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nemo-evaluator-sdk

Evaluate LLM endpoints across standardized benchmarks using YAML-configured multi-backend setups.

Updated May 2, 2026
Search Tags:#benchmarking#llm evaluation#safety#docker#slurm#nemo evaluator#mmlU
One-click install
npx skills add https://github.com/qcmuu/AI-Research-Skills --skill nemo-evaluator-sdk-qcmuu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nemo-evaluator-sdk
Source: https://github.com/qcmuu/AI-Research-Skills/tree/main/11-evaluation/nemo-evaluator
Command: npx skills add https://github.com/qcmuu/AI-Research-Skills --skill nemo-evaluator-sdk-qcmuu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nemo-evaluator-launcher>=0.1.25, docker, and includes references (resource) components.

What problem does it solve?

NeMo Evaluator SDK helps you benchmark LLMs across many standardized and safety/VLM tasks without manually wiring datasets, harnesses, and execution backends.

Core Features & Use Cases

  • Container-first, reproducible evaluation: Execute tasks via NVIDIA’s containerized framework so runs are consistent across machines and clusters.
  • Multi-backend execution: Run locally with Docker, submit at scale on Slurm HPC, or execute on cloud backends while keeping the same task definitions.
  • Cross-harness benchmark coverage: Evaluate with 100+ benchmarks spanning common academic, code, safety, and vision-language harnesses.
  • Operational workflows: List tasks, run evaluations from YAML configs, monitor runs, and export results for comparison across models.

Real-world example: You want to compare two model endpoints on MMLU, HumanEval, GSM8K, and a safety suite using the same harnesses and execution settings, producing a single set of reproducible metrics you can export to MLflow or W&B.

Quick Start

Run the evaluation by creating a config.yaml with your target API endpoint and selected tasks, then execute: nemo-evaluator-launcher run --config-dir . --config-name config.

Frequently Asked Questions about nemo-evaluator-sdk

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

FAQPage Schema
How do I run large-scale LLM evaluations across standardized benchmarks without manually building harnesses?▼

To run large-scale LLM evaluations, you define a YAML configuration with your API endpoint and selected tasks, then execute via nemo-evaluator-launcher. It uses containerized harnesses for reproducible results across 100+ academic, code, safety, and vision-language benchmarks.

Can I evaluate LLMs on Slurm HPC clusters and cloud backends using the same task definitions as local Docker runs?▼

You can evaluate LLMs on Slurm HPC and cloud backends using the same task definitions as local Docker runs. NeMo Evaluator SDK supports multi-backend execution, ensuring consistent, reproducible benchmark results whether running locally or at scale.

What benchmarks are available for evaluating LLM endpoints, and does it include safety and code generation tasks?▼

Available benchmarks for evaluating LLM endpoints include over 100 tasks spanning common academic, code generation, safety, and vision-language harnesses. It provides broad coverage including standardized tests like MMLU, HumanEval, and GSM8K.

Do I need Docker to run reproducible LLM benchmark evaluations, or are there other execution environments?▼

You need Docker for local containerized execution, but it is not the only environment. The framework supports multi-backend execution across Docker, Slurm HPC, and cloud backends, using containerized harnesses to ensure reproducible LLM benchmark results.

How do I compare multiple LLM model endpoints on MMLU and safety suites with consistent execution settings?▼

To compare multiple LLM model endpoints on MMLU and safety suites, you configure them in a YAML file and run evaluations through nemo-evaluator-launcher. It applies the same containerized harnesses and execution settings, producing reproducible metrics for comparison.

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