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ACES: Intel AI/ML Training

Overview

Instructor(s): HPRC: Richard Lawrence, Dr. Zhenhua He; Intel: Aaryan Kothapalli, Yuning Qiu

Time: Tuesday, October 25, 2022 and Tuesday, November 1, 2022 — 10:00AM-12:30PM CT and 1:30PM-4:00PM CT

Location: online using Zoom

Prerequisite(s): Current ACCESS ID or HPRC account, experience with python

In this two-day workshop, you will learn about several components available inside the oneAPI Toolkit for AI/ML applications.

Each day will have a morning session with introductory level material and an afternoon with intermediate level material.

Intel oneAPI Toolkit is a core set of tools and libraries for developing high-performance, data-centric applications across diverse architectures. It simplifies the implementation of HPC applications on CPUs and accelerators with Intel's compiler technology and libraries.

Participants completing these sessions will receive a certificate from Intel.

A Registration button will appear here when registration has been opened.

If you are not a member of TAMU, you will need to provide your ACCESS ID. If you do not have an ACCESS ID, please register at https://identity.access-ci.org/new-user . The ACES project is supported by a National Science Foundation (NSF) award number 2112356.

Course Materials

The presentation slides will be available as downloadable PDF files.

  • ACES: Intel Data Science for Python PDF
  • ACES: Intel Classical ML Optimizations PDF
  • ACES: Introduction to Deep Learning PDF

Participation

During the training, attendees are expected to log in to an HPRC cluster using their own computer and complete the instructor-led examples and exercises.

Agenda

There are two days a week apart, each with a morning and afternoon session (10:00 a.m. - 12:30 p.m., 1:30 p.m. - 4:00 p.m.):

Day 1 : Classical Machine Learning

morning, introductory level (Leading: Richard Lawrence of HPRC)

  • Getting Started with ACES
  • Jupyter Notebook Environment
  • Data Structure with Pandas
  • Machine Learning with Scikit Learn
  • Machine Learning with XGBoost
  • Presentation slides:
    • Data Science for Python PDF

afternoon, advanced level (Leading: Aaryan Kothapalli of Intel)

Day 2: Deep Learning

morning, introductory level (Leading: Zhenhua He of HPRC)

  • Introduction to Deep Learning
    • What is deep learning and why is it important
    • Convolution
    • Pooling
    • Convolutional Neural Networks
    • Presentation Slides:
      • Introduction to Deep Learning PDF
  • Introduction to TensorFlow
    • Build a Handwritten Digits classifier with TensorFlow
  • Introduction to PyTorch
    • Build a Handwritten Digits classifier with PyTorch

afternoon, advanced level (Leading: Yuning Qiu of Intel)