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dist-deep

dist-deep is a deep learning library implemented in C, designed for distributed training using MPI (Message Passing Interface) and OpenMP (Open Multi-Processing). This library allows users to define, train, and evaluate deep learning models efficiently on distributed systems.

Features

  • Layer Abstraction: Support for various layer types, including dense layers.
  • Distributed Training: Utilizes MPI for parallel processing across multiple nodes.
  • Backpropagation: Implements backpropagation for updating weights and biases.
  • Flexible Configuration: Easy to configure models with customizable parameters.

Installation

To get started with dist-deep, clone the repository and compile the source code.

git clone https://github.com/saran-sankar/dist-deep.git
cd dist-deep
make

Usage

Example

example.c: Demonstrates how to load the Iris dataset, configure a model, and train it using the dist-deep library.

File Structure

  • include/
    • bprop.h: Contains functions for backpropagation.
    • estimator.h: Contains the model training functions.
    • layers.h: Defines the structure of layers and activation functions.
    • fprop.h: Contains functions for forward propagation.

Contributing

Contributions are welcome! Please feel free to open issues or submit pull requests for enhancements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments