Troubleshooting#

Comprehensive troubleshooting guide for NeMo Evaluator evaluations, organized by problem type and complexity level.

This section provides systematic approaches to diagnose and resolve evaluation issues. Start with the quick diagnostics below to verify your basic setup, then navigate to the appropriate troubleshooting category based on where your issue occurs in the evaluation workflow.

Quick Start#

Before diving into specific problem areas, run these basic checks to verify your evaluation environment:

# Verify launcher installation and basic functionality
nemo-evaluator-launcher --version

# List available tasks
nemo-evaluator-launcher ls tasks

# Validate configuration without running
nemo-evaluator-launcher run --config-dir packages/nemo-evaluator-launcher/examples --config-name local_llama_3_1_8b_instruct --dry-run

# Check recent runs
nemo-evaluator-launcher ls runs

import requests

# Check health endpoint (adjust based on your deployment)
# vLLM/SGLang/NIM: use /health
# NeMo/Triton: use /v1/triton_health
health_response = requests.get("http://0.0.0.0:8080/health", timeout=5)
print(f"Health Status: {health_response.status_code}")

# Test completions endpoint
test_payload = {
    "prompt": "Hello",
    "model": "megatron_model", 
    "max_tokens": 5
}
response = requests.post("http://0.0.0.0:8080/v1/completions/", json=test_payload)
print(f"Completions Status: {response.status_code}")

from nemo_evaluator import show_available_tasks

try:
    print("Available frameworks and tasks:")
    show_available_tasks()
except ImportError as e:
    print(f"Missing dependency: {e}")

Troubleshooting Categories#

Choose the category that best matches your issue for targeted solutions and debugging steps.

Setup & Installation

Installation problems, authentication setup, and model deployment issues to get NeMo Evaluator running.

Setup and Installation Issues
Runtime & Execution

Configuration validation and launcher management during evaluation execution.

Runtime and Execution Issues

Getting Help#

Log Collection#

When reporting issues, include:

  1. System Information:

    python --version
pip list | grep nvidia
nvidia-smi

  2. Configuration Details:

    print(f"Task: {eval_cfg.type}")
print(f"Endpoint: {target_cfg.api_endpoint.url}")
print(f"Model: {target_cfg.api_endpoint.model_id}")

  3. Error Messages: Full stack traces and error logs

Community Resources#

Professional Support#

For enterprise support, contact: nemo-toolkit@nvidia.com