Ray: Distributed Computing for All, Part 1

2026-01-05 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, medium

Summary

This article, the first in a two-part series, introduces Ray, an open-source Python library designed to simplify distributed computing and scale Python programs from a local PC to multi-server clusters. It demonstrates how Ray Core can significantly accelerate CPU-intensive tasks, such as counting prime numbers within a range, by leveraging all available CPU cores. The example shows a 10x speed improvement, reducing runtime from 31.17 seconds to 3.04 seconds on a 32-core system for a 10,000,000 to 60,000,000 range. The article explains Ray's core components, including Ray Data, Ray Train, Ray Tune, Ray Serve, Ray RLlib, and specifically focuses on Ray Core for general-purpose CPU scaling. It details the concepts of stateless tasks and stateful actors, and provides a four-step process for parallelizing Python code with minimal modifications, along with information on monitoring Ray jobs via its dashboard.

Key takeaway

For Python developers or data scientists optimizing CPU-bound applications, adopting Ray Core can drastically reduce execution times on multi-core machines. By applying just a few code changes, you can parallelize workloads and achieve significant speedups, as demonstrated by the 10x improvement in prime counting. Consider integrating Ray to efficiently utilize your hardware's full potential without complex multiprocessing or language rewrites.

Key insights

Ray simplifies scaling Python applications across multiple CPU cores or clusters with minimal code changes.

Principles

• Ray tasks are stateless workers.
• Ray actors are stateful workers.
• Global variables are not shared across Ray tasks.

Method

To parallelize Python code with Ray Core: initialize Ray with `ray.init()`, decorate functions with `@ray.remote`, launch tasks using `.remote()`, and collect results with `ray.get()`.

In practice

• Use `ray.init()` to start a Ray instance.
• Decorate CPU-intensive functions with `@ray.remote`.
• Monitor Ray jobs via the built-in dashboard at `127.0.0.1:8265`.

Topics

• Ray Framework
• Distributed Computing
• Python Parallel Processing
• Ray Core
• CPU Scaling

Best for: Software Engineer, Data Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.