nemo-evaluator-sdk

name	nemo-evaluator-sdk
description	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.
version	1.0.0
author	Orchestra Research
license	MIT
tags	["Evaluation","NeMo","NVIDIA","Benchmarking","MMLU","HumanEval","Multi-Backend","Slurm","Docker","Reproducible","Enterprise"]
dependencies	["nemo-evaluator-launcher>=0.1.25","docker"]

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