Rubix ML
A high-level machine learning and deep learning library for the PHP language.
- Developer-friendly API is delightful to use.
- 40+ supervised and unsupervised learning algorithms.
- Support for ETL, preprocessing, and cross-validation.
- Open source and free to use commercially.
Requirements
- PHP 8.3 or above.
Recommended
- Tensor Ext 4.1+ for fast Matrix/Vector computing.
Optional
- Swoole extension for fast multiprocessing support.
- GD extension for image support.
- Mbstring extension for fast multibyte string manipulation.
- SVM extension for Support Vector Machine engine (libsvm).
- PDO extension for relational database support.
- GraphViz for graph visualization.
Installation
Install Rubix ML into your project using Composer:
composer require rubix/ml
Optional for best performance
Make sure you have all the necessary build tools installed such as a C compiler and make tools. For example, on an Ubuntu linux system you can enter the following on the command line to install the necessary dependencies.
sudo apt-get install make gcc gfortran php-dev libopenblas-dev liblapacke-dev re2c build-essential
Compile and install the Tensor 4.1+ extension using PIE:
pie install rubix/tensor_ext:^4.1
Documentation
Read the latest docs here.
What is Rubix ML?
Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects.
Getting Started
If you are new to machine learning, we recommend taking a look at the What is Machine Learning? section to get started. If you are already familiar with basic ML concepts, you can browse the basic introduction for a brief look at a typical Rubix ML project. From there, you can browse the official tutorials below which range from beginner to advanced skill level.
Tutorials & Example Projects
Check out these example projects using the Rubix ML library. Many come with instructions and a pre-cleaned dataset.
- CIFAR-10 Image Recognizer
- Color Clusterer
- Credit Default Risk Predictor
- Customer Churn Predictor
- Divorce Predictor
- DNA Taxonomer
- Dota 2 Game Outcome Predictor
- Human Activity Recognizer
- Housing Price Predictor
- Iris Flower Classifier
- MNIST Handwritten Digit Recognizer
- Text Sentiment Analyzer
- Titanic Survival Predictor
Interact With The Community
Contributing
See CONTRIBUTING.md for guidelines.
License
The code is licensed MIT and the documentation is licensed CC BY-NC 4.0.