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