distributed-eval

Guide to running evaluation across multiple GPUs using data parallelism and tensor/pipeline parallelism.

Skills
AI research
1

What it does and how to use it

A skill that teaches Claude Code to do ai research tasks well. It is packaged knowledge the model uses whenever it is relevant.

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Install (Claude Code)

mkdir -p .claude/skills/distributed-eval && curl -fsSL https://doutorai.pt/data/lib/skills__distributed-eval.md -o .claude/skills/distributed-eval/SKILL.md

Paste this command into the terminal, in your project folder, and the component installs automatically.

Content

# Distributed Evaluation

Guide to running evaluation across multiple GPUs using data parallelism and tensor/pipeline parallelism.

## Overview

Distributed evaluation speeds up benchmarking by:
- **Data Parallelism**: Split evaluation samples across GPUs (each GPU has full model copy)
- **Tensor Parallelism**: Split model weights across GPUs (for large models)
- **Pipeline Parallelism**: Split model layers across GPUs (for very large models)

**When to use**:
- Data Parallel: Model fits on single GPU, want faster evaluation
- Tensor/Pipeline Parallel: Model too large for single GPU

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