NCSA is organizing online training sessions throughout the Fall 2021 semester to help users to get started with deep learning projects on HAL. These sessions are designed for novice users to learn about the system and start building deep neural network models. To sign up for training, just request a HAL account prior to the training session and mention “fall training” when describing how the system will be used in your project.
Workshops will be held each Wednesday from 3-5pm.
Trainings here: https://go.ncsa.illinois.edu/HALtraining2021
FAll 2021 Schedule
September 8: Getting Started with HAL – Dawei Mu
This tutorial will introduce students to HAL system, how to interact with it through Open OnDemand interface, including Jupiter notebook, and through command line interface via SSH. The tutorial will cover the Anaconda environment, batch job submission, data transfer, as well as basics of machine learning.
- Trainings Recording: https://www.youtube.com/watch?v=5kEO_MNjQDY
- Training Slides: Slides
September 15: Hands-On Deep Learning for Computer Vision – Asad Khan
This tutorial will introduce how machine learning can be accomplished with neural networks and will go over various examples from simple dense networks to convolutional network architectures using TensorFlow on HAL system.
- Training Recording: https://www.youtube.com/watch?v=dU_u8ORN3Jg
- Training Instructions: https://docs.google.com/document/d/17FL1kgWt3eCTUcNVxwukknAu-kfYUM6ZGgQsABTPFmU/edit
- GitHub Repository Notebook: https://github.com/khanx169/NGA_NFI_webinar
September 22: Intro to TensorFlow – Asad Khan
This tutorial will introduce basics of TensorFlow necessary to build a neural network, train it and evaluate the accuracy of the model.
September 29: Intro to PyTorch – Yao-Yu Lin
This tutorial will teach how to build and train neural networks in PyTorch on HAL.
October 6: Data Loaders – William Eustis
The main objective of this tutorial is to show how to use data loaders provided with PyTorch and how to develop application-specific data loaders.