hpc-dataset-adaptation

name	hpc-dataset-adaptation
description	Use when the user's dataset format differs from what the target repo's data loaders expect, to adapt the code (not the data) for compatibility.

HPC Dataset Adaptation

Overview

Use when the target repo's loader does not read the user's dataset directly. Adapt code to read the data as-is; do not bulk-convert or copy large datasets unless the user explicitly asks.

When to Use

• User data has a different schema, layout, or file format than the repo expects.
• Training works on demo/repo data but not on the user's data.
• The dataset is too large to casually copy or rewrite.

Skip this if the data already matches the repo's expected format.

Agent Algorithm

1. Preflight

   • Confirm the repo's normal training smoke test works with expected/demo data.
   • Identify the user's dataset path and the container/runtime used for training.

2. Inspect actual data inside the container

   • Use native libraries for the format to record keys, shapes, dtypes, lengths, modalities, state/action layout, and timestamps.
   • Save or paste a small schema summary; do not copy or rewrite the dataset.

3. Inspect expected loader contract

   • Read the repo's dataset classes/configs.
   • Identify expected keys, shapes, dtype, normalization, windowing, and language/action/state mappings.

4. Map the gap

   • Write the mapping from user fields to expected fields.
   • If required data is missing or ambiguous, stop and ask.

5. Implement adapter/loader

   • Add code in the target repo that reads the user's format directly and emits the repo's expected sample structure.
   • Register it through the repo's normal config mechanism.

6. Verify

   • Instantiate the loader inside the container and fetch samples.
   • Assert keys, shapes, and dtypes.
   • Run a small training smoke test with the real entry point.
   • Record evidence before proceeding to full training.

Quick Reference

Goal	Approach
Inspect dataset schema	Use native Python library inside the container
Find expected loader	Read dataset classes and config files
Validate adapter	Instantiate dataset, fetch sample, assert keys/shapes/dtypes
Smoke test training	Same entry point with small batch/few steps

Stop Gates

• Dataset path unavailable inside the container.
• Required modality/key is missing or semantically ambiguous.
• Loader sample does not match expected shape/dtype.
• Training smoke test fails.

Common Mistakes

• Converting/copying large datasets instead of writing a loader.
• Inspecting data on the host instead of inside the container.
• Loading whole trajectories into memory for large datasets.
• Forgetting to register the loader/config.
• Testing the loader but skipping a training step.