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eagle3-validate

Validate EAGLE3 pipeline runs by checking completion, artifacts, and benchmark metrics.

3.4k|535|Updated Apr 23, 2024
Search Tags:#benchmarking#machine learning#model optimization#pipeline validation#artifact verification#eagle3
One-click install
npx skills add https://github.com/NVIDIA/Model-Optimizer --skill eagle3-validate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eagle3-validate
Source: https://github.com/NVIDIA/Model-Optimizer/tree/main/.agents/skills/eagle3-validate
Command: npx skills add https://github.com/NVIDIA/Model-Optimizer --skill eagle3-validate

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill removes the manual effort of checking whether an EAGLE3 pipeline execution completed correctly by validating task outcomes, generated artifacts, acceptance rates, and training quality signals.

Core Features & Use Cases

  • Pipeline Validation: Checks all four EAGLE3 pipeline stages for completion, failures, timeouts, and expected outputs.
  • Metric Verification: Evaluates acceptance rate thresholds, training loss behavior, and benchmark results to determine run quality.
  • Use Case: Help an ML engineer verify a new EAGLE3 experiment before recording results or promoting a checkpoint for deployment.

Quick Start

Ask the eagle3-validate skill to verify the latest EAGLE3 pipeline experiment and generate a validation report.

Frequently Asked Questions about eagle3-validate

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

FAQPage Schema
How do I validate an EAGLE3 pipeline run and check benchmark quality?▼

Pipeline validation checks EAGLE3 execution stages for completion, failures, and expected outputs. It analyzes pipeline logs, artifact evidence, and training quality indicators to confirm successful runs.

How do I verify EAGLE3 training loss behavior and acceptance rates?▼

Metric verification evaluates acceptance rate thresholds, training loss behavior, and benchmark results. By analyzing these training quality indicators, you can determine the run quality and verify EAGLE3 experiment outcomes before recording results.

How do I check EAGLE3 pipeline stages for failures or timeouts?▼

Checking EAGLE3 pipeline stages for failures involves validating task outcomes across all four execution phases. The process identifies failures, timeouts, and missing expected outputs to ensure complete pipeline execution.

When do I need pipeline validation for machine learning inference optimization?▼

Pipeline validation for inference optimization is needed before promoting an EAGLE3 checkpoint for deployment. It confirms artifact generation and benchmark quality, ensuring the experiment meets required acceptance thresholds.

What is the best way to verify EAGLE3 experiment artifacts before deployment?▼

The best way to verify EAGLE3 artifacts is automated artifact verification, which checks generated outputs against expected evidence. This removes manual effort and confirms artifact integrity before checkpoint promotion.

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