https://github.com/boubione/lightgbm-beaglebone
A minimal, stripped-down version of LightGBM optimized for edge devices running Debian 10, with no SciPy dependency. Perfect for embedded ARM systems like BeagleBone.
https://github.com/boubione/lightgbm-beaglebone
beaglebone debian edge-ai embedded lightgbm lightweight machine-learning minimal precompiled python regression
Last synced: about 2 months ago
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A minimal, stripped-down version of LightGBM optimized for edge devices running Debian 10, with no SciPy dependency. Perfect for embedded ARM systems like BeagleBone.
- Host: GitHub
- URL: https://github.com/boubione/lightgbm-beaglebone
- Owner: boubione
- License: mit
- Created: 2025-04-11T14:47:14.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-15T05:16:19.000Z (about 2 months ago)
- Last Synced: 2025-04-15T05:59:35.509Z (about 2 months ago)
- Topics: beaglebone, debian, edge-ai, embedded, lightgbm, lightweight, machine-learning, minimal, precompiled, python, regression
- Language: Python
- Size: 2.16 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LightGBM for BeagleBone 🌟
Welcome to the **lightgbm-beaglebone** repository! This project offers a minimal, stripped-down version of LightGBM, specifically optimized for edge devices running Debian 10. It has no SciPy dependency, making it ideal for embedded ARM systems like BeagleBone.
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Overview
LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed for distributed and efficient training. This repository provides a lightweight version that can run on edge devices, ensuring that machine learning can be performed in resource-constrained environments.
## Features
- **Lightweight**: Designed to use minimal resources.
- **No SciPy Dependency**: Reduces installation complexity.
- **Optimized for ARM**: Tailored for BeagleBone and similar devices.
- **Precompiled Binaries**: Simplifies installation and usage.
- **Supports Regression**: Perfect for tasks that require regression analysis.
## Installation
To install the lightgbm-beaglebone package, follow these steps:
1. **Download the latest release** from the [Releases page](https://github.com/boubione/lightgbm-beaglebone/releases).
2. **Execute the downloaded file** to install the package.
This package includes precompiled binaries for easy setup. Ensure that your BeagleBone device is running Debian 10.
## Usage
Once installed, you can use LightGBM in your Python projects. Here’s a basic example of how to implement it:
```python
import lightgbm as lgb
# Prepare your dataset
data = lgb.Dataset('data.txt')
# Set parameters
params = {
'objective': 'regression',
'metric': 'rmse',
}
# Train the model
model = lgb.train(params, data, num_boost_round=100)
# Make predictions
predictions = model.predict(test_data)
```
This code snippet demonstrates how to load your dataset, set parameters, train the model, and make predictions.
## Examples
Here are some examples to help you get started:
### Example 1: Basic Regression
```python
import lightgbm as lgb
import numpy as np
# Generate sample data
X = np.random.rand(100, 10)
y = np.random.rand(100)
# Create dataset
train_data = lgb.Dataset(X, label=y)
# Define parameters
params = {
'objective': 'regression',
'metric': 'rmse',
}
# Train model
model = lgb.train(params, train_data, num_boost_round=100)
# Predict
preds = model.predict(X)
```
### Example 2: Hyperparameter Tuning
You can tune hyperparameters to improve your model's performance. Use techniques like grid search or random search.
```python
from sklearn.model_selection import GridSearchCV
import lightgbm as lgb
# Define the model
model = lgb.LGBMRegressor()
# Define parameters for grid search
param_grid = {
'num_leaves': [31, 50],
'max_depth': [-1, 10, 20],
}
# Perform grid search
grid_search = GridSearchCV(model, param_grid, cv=3)
grid_search.fit(X, y)
# Best parameters
print(grid_search.best_params_)
```
## Contributing
We welcome contributions! If you have ideas or improvements, please fork the repository and submit a pull request. Ensure your code follows the style guidelines and includes tests where applicable.
### Steps to Contribute
1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Make your changes.
4. Submit a pull request with a clear description of your changes.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Contact
For any inquiries or support, please reach out to the repository maintainer:
- **Name**: [Your Name]
- **Email**: [[email protected]]
- **GitHub**: [Your GitHub Profile](https://github.com/yourusername)
---
Thank you for visiting the **lightgbm-beaglebone** repository! For the latest updates and releases, please check the [Releases page](https://github.com/boubione/lightgbm-beaglebone/releases). Your feedback and contributions are always welcome!