Distributed Computation Foundations (Ray Core)
Foundations
Master the basics of distributed computing with this foundations course on Ray Core.
About this course
Course Modules
Basics
Learn the fundamentals of Ray Core by turning Python functions into distributed tasks with `@ray.remote`, executing them in parallel with `.remote()`, and retrieving results using `ray.get`. You’ll practice by writing and running your own remote task and learn a key anti-pattern to avoid (`ray.get` in a loop) to preserve parallelism.
Introduction to Ray Core- Overview of Ray Core
- Setting up Remote Functions
Advanced
In this Advanced module, you’ll learn Ray Core’s fundamental building blocks—object store, tasks, and actors—and how to use `ObjectRef`s to efficiently share large data and chain distributed computations without unnecessary `ray.get()` calls. You’ll practice key patterns (and avoid common anti-patterns) for building scalable execution graphs locally or on a Ray cluster.
- Introduction to Ray Core (Advancement): Object store, Tasks, Actors
- Object store
- Chaining Tasks and Passing Data