ML Workshop 2026 Technical Setup Instructions for Autoencoder Training Sessions
Introduction:
This post covers the technical setup requirements for the hands-on training session on:
Autoencoders: Part 2 | 2026-08-20T03:30:00Z→2026-08-20T05:00:00Z.
We will go through the rest of these setup instructions together at the end of the Autoencoders: Part 1 training session, so it’s not essential to complete these steps 3-4 in advance. However, if you’d like to maximise your hands-on time during the workshop, working through these steps beforehand will give you a head start.
Table of Contents:
The first two steps must be completed prior to the training sessions.
-
Sign up for an NCI account
-
Join up to the relevant NCI projects
Setup instructions: We’ll go together at the end of Day 1 Training
-
Create your working directories and copy the training notebooks
-
Set up a JupyterLab session in NCI ARE
-
Test your setup
On the day: Start the session 30 mins prior
- Launch JupyterLab early on the day Preferably 30 minutes prior
Required setup before the training session
1. Sign up for NCI account
If you don’t already have an NCI account, sign up here.
When creating your account, you can nominate project nf33 as your main project.
New to NCI ARE?
The practical exercises will use the NCI Australian Research Environment (ARE). Basic familiarity with the ARE environment is recommended, but we’ll also step through the setup together during the training.
2. Join NCI projects
Please request membership of the following NCI projects.
- dk92: Data science environment, used to access the PyEarthTools Python modules.
- nf33: ACCESS-NRI training environment, used for compute and setting up your working directory.
- rv74: Himawari 8 data used in the Auto-encoder examples.
- rt52: Required to fully participate in the workshop.
- Optional wb00: For accessing ERA5 on-disk data without needing to download it.
- Optional rq0: For radar-based precipitation data.
Note: rt52 project was not listed in the original setup email, but is required to fully participate in the workshop. Please make sure you request access to this project as well.
Setup instructions: We’ll go through at the end of the Part 1 training
3. Create your working directories and copy the training notebooks
Once your NCI account and project access are ready, log in to Gadi from a terminal:
ssh gadi
Move to the nf33 project directory:
cd /g/data/nf33
Create a directory using your own NCI username, then move into it:
mkdir -p <your-nci-username>
cd <your-nci-username>
For example, if your NCI username were ab1234:
mkdir -p ab1234
cd ab1234
You can check your current location with:
pwd
It should look similar to:
/g/data/nf33/<your-nci-username>
Now create the directories you’ll need for the workshop:
mkdir -p userbase62 notebooks
Your directory should now contain:
userbase62/
notebooks/
Now, we are done with the creation of working directories! Let’s copy over the training notebooks.
Copy the training notebooks:
Move into your notebooks directory:
cd notebooks
Copy the workshop notebooks from the shared training directory:
cp -r /g/data/nf33/autoencoder_notebooks/* .
Check that the files have copied successfully:
ls -l
You should see files including the workshop notebooks and a tutorial directory.
Example Output for ls -l:
[ab1234@gadi-login-08 notebooks\]$ ls -l
total 696
rw-r----- 1 ab1234 nf33 523602 Aug 12 17:04 DataAccessForWorkshopsNCI.ipynb
rw-r----- 1 ab1234 nf33 184259 Aug 12 17:04 TemporalWindowDemo.ipynb
drwxr-sr-x 2 ab1234 nf33 4096 Aug 12 17.04 tutorial
4. Set up JupyterLab in NCI ARE
Open NCI ARE and select JupyterLab.
Main JupyterLab settings:
Recommended fields are as follows:
| Setting |
Value |
| Walltime |
3 hours |
| Queue |
gpuvolta |
| Compute Size |
1gpu |
| Project |
nf33 |
| Storage |
See below |
For Storage, add the project directories required by the notebooks:
gdata/dk92+gdata/nf33+gdata/wb00+gdata/rv74+gdata/rq0+gdata/rt52
Main JupyterLab settings
Advanced Settings:
Tick Show advanced settings.
For Module directories, enter:
/g/data/dk92/apps/Modules/modulefiles
For Modules, enter:
pet/0.6.2
Advanced Settings:
Further down the advanced settings, find Environment variables.
Enter:
PYTHONUSERBASE="/g/data/nf33/<your-nci-username>/userbase62/"
Replace <your-nci-username> with your actual NCI username.
For example:
PYTHONUSERBASE="/g/data/nf33/abc123/userbase62/"
Set Jobfs size to:
100GB
Advanced Settings > Environment Variables:
Tip: Once you’ve confirmed everything is working, you can use Save settings in ARE so you don’t need to enter all of the configuration again on the day.
When you’re ready, select Launch. It may take a few minutes for your JupyterLab session to start.
5. Test your setup
Once the JupyterLab session has started, click Open JupyterLab button under My Interactive Sessions tab.
Click Open JupyterLab
Use the file browser to navigate to:
/g/data/nf33/<your-nci-username>/notebooks
Open and run the cells for:
DataAccessForWorkshopsNCI.ipynb
This gives us a chance to check that the environment and data access are all working before moving on to the main exercises.
6. Launch JupyterLab early on the day
Before we move into the hands-on component, we’ll make sure everyone’s JupyterLab session is running. ARE jobs can sometimes spend a little time waiting in the queue, so we’ll aim to launch the sessions around 30 minutes before we need them.
Once your session starts, navigate to:
/g/data/nf33/<your-nci-username>/notebooks/tutorial
and open:
IntroductionForWorkshops.ipynb
From there, we’re ready to start the exercises. 
