semvec/pypi-packages huggingface dataset:
tc-prcmmt-hks 1.0.3
Reusable pre‑commit hooks for TwinCAT projects
Requires Python: >=3.8


# pre-commit-hooks for TwinCAT
Some out-of-the-box pre-commit hooks for TwinCAT.

See also: https://github.com/pre-commit/pre-commit

## Using pre-commit-hooks with pre-commit
Add this to your .pre-commit-config.yaml

```
-   repo: https://github.com/janbirki/pre-commit-hooks.git
    rev: v1.0.0  # Use the ref you want to point at
    hooks:
    -   id: trailing-whitespace
    # -   id: ...
```

## Hooks available

### TwinCAT default core settings
This hook is changing the core settings to default by deleting the Element "Settings" inside your *.tsproj file

It's needed to run unit tests in a simulated enviromnent, where the actual available cores doesn't matter.

*Before*
![user defined core settings to run project locally](doc/img/user_defined_core_settings.png)

*After*
![default core settings twincat system](doc/img/after_running_script.png)
pipelight 0.0.1b5
This is a pipeline that is driven by configuration files for model running based on PyTorch Lightning.
Requires Python: >=3.7

# Pipelight: The Easiest Pipeline for Training, Validation and Test Based on PyTorch Lightning

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
[![Python Version](https://img.shields.io/badge/Python-3.7%2B-green)](https://www.python.org/)
[![PyTorch Version](https://img.shields.io/badge/PyTorch-2.0%2B-orange)](https://pytorch.org/)
[![PyTorch Lightning Version](https://img.shields.io/badge/PyTorch_Lightning-2.4.0%2B-blue)](https://lightning.ai/pytorch-lightning)

**Pipelight** is a pipeline that is driven by configuration files for model running based on PyTorch Lightning.

## Key Features
- **Reproducible Pipelines**: Built-in experiment tracking and deterministic training
- **Configuration-Driven Workflows**: Define experiments through YAML configs with PyTorch Lightning integration

## Installation
```bash
pip install pipelight
```

## Quick Start

### Configuration-Based Execution
Copy `pipelight/configs` as a template to the project folder, and enter this folder. All YAML files can be modified to meet the task requirements.

For training and validation, just run:
```bash
python -m pipelight.run --train configs/train.yaml --val configs/val.yaml -m {model config} -r configs/running/runner.yaml -n experiment_01
```

After that, run the following to test:
```bash
python -m pipelight.run --test configs/test.yaml -m {model config} -r configs/running/runner.yaml -n experiment_01
```
Make sure the name of the experiment should be kept the same.