ACES: AI TechLab in Jupyter Notebooks
Overview
Instructor(s): Dr. Zhenhua He
Time: February 2026
Location: Online using Zoom
Prerequisite(s): Current ACCESS ID, basic Python skills
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).
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Course Materials
Participation
During the training, attendees are expected to use their own computer and complete the instructor-led application and allocation processes.
Learning Objectives and Agenda
In this course, participants will:
- Access the ACES cluster
- Learn to use JupyterLab app on ACES Open OnDemand (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).