ACES: AI TechLab in Jupyter Notebooks
Overview
Instructor: Zhenhua He
Time: Tuesday, September 17, 2024 — 10:00AM-12:30PM CT
Location: Online using Zoom
Prerequisites: Current ACCESS ID, Python
This technology lab contains a set of sessions to help a new user start an AI project on the ACES cluster, a composable accelerator testbed at Texas A&M University. You will learn how to create and activate virtual environment, manipulate and visualize data with Pandas and Matplotlib, use Scikit-learn for linear regression and classification applications, and use Pytorch to create and train a simple image classification model with deep neural networks (DNN).
Course Materials
Presentation slides
The presentation slides are available as downloadable PDF files.
Learning Objectives and Agenda
In this course, participants will:
- Access the ACES cluster
- Learn to use JupyterLab app on ACES OpenOnDemand (OOD) portal
- Learn to load software modules and create virtual environment for AI/ML projects
- Learn two Python libraries (Pandas and Matplotlib) for data science
- Learn fundamentals of AI/ML
- Learn how to use the scikit-learn and keras libraries for ML and DL applications.
This session will be organized into four labs, as follows:
- Lab 1 - Jupyter Notebook (15 mins)
We will create and activate a virtual environment and run JupyterLab on the HPRC Portal.
- Lab 2 - Data Exploration (30 mins)
We will go through simple examples with two popular Python modules: Pandas and Matplotlib for simple data exploration.
- Lab 3 - Machine Learning (30 minutes)
We will learn to use scikit-learn for linear regression and classification applications.
- Lab 4 - Deep Learning (30 minutes)
We will learn how to use Pytorch to create and train a simple image classification model with deep neural networks (DNN).