nemo-evaluator-sdk

Q: How do I install nemo-evaluator-sdk?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill nemo-evaluator-sdk in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/nemo-evaluator-sdk/ (personal) or .claude/skills/nemo-evaluator-sdk/ (project).

by orchestra-researchProductivity & Automation

Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/orchestra-research/ai-research-skills --skill nemo-evaluator-sdk

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads nemo-evaluator-sdk automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

NeMo Evaluator SDK - Enterprise LLM Benchmarking

Quick Start

NeMo Evaluator SDK evaluates LLMs across 100+ benchmarks from 18+ harnesses using containerized, reproducible evaluation with multi-backend execution (local Docker, Slurm HPC, Lepton cloud).

Installation:

pip install nemo-evaluator-launcher

Set API key and run evaluation:

export NGC_API_KEY=nvapi-your-key-here

# Create minimal config
cat > config.yaml << 'EOF'
defaults:
  - execution: local
  - deployment: none
  - _self_

execution:
  output_dir: ./results

target:
  api_endpoint:
    model_id: meta/llama-3.1-8b-instruct
    url: https://integrate.api.nvidia.com/v1/chat/completions
    api_key_name: NGC_API_KEY

evaluation:
  tasks:
    - name: ifeval
EOF

# Run evaluation
nemo-evaluator-launcher run --config-dir . --config-name config

View available tasks:

nemo-evaluator-launcher ls tasks

Common Workflows

Workflow 1: Evaluate Model on Standard Benchmarks

Run core academic benchmarks (MMLU, GSM8K, IFEval) on any OpenAI-compatible endpoint.

Checklist:

Standard Evaluation:
- [ ] Step 1: Configure API endpoint
- [ ] Step 2: Select benchmarks
- [ ] Step 3: Run evaluation
- [ ] Step 4: Check results

Step 1: Configure API endpoint

# config.yaml
defaults:
  - execution: local
  - deployment: none
  - _self_

execution:
  output_dir: ./results

target:
  api_endpoint:
    model_id: meta/llama-3.1-8b-instruct
    url: https://integrate.api.nvidia.com/v1/chat/completions
    api_key_name: NGC_API_KEY

For self-hosted endpoints (vLLM, TRT-LLM):

target:
  api_endpoint:
    model_id: my-model
    url: http://localhost:8000/v1/chat/completions
    api_key_name: ""  # No key needed for local

Step 2: Select benchmarks

Add tasks to your config:

evaluation:
  tasks:
    - name: ifeval           # Instruction following
    - name: gpqa_diamond     # Graduate-level QA
      env_vars:
        HF_TOKEN: HF_TOKEN   # Some tasks need HF token
    - name: gsm8k_cot_instruct  # Math reasoning
    - name: humaneval        # Code generation

Step 3: Run evaluation

# Run with config file
nemo-evaluator-launcher run \
  --config-dir . \
  --config-name config

# Override output directory
nemo-evaluator-launcher run \
  --config-dir . \
  --config-name config \
  -o execution.output_dir=./my_results

# Limit samples for quick testing
nemo-evaluator-launcher run \
  --config-dir . \
  --config-name config \
  -o +evaluation.nemo_evaluator_config.config.params.limit_samples=10

Step 4: Check results

# Check job status
nemo-evaluator-launcher status <invocation_id>

# List all runs
nemo-evaluator-launcher ls runs

# View results
cat results/<invocation_id>/<task>/artifacts/results.yml

Workflow 2: Run Evaluation on Slurm HPC Cluster

Execute large-scale evaluation on HPC infrastructure.

Checklist:

Slurm Evaluation:
- [ ] Step 1: Configure Slurm settings
- [ ] Step 2: Set up model deployment
- [ ] Step 3: Launch evaluation
- [ ] Step 4: Monitor job status

Step 1: Configure Slurm settings

# slurm_config.yaml
defaults:
  - execution: slurm
  - deployment: vllm
  - _self_

execution:
  hostname: cluster.example.com
  account: my_slurm_account
  partition: gpu
  output_dir: /shared/results
  walltime: "04:00:00"
  nodes: 1
  gpus_per_node: 8

Step 2: Set up model deployment

deployment:
  checkpoint_path: /shared/models/llama-3.1-8b
  tensor_parallel_size: 2
  data_parallel_size: 4
  max_model_len: 4096

target:
  api_endpoint:
    model_id: llama-3.1-8b
    # URL auto-generated by deployment

Step 3: Launch evaluation

nemo-evaluator-launcher run \
  --config-dir . \
  --config-name slurm_config

Step 4: Monitor job status

# Check status (queries sacct)
nemo-evaluator-launcher status <invocation_id>

# View detailed info
nemo-evaluator-launcher info <invocation_id>

# Kill if needed
nemo-evaluator-launcher kill <invocation_id>

Workflow 3: Compare Multiple Models

Benchmark multiple models on the same tasks for comparison.

