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I4h Workflow Finetune

NVIDIAOfficialCreated by Isaac for Healthcare TeamDistributed by NVIDIA

  • Skill
  • Workflow
  • Data and AI
  • Apache-2.0
  • Medium risk

From the author

Fine-tune a manifest-backed GR00T or openpi remote Task on compatible LeRobot data. Use for training; do not use for inference-only Tasks or checkpoint rollout.
— the description by Isaac for Healthcare Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured procedure for fine-tuning robotic policy tasks within the Isaac for Healthcare framework. It guides the user through resolving workflow dependencies, verifying dataset compatibility, performing dry-run configuration checks, executing the training process, and validating the resulting checkpoint through a load-smoke test. The workflow ensures that training parameters are preserved exactly as requested and that the final artifacts are verified for usability.

What it does

  • Resolves base checkouts, policy modes, and remote task manifests.
  • Verifies LeRobot dataset compatibility with the target task.
  • Executes a dry-run to validate configuration settings before training.
  • Performs foreground training with specified hyperparameters.
  • Conducts a bounded load-smoke test to verify checkpoint integrity.
  • Reports detailed training outcomes, disk usage, and validation status.

When to use it

  • When you need to fine-tune a GR00T or openpi remote task.
  • When you have a compatible LeRobot dataset ready for training.
  • When you need to ensure training configurations are validated before execution.
  • When you must verify that a trained checkpoint is loadable by the inference backend.

Advantages

  • Ensures configuration consistency by requiring exact parameter preservation.
  • Prevents deployment of broken checkpoints via mandatory load-smoke testing.
  • Provides a clear, repeatable process for managing complex robotic training workflows.
  • Integrates validation steps to confirm artifact usability post-training.

Usage examples

“Fine-tune the current task for 200 steps with a batch size of 32 and turn off vision tuning.”
The agent will perform a dry-run with the specified parameters, apply the GR00T vision flag, execute the training, perform a load-smoke test, and report the final checkpoint path.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/i4h-workflow-finetune/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip i4h-workflow-finetune.zip -d .agents/skills

Documentation by Isaac for Healthcare Team

Fine-tune a Workflow Policy Task

Purpose

Resolve and run training from the selected workflow run mode and owning remote-task manifest.

Instructions

  1. Resolve the base checkout, policy mode, and remote task.

  2. Verify dataset compatibility and a train block.

  3. Dry-run the exact configuration.

  4. Train in the foreground and verify checkpoint artifacts.

Resolve the workflow, task, and data

export I4H_WORKFLOWS_REPO_URL="${I4H_WORKFLOWS_REPO_URL:-https://github.com/isaac-for-healthcare/i4h-workflows}"
I4H_REPO_DIR_NAME="${I4H_WORKFLOWS_REPO_URL%/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*:}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME%.git}"
[ -n "$I4H_REPO_DIR_NAME" ] || { echo "Cannot derive a checkout name from I4H_WORKFLOWS_REPO_URL" >&2; exit 2; }
ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/i4h_workflows" ]; then
  ROOT="${I4H_WORKFLOWS:-$HOME/$I4H_REPO_DIR_NAME}"
  [ -d "$ROOT/workflows/i4h_workflows" ] || git clone "$I4H_WORKFLOWS_REPO_URL" "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"
cd "$ROOT"
./run.sh list
./run.sh show <workflow> --mode <policy-mode>
test -f /absolute/path/to/dataset/meta/info.json
nvidia-smi

Treat the resolver above as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.

Read the selected workflow run mode to identify its remote task id. Open tasks/<project>/i4h_tasks/<project>/manifest/<task>.yaml and require train:. Resolve the project, entry point, base model/config, output defaults, and modality contract from that manifest and the project's train.py.

Use the current-chain LeRobot dataset when the prompt omits a path. Verify its embodiment, cameras, task text, feature widths, and episode count are compatible with the remote task.

Resolve configuration before a long run

All policy train entry points support --dry-run:

uv run --project "tasks/<project>" "i4h-tasks-<project-with-hyphens>-train" \
  --task <project>/<task> \
  --dataset /absolute/path/to/dataset \
  --output-dir /absolute/path/to/checkpoints \
  --max-steps <N> \
  --batch-size <N> \
  --dry-run

Inspect the resolved config. Keep user-requested steps, batch size, model/config, and GPU count exact.

For GR00T, “turn off vision tuning” maps to --no-tune-visual. Do not pass that flag to openpi, whose CLI does not expose it. Use only flags present in the selected project's current train.py.

Train

Remove --dry-run and keep the command in the foreground:

uv run --project "tasks/<project>" "i4h-tasks-<project-with-hyphens>-train" \
  --task <project>/<task> \
  --dataset /absolute/path/to/dataset \
  --output-dir /absolute/path/to/checkpoints \
  --max-steps <N> \
  --save-steps <N> \
  --batch-size <N> \
  --num-gpus <N>

Add backend-specific flags only after resolving them. Do not silently lower requested steps or batch size to make training fit.

Verify

Require exit status 0, completed requested steps, saved training logs, and at least one loadable checkpoint artifact. Resolve the exact checkpoint path rather than calling an incomplete output directory a checkpoint.

Run a bounded backend load smoke before reporting the checkpoint usable:

uv run --project "tasks/<project>" python -m "<project>.server" \
  --namespace "checkpoint-smoke-$$" \
  --preload <project>/<task> \
  --checkpoint /absolute/path/to/checkpoint \
  --preload-only

Use the selected project's actual module path. --preload-only loads the manifest and checkpoint through the inference backend, then exits without starting a rollout. A training exit alone proves that files were written, not that inference can load them.

Report du -sh for the task output and the selected checkpoint. Some trainers save both a final model at the output root and numbered checkpoints; identify that duplication, but do not delete either copy unless the user explicitly asks for cleanup.

Hand the exact load-smoked checkpoint path to i4h-workflow-validate; do not evaluate unless the user requested it.

Troubleshooting

Report the first dataset, manifest, model-access, GPU-memory, or backend error. Preserve logs and never silently change requested hyperparameters.

Prerequisites

Require a compatible LeRobot dataset, synced policy environment, model access, GPU capacity, and a remote-task manifest with train:.

Limitations

Inference-only Tasks cannot be fine-tuned, and this skill does not claim rollout success from training alone.

Examples

  • Fine-tune for 200 steps with a batch size of 32. Turn off vision tuning. → preserve exact values, apply GR00T's supported vision flag, dry-run, train, and report the checkpoint.

Completion gate

Report workflow/mode, task id and manifest, dataset compatibility, resolved config, requested/completed steps, batch/GPU/vision settings, checkpoint path, bounded load-smoke result, output/checkpoint disk sizes, exact validation handoff, exit summary, and any inference-only or resource blocker.

Files

LICENSE

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

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Attribution and licence

Created by
Isaac for Healthcare Team
Publisher
NVIDIA (Official)
Distributed by
NVIDIA
Source
NVIDIA/skills · skills/i4h-workflow-finetune · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Isaac for Healthcare Team or NVIDIA.

Download ZIP

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