nemo-evaluator-sdk

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

Details, code examples and parameter tables: references/python-api-usage.md. Read it when this step applies.

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

Agent operating procedure

1. Check the environment. Confirm the model, checkpoint, benchmark version and evaluation harness version.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Evaluate on a small subset to verify prompts, parsing and scoring.
4. Execute the full task using the instructions and references above.
5. Validate the result. Use the benchmark's official splits and metrics; check for contamination; report variance across seeds or prompts.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
Scores differ from published numbers	Check prompt format, few-shot settings and harness version before concluding.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Report evaluation settings in full; never cherry-pick prompts, seeds or subsets.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• evaluating-llms-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag).
• evaluating-code-models: Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics.
• nextflow: Build, run, and debug Nextflow data pipelines and nf-core workflows end to end.