Checklist:

Model Comparison:
- [ ] Step 1: Create base config
- [ ] Step 2: Run evaluations with overrides
- [ ] Step 3: Export and compare results

Step 1: Create base config

# base_eval.yaml
defaults:
  - execution: local
  - deployment: none
  - _self_

execution:
  output_dir: ./comparison_results

evaluation:
  nemo_evaluator_config:
    config:
      params:
        temperature: 0.01
        parallelism: 4
  tasks:
    - name: mmlu_pro
    - name: gsm8k_cot_instruct
    - name: ifeval

Step 2: Run evaluations with model overrides

# Evaluate Llama 3.1 8B
nemo-evaluator-launcher run \
  --config-dir . \
  --config-name base_eval \
  -o target.api_endpoint.model_id=meta/llama-3.1-8b-instruct \
  -o target.api_endpoint.url=https://integrate.api.nvidia.com/v1/chat/completions

# Evaluate Mistral 7B
nemo-evaluator-launcher run \
  --config-dir . \
  --config-name base_eval \
  -o target.api_endpoint.model_id=mistralai/mistral-7b-instruct-v0.3 \
  -o target.api_endpoint.url=https://integrate.api.nvidia.com/v1/chat/completions

Step 3: Export and compare

# Export to MLflow
nemo-evaluator-launcher export <invocation_id_1> --dest mlflow
nemo-evaluator-launcher export <invocation_id_2> --dest mlflow

# Export to local JSON
nemo-evaluator-launcher export <invocation_id> --dest local --format json

# Export to Weights & Biases
nemo-evaluator-launcher export <invocation_id> --dest wandb

Workflow 4: Safety and Vision-Language Evaluation

Evaluate models on safety benchmarks and VLM tasks.

Checklist:

Safety/VLM Evaluation:
- [ ] Step 1: Configure safety tasks
- [ ] Step 2: Set up VLM tasks (if applicable)
- [ ] Step 3: Run evaluation

Step 1: Configure safety tasks

evaluation:
  tasks:
    - name: aegis              # Safety harness
    - name: wildguard          # Safety classification
    - name: garak              # Security probing

Step 2: Configure VLM tasks

# For vision-language models
target:
  api_endpoint:
    type: vlm  # Vision-language endpoint
    model_id: nvidia/llama-3.2-90b-vision-instruct
    url: https://integrate.api.nvidia.com/v1/chat/completions

evaluation:
  tasks:
    - name: ocrbench           # OCR evaluation
    - name: chartqa            # Chart understanding
    - name: mmmu               # Multimodal understanding

When to Use vs Alternatives

Use NeMo Evaluator when:

Need 100+ benchmarks from 18+ harnesses in one platform
Running evaluations on Slurm HPC clusters or cloud
Requiring reproducible containerized evaluation
Evaluating against OpenAI-compatible APIs (vLLM, TRT-LLM, NIMs)
Need enterprise-grade evaluation with result export (MLflow, W&B)

Use alternatives instead:

lm-evaluation-harness: Simpler setup for quick local evaluation
bigcode-evaluation-harness: Focused only on code benchmarks
HELM: Stanford's broader evaluation (fairness, efficiency)
Custom scripts: Highly specialized domain evaluation

Supported Harnesses and Tasks

Harness	Task Count	Categories
`lm-evaluation-harness`	60+	MMLU, GSM8K, HellaSwag, ARC
`simple-evals`	20+	GPQA, MATH, AIME
`bigcode-evaluation-harness`	25+	HumanEval, MBPP, MultiPL-E
`safety-harness`	3	Aegis, WildGuard
`garak`	1	Security probing
`vlmevalkit`	6+	OCRBench, ChartQA, MMMU
`bfcl`	6	Function calling v2/v3
`mtbench`	2	Multi-turn conversation
`livecodebench`	10+	Live coding evaluation
`helm`	15	Medical domain
`nemo-skills`	8	Math, science, agentic

