled. It is configured to automatically surface your specific project GPU to Tensorflow within the notebook. This notebook notebook only supports Python. It also includes VS Code and Tensorboard, as well as an extension for monitoring your GPU resource usage.
5. If you are on more than 1 GPU project (e.g. project 1234 with GPU and project 1653 with GPU) you will see separate options in the notebook image list for each project. In this case, the list will look something like this:
   1. Standard Jupyter notebook
   2. Standard Jupyter notebook with GPU for project 1234
   3. GPU-enabled Jupyter notebook with GPU for project 1234
   4. Standard Jupyter notebook with GPU for project 1653
   5. GPU-enabled Jupyter notebook with GPU for project 1653

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There are two options for moving files from your SAIL desktop and into your Jupyterhub Notebook.

1. If the file is under 8mb 8gb you can simply drag and drop it into the browser window.
2. Or you can sync it via GitLab (very much the recommended option).

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