NVIDIA-NeMo / NVIDIA-NeMo/Gym

doc: distributed computing with ray

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Description

Use cases, pain points, and background
The Ray documentation in reference/faq.md has several issues:

  1. Wrong location — Buried in FAQ, hard to discover for users hitting scale issues
  2. Unclear audience — Mixes what's automatic (Ray init) with what's optional (using @ray.remote in your code)
  3. Missing decision criteria — Doesn't explain when you need Ray
  4. Generic example — Shows standalone function, not integrated with resource server patterns

Description:
Relocate documentation from FAQ into standalone docs page and improve the content for clarity

Design:

# Distributed Computing with Ray

## Overview
<!-- What Ray does in NeMo Gym, that it's auto-initialized -->

## When You Need Ray
<!-- Decision criteria: CPU-bound verification, 1000s of concurrent requests -->

## Using Ray in Your Code
<!-- How to add @ray.remote to your functions -->

## Configuration  
<!-- ray_head_node_address for training framework integration -->

Out of scope:

  • General Ray tutorials or Ray cluster administration (link to Ray docs instead)
  • Changes to Ray initialization logic in NeMo Gym code
  • Performance benchmarking or profiling guidance (covered in separate profiling docs)
  • Troubleshooting Ray-specific errors (e.g., serialization issues, memory limits)
  • Multi-node cluster setup instructions (training framework responsibility)

Acceptance Criteria:

  • New docs page created at docs/infrastructure/distributed-computing.md
  • Page covers all 4 sections: Overview, When You Need Ray, Using Ray, Configuration
  • Example code shows integration with a resource server verify() method (not standalone function)
  • Decision criteria section includes concrete thresholds (e.g., "CPU-bound tasks taking >Xms at >Y concurrent requests")
  • FAQ entry removed entirely
  • Toctree updated in docs/index.md with new Infrastructure section
  • Docs build successfully (make html in docs/)

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reading the Ray-related content in reference/faq.md, then inspect docs/index.md and nearby pages under docs/infrastructure/. Create docs/infrastructure/distributed-computing.md with the four specified sections and resource-server verify() example, remove the FAQ entry, update the toctree, and run make html in docs/ to confirm the build succeeds.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
distributed-systems, documentation
Issue type
Documentation
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Clearly specified
Newbie friendliness
58/100

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