Introduction to Pytorch

This repo is designed as a comprehensive starting point for those new to PyTorch and deep learning. It provides hands-on tutorials and examples to help you get acquainted with the core concepts and features of PyTorch, one of the most popular open-source machine learning libraries.

Documentation: https://intro-to-pytorch.readthedocs.io/en/latest/index.html

Australian Research Environment (ARE): https://handson-with-gadi.readthedocs.io/en/latest/tutorial/login.html

Guide to Launch JupyterLab on ARE

This quick guide demonstrates the material and environment setup required for the workshop.

Prerequisite

Project membership:

vp91 (Note: after the request is approved, the system takes around 20-30 mins update your account.)

Step 1. Make User Directory

Create a username directory under /scratch/vp91:

mkdir -p /scratch/vp91/$USER

This should be the place where you store all your training material.

Step 2. Clone GitHub Repo

The material and data for today’s session are available in our public GitHub repository, clone it to the folder above.

cd /scratch/vp91/$USER    
git clone https://github.com/NCI900-Training-Organisation/intro-to-pytorch

Step 3. Prepare JypyterLab in ARE

At your browser go to are.nci.org.au, login with your NCI account. At the interface go to JupyterLab.

Step 4. Configure Job Settings

The parameters depend on the content of the workshop. Below are general use only unless specified by your instructor.

Queue: normal (Note: this is a free text field).
Compute size: small

or if you need GPUs
Queue: gpuvolta
Compute size: 1gpu

Project: vp91
Storage: scratch/vp91 (Note: No starting slash)

Step 5. Configure Modules and Environments

Below are examples only. Please check the workshop resources for the correct settings.

Click on the Advanced options

Modules: python3/3.11.0 cuda/12.3.2 (Note: One space only between modules)
Python or Conda virtual environment base: /scratch/vp91/Training-Venv/pytorch

alishakiba/nci-pytorch