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First of all, you need to request multiple nodes in ARE JupyterLab session and specify the proper storage projects.

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Then click "Advanced options" button, and put "/g/data/dk92/apps/Modules/modulefiles" in "Module directories" and load both NCI-data-analysis/2022.06 and gadi-jupyterlab/22.06 modules in "Module" field. In the "Pre-script" field, fill in the command "jupyterlab.ini.sh -R" to set up the pre-defined Ray cluster.  

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Click "Open JupyterLab" button to open the JupyterLab session as soon it is highlighted.

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In the Jupyter notebook, using the following lines to connect the pre-defined Ray cluster and print the resources information. 

import ray
ray.init(address="auto")
print(ray.cluster_resources())

You will see 96 CPU Cores and two nodes are used by the cluster as expected.

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Monitoring Ray status

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The Ray status will be kept updating every 2 seconds

Every 2.0s: ray status gadi-cpu-clx-114660021448.gadi.nci.org.au: MonThu MayJul 237 1511:2726:2759 2022
======== Autoscaler status: 2022-0507-2307 1511:27:26.840051480900 ========
Node status
---------------------------------------------------------------
Healthy:
1 node_ab2c1ef9316a7ee01bae8cd9d087f5e5dfbe3c8c254cf0e8752be0b1c166858d7a953a2050cd004f54239c0014cc9fcf34b640d7cac21de 
1 node_b2f22752fc2b329e68649a81ba3c26f2d4f6080fa822dda0121d5514e6d5c4a8797357ebc0a5d0b76a6e264df133f0cba2dc236f696d7c8
Pending:
  (no pending nodes)
Recent failures:
  (no failures)

Resources
---------------------------------------------------------------
Usage:

96.0/96.0 CPU
0.00/239.062 GiB memory
23.70/106.446 GiB object_store_memory

Demands:
{'CPU': 1.0}: 225+ pending tasks/actors

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