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Apache Horn

The Apache Horn is an Apache Incubating project, allowing you to do Downpour SGD based large-scale deep learning, using the heterogeneous resources on existing Hadoop and Hama clusters. It is originally inspired by Google's DistBelief (Jeff Dean et al, 2012). Its architecture is designed with an intuitive programming model based on the neuron-centric abstraction. 

Why Apache Horn?

Deep learning and unsupervised feature learning have shown great promise in many practical applications. State-of-the-art performance has been reported in several domains, ranging from speech recognition visual object recognition, to text processing. 

It has been observed that increasing the scale of Deep Learning model, in terms of model parameters in proportion to training examples, can drastically improve ultimate classification accuracy. There has been lot of research regarding making the architecture and optimization algorithm feasible to handle large models having billions of parameters. One such way is to increase parallelism by allowing model as well as data parallelism. 

Horn’s architecture takes into account the requirement for model parallelism and follows the recently introduced approach of distributed Parameter server. For data parallelism, it makes use of Hama’s master-groom framework.

With more involved Distributed optimization and inference, we plan to achieves Sate-of-the-art performance on training of large-scale Deep learning algorithms.

Goals

As a Distributed framework, we first plan to reduce total training time to achieve certain accuracy with more computing resources using Downpour SGD training method for feed forward models including Convolutional Neural Network (CNN), Auto encoder.

In Apache Horn, we are planning to support various Distributed training frameworks like Sandblaster, AllReduce, and Distributed Hogwild and acceleration.

In the end we would like to extend it to make it usable with other Deep Learning models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN)

Principles

We want to provide general architecture to exploit the scalability of various training methods. Synchronous methods increase efficiency and ensure consistency while asynchronous methods increase the convergence rate. We plan to introduce a hybrid approach which has best of both the worlds.

We plan to use BSP computing framework as the base of our computation. BSP framework for parallel computation makes logic and implementation much cleaner, one the biggest challenge of implementing distributed algorithms.

Further, programming model is based on the neuron centric abstraction, which is intuitive for deep learning models. 

Getting Involved

HORN is an open source volunteer project under the Apache Software Foundation.

Currently, a number of researchers and developers from various organizations, such as Microsoft, Samsung Electronics, Seoul National University, Technical University of Munich, KAIST, LINE plus, and Cldi Inc., are involved in the Horn project.

We encourage you to learn about the project and contribute your expertise.

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