nvidia-model-optimizer-launching-evals

name	nvidia-model-optimizer-launching-evals
description	Run, monitor, analyze, and debug LLM evaluations via nemo-evaluator-launcher. Covers running evaluations, checking status and live progress, debugging failed runs, exporting artifacts and logs, and analyzing results. ALWAYS triggers on mentions of running evaluations, checking progress, debugging failed evals, analyzing or analysing runs or results, run directories or artifact paths on clusters, Slurm job issues, invocation IDs, or inspecting logs (client logs, server logs, SSH to cluster, tail logs, grep logs). Do NOT use for creating or modifying evaluation configs.
license	Apache-2.0
metadata	{"author":"NVIDIA"}

NeMo Evaluator Skill

Quick Reference

nemo-evaluator-launcher CLI

# Run evaluation
uv run nemo-evaluator-launcher run --config <path.yaml>
uv run nemo-evaluator-launcher run --config <path.yaml> -t <a_single_task_to_be_run_by_name>
uv run nemo-evaluator-launcher run --config <path.yaml> -t <task_name_1> -t <task_name_2> ...
uv run nemo-evaluator-launcher run --config <path.yaml> -o evaluation.nemo_evaluator_config.config.params.limit_samples=10 ...

# Preview the resolved config and the sbatch script without running the evaluation
uv run nemo-evaluator-launcher run --config <path.yaml> --dry-run

# Check status (--json for machine-readable output)
uv run nemo-evaluator-launcher status <invocation_id> --json

# Get evaluation run info (output paths, slurm job IDs, cluster hostname, etc.)
uv run nemo-evaluator-launcher info <invocation_id>

# Copy just the logs (quick — good for debugging)
uv run nemo-evaluator-launcher info <invocation_id> --copy-logs ./evaluation-results/

# For artifacts: use `nel info` to discover paths. If remote, SSH to explore and rsync what you need.
# If local, just read directly from the paths shown by `nel info`.
# ssh <user>@<hostname> "ls <artifacts_path>/"
# rsync -avzP <user>@<hostname>:<artifacts_path>/{results.yml,eval_factory_metrics.json,config.yml} ./evaluation-results/<invocation_id>.<job_index>/artifacts/

# Resume a failed/interrupted run (re-sbatches existing run.sub in the original run directory)
uv run nemo-evaluator-launcher resume <invocation_id>

# List past runs
uv run nemo-evaluator-launcher ls runs --since 1d

# List available evaluation tasks (by default, only shows tasks from the latest released containers)
uv run nemo-evaluator-launcher ls tasks
uv run nemo-evaluator-launcher ls tasks --from_container nvcr.io/nvidia/eval-factory/simple-evals:26.03

Workflow

The complete evaluation workflow is divided into the following steps you should follow IN ORDER.

1. Create or modify a config using the nel-assistant skill. If the user provides a past run, use its config.yml artifact as a starting point.
2. Run the evaluation. See references/run-evaluation.md when executing this step.
3. Monitor progress (MANDATORY after every nel run): poll status repeatedly until SUCCESS/FAILED. See references/check-progress.md.
4. Post-run actions (when terminal state reached):
   1. When the evaluation status is SUCCESS, analyze the results. See references/analyze-results.md when executing this step.
   2. When the evaluation status is FAILED, debug the failed run. See references/debug-failed-runs.md when executing this step.

Key Facts

• Benchmark-specific info learned during launching/analyzing evals should be added to references/benchmarks/
• PPP = Slurm account (the account field in cluster_config.yaml). When the user says "change PPP to X", update the account value (e.g., coreai_dlalgo_compeval → coreai_dlalgo_llm).
• Slurm job pairs: NEL (nemo-evaluator-launcher) submits paired Slurm jobs — a RUNNING job + a PENDING restart job (for when the 4h walltime expires). Never cancel the pending restart jobs — they are expected and necessary.
• HF cache requirement: For configs with HF_HUB_OFFLINE=1, models must be pre-downloaded to the HF cache on each cluster before launching. Before running a model on a new cluster, always ask the user if the model is already cached there. If not, on the cluster login node: python3 -m venv hf_cli && source hf_cli/bin/activate && pip install huggingface_hub then HF_HOME=/lustre/fsw/portfolios/coreai/users/<username>/cache/huggingface hf download <model>. Without this, vLLM will fail with LocalEntryNotFoundError.
• data_parallel_size is per node: dp_size=1 with num_nodes=8 means 8 model instances total (one per node), load-balanced by haproxy. Do NOT interpret dp_size as the global replica count.
• payload_modifier interceptor: The params_to_remove list (e.g. [max_tokens, max_completion_tokens]) strips those fields from the outgoing payload, intentionally lifting output length limits so reasoning models can think as long as they need.
• Auto-export git workaround: The export container (python:3.12-slim) lacks git. When installing the launcher from a git URL, set auto_export.launcher_install_cmd to install git first (e.g., apt-get update -qq && apt-get install -qq -y git && pip install "nemo-evaluator-launcher[all] @ git+...#subdirectory=packages/nemo-evaluator-launcher").
• Do NOT use nemo-evaluator-launcher export --dest local — it only writes a summary JSON (processed_results.json), it does NOT copy actual logs or artifacts despite accepting --copy_logs and --copy-artifacts flags. nel info --copy-artifacts works but copies everything (very slow for large benchmarks). Preferred approach: use to discover paths — if local, read directly; if remote, SSH to explore and rsync only what you need. Note that prints standard artifacts but benchmarks produce additional artifacts in subdirs — explore to find them.