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AI/ML TechLab - Accelerating AI/ML Workflows on a Composable Cyberinfrastructure
Submission navigation links for Knowledge Base Resources
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Submission information
Submission Number:
277
Submission ID:
4157
Submission UUID:
5b136f58-a649-45c2-87ab-91a53641735b
Submission URI:
/form/resource
Created:
Mon, 10/09/2023 - 11:37
Completed:
Mon, 10/09/2023 - 11:43
Changed:
Sun, 12/03/2023 - 20:51
Remote IP address:
165.91.13.137
Submitted by:
Zhenhua He
Language:
English
Is draft:
No
Webform:
Knowledge Base Resources
Approved
Yes
Title
AI/ML TechLab - Accelerating AI/ML Workflows on a Composable Cyberinfrastructure
Category
Docs
Tags
ACES
,
documentation
,
TAMU
,
ai
,
visualization
,
deep-learning
,
machine-learning
,
neural-networks
,
login
,
authentication
,
composable-systems
,
gpu
,
nvidia
,
slurm
,
bash
,
modules
,
vim
,
anaconda
,
conda
,
programming
,
python
,
scikit-learn
Skill Level
Intermediate
Description
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 a 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).
Link to Resource
Presentation slides
GitHub Repository of Examples and Hands-on Exercises
Domain
{Empty}