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domino-distributed-computing

Provision and manage on-demand Spark, Ray, and Dask clusters in Domino.

Updated Aug 23, 2026
Search Tags:#distributed#ray#spark#cluster#domino#dask
One-click install
npx skills add https://github.com/jvdomino/domino-data-lab-plugin --skill domino-distributed-computing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: domino-distributed-computing
Source: https://github.com/jvdomino/domino-data-lab-plugin/tree/main/skills/distributed-computing
Command: npx skills add https://github.com/jvdomino/domino-data-lab-plugin --skill domino-distributed-computing

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about domino-distributed-computing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I provision a distributed computing cluster in Domino for ML training?▼

Provision distributed computing clusters in Domino by selecting Ray, specifying worker counts, and choosing hardware tiers to enable scalable ML training across notebooks and jobs with autoscaling support.

When should I choose Spark, Ray, or Dask for my data processing workflows?▼

Select Spark for big SQL/ETL processing, Ray for distributed ML training, and Dask for pandas-scale analytics to optimize resource usage and match your specific data processing workload requirements.

Can I run Spark, Ray, and Dask clusters with autoscaling and monitoring in Domino?▼

Yes, Domino supports running Spark, Ray, and Dask clusters with built-in monitoring and autoscaling controls to optimize resource usage across notebooks, jobs, and apps for scalable compute workflows.

Do I need specific hardware tiers to run distributed compute jobs on Spark and Dask?▼

You need to select configurable worker counts and hardware tiers when provisioning Spark or Dask clusters to ensure your distributed compute jobs have appropriate resources for large-scale analytics.

What is the best way to scale pandas-scale analytics without moving to Spark?▼

Using Dask clusters in Domino is the best way to scale pandas-style analytics without switching to Spark, providing on-demand provisioning and autoscaling tailored for large-scale analytics workflows.

Why does setting up on-demand distributed compute clusters reduce operational overhead?▼

Provisioning on-demand distributed compute clusters reduces operational overhead by streamlining Spark, Ray, and Dask setup, minimizing configuration time, and optimizing resource allocation through autoscaling controls.

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