/ai-science-training-series

Primary LanguageJupyter Notebook

Introduction to AI-driven Science on Supercomputers: A Student Training Series

2022 Fall Series

Public Page for Series Schedule

Agenda with content links

This repository is organized into one subdirectory per topic. All content is prefixed by a two-digit index in the order of presentation in the tutorials.

Table of Contents
  1. Introduction to ALCF Systems
    1. ALCF Compute Systems Overview
    2. Shared Resources
    3. Introduction to Jupyter Notebooks
    4. How to Submit the Homeworks
    5. How to Login on the Command Line
    6. How to Setup a Shell Enviroment
    7. Submitting Jobs to a Queue
  2. Introduction to Machine Learning
    1. Introduction to Machine Learning with Linear Regression
    2. Introduction to Machine Learning with k-means Clustering
  3. Neural Networks in Python
  4. Neural Networks in TensorFlow
  5. Modern Classification Networks
  6. Handling Data During AI Training
  7. Supercomputers and Parallel AI Training
  8. Running Large Scale Training on a Supercomputer
  9. Advanced AI Architectures and Learning Methods

Note for contributors: please run git config --local include.path ../.gitconfig once upon cloning the repository (from anywhere in the repo) to add the gitattribute filter defintions to your local git configuration options.1 Be sure that the jupyter command is in your $PATH, otherwise the filter and git staging will fail.23

Footnotes

  1. https://zhauniarovich.com/post/2020/2020-10-clearing-jupyter-output-p3/ ↩

  2. https://stackoverflow.com/questions/28908319/how-to-clear-jupyter-notebooks-output-in-all-cells-from-the-linux-terminal ↩

  3. https://bignerdranch.com/blog/git-smudge-and-clean-filters-making-changes-so-you-dont-have-to/ ↩