1. Prerequisites

1. You should be a member of NCI project "dk92"
2. Make a copy of the NCI Data Analysis Example Notebooks in one of your working directories
3. Some notebooks will require you to access specific dataset projects. The data projects you will need to join are detailed in each notebook.

2. Start a JupyterLab session

You can start a JupyterLab session from NCI ARE platform.

2.1 JupyterLab on the NCI ARE

Follow our JupyterLab User Guide to launch a JupyterLab session at ARE. Please note the ARE JupyterLab session utilises Gadi resources. You will need to

1. Choose the appropriate compute resources
2. Click "advanced options" and
   1. Type in "/g/data/dk92/apps/Modules/modulefiles" in the "Module directories" box.
   2. Type in "NCI-data-analysis/2023.02" in the "Modules" box.
3. Click the "Launch" button to start a JupyterLab session.

3. Start a Python Notebook

In the "Launch" interface, you can click "Python" button under Notebook catalogue to start a Python notebook.

Or, you can click "Python" button under Console catalogue to start a Python console.

4. Run the NCI data analysis example

Navigate to the working directory in your JupyterLab session and open a notebook from the NCI Data Analysis Example Notebooks. 

Please note you need to invoke different APIs to set up a dynamic Dask cluster within your notebook at ARE. Please check here for more details.

You can monitor your Dask cluster activities by following the Dask-JupyterLab extension documentation.