Common Issues

Issue: Container pull fails

Ensure NGC credentials are configured:

docker login nvcr.io -u '$oauthtoken' -p $NGC_API_KEY

Issue: Task requires environment variable

Some tasks need HF_TOKEN or JUDGE_API_KEY:

evaluation:
  tasks:
    - name: gpqa_diamond
      env_vars:
        HF_TOKEN: HF_TOKEN  # Maps env var name to env var

Issue: Evaluation timeout

Increase parallelism or reduce samples:

-o +evaluation.nemo_evaluator_config.config.params.parallelism=8
-o +evaluation.nemo_evaluator_config.config.params.limit_samples=100

Issue: Slurm job not starting

Check Slurm account and partition:

execution:
  account: correct_account
  partition: gpu
  qos: normal  # May need specific QOS

Issue: Different results than expected

Verify configuration matches reported settings:

evaluation:
  nemo_evaluator_config:
    config:
      params:
        temperature: 0.0  # Deterministic
        num_fewshot: 5    # Check paper's fewshot count

CLI Reference

Command	Description
`run`	Execute evaluation with config
`status <id>`	Check job status
`info <id>`	View detailed job info
`ls tasks`	List available benchmarks
`ls runs`	List all invocations
`export <id>`	Export results (mlflow/wandb/local)
`kill <id>`	Terminate running job

Configuration Override Examples

# Override model endpoint
-o target.api_endpoint.model_id=my-model
-o target.api_endpoint.url=http://localhost:8000/v1/chat/completions

# Add evaluation parameters
-o +evaluation.nemo_evaluator_config.config.params.temperature=0.5
-o +evaluation.nemo_evaluator_config.config.params.parallelism=8
-o +evaluation.nemo_evaluator_config.config.params.limit_samples=50

# Change execution settings
-o execution.output_dir=/custom/path
-o execution.mode=parallel

# Dynamically set tasks
-o 'evaluation.tasks=[{name: ifeval}, {name: gsm8k}]'

Python API Usage

For programmatic evaluation without the CLI:

from nemo_evaluator.core.evaluate import evaluate
from nemo_evaluator.api.api_dataclasses import (
    EvaluationConfig,
    EvaluationTarget,
    ApiEndpoint,
    EndpointType,
    ConfigParams
)

# Configure evaluation
eval_config = EvaluationConfig(
    type="mmlu_pro",
    output_dir="./results",
    params=ConfigParams(
        limit_samples=10,
        temperature=0.0,
        max_new_tokens=1024,
        parallelism=4
    )
)

# Configure target endpoint
target_config = EvaluationTarget(
    api_endpoint=ApiEndpoint(
        model_id="meta/llama-3.1-8b-instruct",
        url="https://integrate.api.nvidia.com/v1/chat/completions",
        type=EndpointType.CHAT,
        api_key="nvapi-your-key-here"
    )
)

# Run evaluation
result = evaluate(eval_cfg=eval_config, target_cfg=target_config)

Advanced Topics

Multi-backend execution: See references/execution-backends.md Configuration deep-dive: See references/configuration.md Adapter and interceptor system: See references/adapter-system.md Custom benchmark integration: See references/custom-benchmarks.md

Requirements

Python: 3.10-3.13
Docker: Required for local execution
NGC API Key: For pulling containers and using NVIDIA Build
HF_TOKEN: Required for some benchmarks (GPQA, MMLU)

Resources

GitHub: https://github.com/NVIDIA-NeMo/Evaluator
NGC Containers: nvcr.io/nvidia/eval-factory/
NVIDIA Build: https://build.nvidia.com (free hosted models)
Documentation: https://github.com/NVIDIA-NeMo/Evaluator/tree/main/docs

FAQ

What does the nemo-evaluator-sdk skill do?

How do I install nemo-evaluator-sdk?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill nemo-evaluator-sdk in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/nemo-evaluator-sdk/ (personal) or .claude/skills/nemo-evaluator-sdk/ (project).

Which AI agents can use nemo-evaluator-sdk?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is nemo-evaluator-sdk safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains nemo-evaluator-sdk?

It is published by orchestra-research in orchestra-research/ai-research-skills. It has 948 recorded installs.