django-ray Documentation¶

Welcome to the django-ray documentation. django-ray is a Ray-based backend for Django Tasks that enables distributed task execution with database-backed reliability.

What is django-ray?¶

django-ray is a library that provides:

• RayTaskBackend - A Django Tasks backend
• RayTaskExecution model - Task execution tracking in your database
• TaskWorkerLease model - Worker coordination for distributed deployments
• django_ray_worker command - Management command to process tasks
• Django Admin integration - Monitor and manage tasks

Note: This repository also contains a testproject/ directory with example code demonstrating django-ray features. The testproject (including its REST API) is not part of the django-ray library - it's provided for learning and testing purposes only.

The bundled testproject includes a landing page that links to the sample API, admin, Ray dashboard, project resources, task stats, and a smoke-task trigger:

User Guide¶

• Getting Started - Installation and basic setup
• Configuration - All configuration options
• Worker Modes - Understanding execution modes
• Task Definition - Defining and enqueueing tasks
• Queues - Working with task queues
• Retry & Error Handling - Configuring retries and handling failures

Deployment¶

• Kubernetes Deployment - Deploy to Kubernetes
• Docker - Running with Docker
• TLS Configuration - Securing Ray cluster communication
• Operator Runbook - Incident diagnosis and manual recovery

Reference¶

• CLI Reference - Command-line interface
• Settings Reference - All settings
• Result Storage - Oversized result backends and retrieval
• Handle Compatibility - Ray Core handle formats and migration policy
• API Reference - How to build your own API (with testproject examples)

Development¶

• Contributing - How to contribute
• Architecture - System design overview
• Changelog - Release history