Skip to main content 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.
npx skills add zechenzhangagi/ai-research-skills --skill nemo-evaluator-sdk ai ai-research claude claude-code claude-skills codex
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
# 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
# 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.
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.
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.
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
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 evaluationbigcode-evaluation-harness : Focused only on code benchmarksHELM : Stanford's broader evaluation (fairness, efficiency)Custom scripts : Highly specialized domain evaluation
Supported Harnesses and Tasks Harness Task Count Categories lm-evaluation-harness60+ MMLU, GSM8K, HellaSwag, ARC simple-evals20+ GPQA, MATH, AIME bigcode-evaluation-harness25+ HumanEval, MBPP, MultiPL-E safety-harness3 Aegis, WildGuard garak1 Security probing vlmevalkit6+ OCRBench, ChartQA, MMMU bfcl6 Function calling v2/v3 mtbench2 Multi-turn conversation livecodebench10+ Live coding evaluation helm15 Medical domain nemo-skills8 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 runExecute evaluation with config status <id>Check job status info <id>View detailed job info ls tasksList available benchmarks ls runsList 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
Requirements
Python : 3.10-3.13Docker : Required for local executionNGC API Key : For pulling containers and using NVIDIA BuildHF_TOKEN : Required for some benchmarks (GPQA, MMLU)
Resources Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
