Principle:Datajuicer Data juicer Ray Cluster Initialization
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Computing, Infrastructure |
| Last Updated | 2026-02-14 17:00 GMT |
Overview
A cluster bootstrapping pattern that initializes a Ray runtime environment for distributed data processing across multiple nodes.
Description
Ray Cluster Initialization starts a distributed computing runtime that Data-Juicer uses for large-scale data processing. A Ray cluster consists of a head node (coordinator) and optional worker nodes. The head node manages task scheduling, object storage, and the Global Control Store (GCS). Data-Juicer connects to this cluster via its address and distributes operator execution across all available resources.
Usage
Use this principle before running any distributed Data-Juicer pipeline. For single-machine usage, ray.init() can be called programmatically. For multi-node clusters, start the head node first, then connect workers.
Theoretical Basis
# Abstract pattern (NOT real implementation)
# Option 1: CLI-based cluster start
# Head node: ray start --head --port=6379
# Workers: ray start --address=HEAD_IP:6379
# Option 2: Programmatic (single machine)
ray.init() # Local cluster with all CPUs/GPUs
# Data-Juicer connects via config
# executor_type: ray
# ray_address: auto (or explicit address)