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domino-distributed-computing

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Scale compute with Spark, Ray, and Dask on Domino.

Data & Analytics#distributed#ray#spark#cluster#domino#dask
Authorjvdomino
Version1.0.0
Installs0

System Documentation

What problem does it solve?

Efficiently managing and scaling distributed computing workloads in Domino across Spark, Ray, and Dask clusters, reducing setup time and operational overhead.

Core Features & Use Cases

  • Provision on-demand clusters with configurable cluster type, worker counts, and hardware tiers for Spark, Ray, or Dask.
  • Select the appropriate framework based on workload: Spark for big SQL/ETL, Ray for distributed ML, Dask for pandas-scale analytics.
  • Run notebooks, jobs, or apps at scale with monitoring and autoscaling support to optimize resource usage.

Quick Start

Start a workspace with a distributed compute cluster selecting Spark, Ray, or Dask and specify 4 workers.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: domino-distributed-computing
Download link: https://github.com/jvdomino/domino-data-lab-plugin/archive/main.zip#domino-distributed-computing

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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