Skip to content

README.md

Latest commit

 

History

History
78 lines (51 loc) · 2.03 KB

README.md

File metadata and controls

78 lines (51 loc) · 2.03 KB

PLCT AI Data Unifier Runner

Minimal runner wrapper for: https://github.com/Petlja/PLCT-AI-Data-Unifier

This repository is intentionally small. It uses the package above as a dependency and only provides a local config and one batch entry point for Windows users.

What this runner does

  • Reads repositories from plct-ai-data-unifier-config.yaml.
  • Uses uv to install and lock dependencies from pyproject.toml.
  • Runs the PLCT AI Data Unifier command flow.
  • Keeps generated output on disk after the run is finished.

Configuration

Edit plct-ai-data-unifier-config.yaml and keep your repository list under:

repos:
  - url: https://github.com/Petlja/example-repo

You can include as many repos as needed.

Run

  1. Install uv.
  2. Edit plct-ai-data-unifier-config.yaml.
  3. Run run-au.bat.

Default run is full bootstrap and produces reusable output folders.

Batch script modes

run-au.bat [config-file] [mode] [base-dir] [output-dir] [jobs]

Supported modes:

  • bootstrap
  • git-sync
  • prepare-dataset

Examples:

  • Full run (default values) run-au.bat

  • Full run with explicit config and serial conversion run-au.bat plct-ai-data-unifier-config.yaml bootstrap repos dataset 1

  • Only sync repositories run-au.bat plct-ai-data-unifier-config.yaml git-sync repos

  • Only build dataset from already synced repositories run-au.bat plct-ai-data-unifier-config.yaml prepare-dataset repos dataset 4

Equivalent direct uv commands (same as package README, without batch wrapper):

uv sync
uv run plct-ai-data-unifier bootstrap --config plct-ai-data-unifier-config.yaml --base-dir repos --output-dir dataset --jobs 1

# or step by step
uv run plct-ai-data-unifier git-sync --config plct-ai-data-unifier-config.yaml --base-dir repos
uv run plct-ai-data-unifier prepare-dataset --base-dir repos --output-dir dataset --jobs 1

Output folders are preserved

After a successful run, folders are kept so users can continue to work with the result without rerunning everything:

  • repos
  • dataset

No cleanup is performed by run-au.bat.