https://github.com/southernmethodistuniversity/distributed_python

ODSRCI Workshop: Distributed Python
https://github.com/southernmethodistuniversity/distributed_python

dask distributed-computing gpu hpc parallel-programming python ray

Last synced: about 1 year ago
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ODSRCI Workshop: Distributed Python

• Host: GitHub
• URL: https://github.com/southernmethodistuniversity/distributed_python
• Owner: SouthernMethodistUniversity
• License: mit
• Created: 2025-01-08T23:05:18.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-24T15:56:03.000Z (about 1 year ago)
• Last Synced: 2025-01-30T09:18:20.793Z (about 1 year ago)
• Topics: dask, distributed-computing, gpu, hpc, parallel-programming, python, ray
• Language: Jupyter Notebook
• Homepage: https://southernmethodistuniversity.github.io/distributed_python/
• Size: 3.84 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# SMU O'Donnell Data Science and Research Computing Institute Distributed Python Workshop

The SMU [O’Donnell Data Science and Research Computing

Institute](https://www.smu.edu/provost/odonnell-institute) Distributed Python

workshop is designed for researchers seeking to optimize their workflows for

large-scale data processing and parallel computing. Participants will explore

the fundamentals of distributed computing with Python, learning how to leverage

Dask for efficient task parallelism and Ray for scalable machine learning and

reinforcement learning applications. Through hands-on exercises, attendees will

gain practical experience in deploying these tools to handle massive datasets,

optimize computational resources, and streamline workflows.

## Overview

## Key Topics

1. **Introduction to Parallel Programming:**

   - Understanding the basics of parallel computing.

   - Advantages of parallelism in software development.

   - Overview of parallel hardware and software architectures.

2. **Dask:**

4. **Ray:**

## Getting Started

- Prerequisites: Basic understanding of Python programming.

- Environment Setup: Guidance on setting up a development environment.

- Example Projects: Hands-on examples provided to practice the concepts

  learned.

## Resources

- **Documentation:** Detailed explanations and examples of the concepts

  covered.

- **Examples:** Sample code demonstrating the use of parallel constructs in

  real-world scenarios.

- **Exercises:** Problems and projects to test your understanding and skills.

## Contribution

This workshop is open for contributions from the community. You can suggest

changes, report issues, or add new content to help enhance the learning

experience.