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---
name: vinn-offline
description: "Routes VINN feature caching and non-interactive k-selection
workflows for ACT++ BYOL/ResNet episode features."
disable-model-invocation: true
metadata:
disco-role: operating
license: MIT
---
# vinn-offline
Use this sub-skill when the task is about caching image features for VINN or choosing a k value from cached episode features.
## Typical triggers
- "Cache BYOL features for the simulated cube dataset"
- "Choose k for VINN"
- "How are feature files named?"
- "Why does the raw k-selection script stop in IPython?"
## What this sub-skill covers
- Feature caching from per-camera ResNet18 checkpoints.
- Feature-file naming and layout for simulated and cotrain variants.
- Offline nearest-neighbor k selection over cached features.
- CUDA requirements and dataset-index assumptions for VINN preprocessing.
## What it excludes
- ACT/CNNMLP/Diffusion training and eval -> [policy-training](../policy-training/SKILL.md).
- Simulation episode generation / replay / visualization -> [simulation-data](../simulation-data/SKILL.md).
- Real-robot VINN deployment -> root troubleshooting only.
## Read these first
- [Workflow recipes](references/workflows.md)
- [Troubleshooting](references/troubleshooting.md)
- [Data formats](../../references/data-formats.md)
## Run this helper first
Before a long cache or k-selection job, use [check_vinn_stack.py](scripts/check_vinn_stack.py) to confirm the repo checkout imports and the CUDA backend is visible.
