Introduction to Ray

This course is designed for users new to Ray. It serves as an introductory step in learning Ray, covering fundamentals and providing an overview of its key components. Course includes modules on Ray Data, Ray Train, Ray Tune, Ray Serve, and Ray Core.

Start Course

Course curriculum

1. Course Welcome and Overview
2. Why you should use Ray
3. Local setup and running notebooks
4. How to get the most out of this course
5. Introduction - Full Notebook
1. Introduction to Ray Core
2. Intro to Ray Core - Notebook
3. Overview of Ray Core
4. Overview - Notebook
5. Setting up Remote Functions
6. Remote Function Setup - Notebook
7. Executing Remote Functions
8. Remote Function Execution - Notebook
9. Examples and Anti-Patterns
10. Anti-Patterns - Notebook
11. Ray Core Fundamentals - Full Notebook
1. Overview: Object Store, Tasks, Actors
2. Object Store
3. Chaining Tasks and Passing Data
4. Task Retries
5. Task Runtime Environments
6. Resource Allocation and Management
7. Nested Tasks
8. Pattern: Pipeline data processing and waiting for results
9. Ray Actors
1. Introduction to Ray AI Libraries
2. Ray AI Libraries Intro - Notebook
3. Ray AI Libraries Overview
4. Ray AI Libs Overview - Notebook
5. AI Libraries Extended Example
6. AI Libs Example - Notebook
7. Ray AI Libraries - Full Notebook
8. Ray AI Libraries Quiz
1. Introduction to Ray Train
2. Intro to Ray Train - Notebook
3. When to use Ray Train?
4. When Ray Train - Notebook
5. PyTorch Example
6. PyTorch Example - Notebook
7. Ray Train PyTorch Example
8. Ray PyTorch Example - Notebook
9. Ray Train - Full Notebook
10. Ray Train Quiz
1. Introduction to Ray Train with PyTorch Lightning
2. Single GPU Training with PyTorch Lightning
3. Ray Train DDP with PyTorch Lightning
1. Introduction to Ray Tune
2. Ray Tune Intro - Notebook
3. Loading and visualizing data
4. Data Loading & Viz - Notebook
5. Setting up a PyTorch model
6. PyTorch Setup - Notebook
7. Ray Tune Basics
8. Ray Tune Basics - Notebook
9. Diving deeper into Ray Tune concepts
10. Tune Concepts - Notebook
11. Hyperparameter tuning the PyTorch model using Ray Tune
12. Hyperparameter Tuning - Notebook
13. Tune in Production
14. Chapter Notebook
15. Ray Tune Quiz
1. Introduction to Ray Data
2. Ray Data Intro - Notebook
3. What is Ray Data and How To Use It?
4. What is Ray Data - Notebook
5. Data Loading
6. Loading Data - Notebook
7. Data Transformations
8. Data Transformation - Notebook
9. Writing Data
10. Writing Data - Notebook
11. Common Data Operations
12. Data Ops - Notebook
13. When To Use Ray Data in Production
14. Ray Data in Prod
15. Ray Data - Full Notebook
16. Ray Data Quiz
1. Intro to Ray Data with Unstructured Data
2. Loading Unstructured Data
3. Lazy Execution Mode
4. Transforming Unstructured Data
5. Stateful transformations with Ray Actors
6. Materializing Data
7. Data Operations
1. Introduction to the Data Industry Landscape
2. The Compute Layer
3. The Orchestration Layer
4. Distributed Computing Frameworks
5. Data Processing with Ray Data
6. Ray Serve
1. Ray Serve Overview
2. Ray Overview - Notebook
3. When To Use Serve?
4. Using Ray Serve - Notebook
5. Ray Serve Architecture
6. Architecture Overview - Notebook
7. Image Classifier Example
8. Image Classifier - Notebook
9. Ray Serve in Production
10. Development Workflow - Notebook
11. Ray Serve - Full Notebook
12. Ray Serve Quiz
1. Ray AI Libraries Quiz

About this course

103 lessons
3 hours of video content
Level: Beginner
Ray AI Libraries Overview
Featuring XGBoost and PyTorch

Introduction to Ray

Course curriculum

Welcome to the Introduction to Ray

Ray Core Fundamentals (Videos)

Ray Core Advanced Topics (Notebook)

Ray AI Libraries Overview (Videos)

Ray Train with PyTorch (Videos)

Ray Train with PyTorch Lightning (Notebook)

Ray Tune (Videos)

Ray Data - Structured Data (Videos)

Ray Data - Unstructured Data (Notebook)

Bonus: Ray Data & The Industry Landscape (Notebook)

Ray Serve (Videos)

Final Test

About this course