AutoML Project with Ludwig

This project showcases an automated machine learning (AutoML) solution using Ludwig, Uber's open source, deep learning toolbox. Ludwig provides a unique and user-friendly interface to deep learning, which does not require the user to have extensive knowledge about the inner workings of deep learning models.

Features

Easy-to-Use: Ludwig is designed to be user-friendly and requires no programming experience. All you need to train a model and make predictions is a tabular dataset and a few lines of command-line code.
Flexible: Ludwig enables you to train a deep learning model using various types of input data such as text, images, and more. You can also combine different types of input data to create a hybrid model.
Versatile: Ludwig is suitable for a wide range of tasks such as image classification, text classification, and time series forecasting.

Getting Started

Prerequisites

Python 3.7+

Installation

Clone the repository to your local machine:

Install the dependencies:

pip install -r requirements.txt

Data Preparation

Ludwig requires the data to be in a tabular format such as CSV. The column names of the CSV file will be used as the feature names.

Usage

How to run the code to train a model:

python train_automl.py --data-path ./data/agnews.csv --config-path ./config/config.json

And to generate with a pytorch model:

make gen

And to predict with a pre-trained model:

python predict_automl_torch.py --predict-path ./data/text_to_predict.csv

Acknowledgements

This project makes use of the fantastic Ludwig toolbox developed by Uber's AI team. We thank them for their work and for making it open source.

Contact

If you have any questions or suggestions, please feel free to open an issue on this repository.

szheng3/automl