Deep Learning

Elephas – distributed deep learning with Keras and Spark

Elephas brings deep learning with Keras to Spark. Elephas intends to keep the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models, which can be run on massive data sets.

Elephas implements a class of data-parallel algorithms on top of Keras, using Spark’s RDDs and data frames. Keras Models are initialized on the driver, then serialized and shipped to workers, alongside with data and broadcasted model parameters. Spark workers deserialize the model, train their chunk of data and send their gradients back to the driver. The “master” model on the driver is updated by an optimizer, which takes gradients either synchronously or asynchronously.

This is free and open source software.

Website: github.com/danielenricocahall/elephas
Support:
Developer: Max Pumperla
License: MIT License

Elephas is written in Python. Learn Python with our recommended free books and free tutorials.


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PyTorch LightningFramework for scaling PyTorch training workflows
JAXHigh-performance numerical computing and ML library
PyTensorLibrary for fast numerical computation
MindSporeFramework for training and deploying neural networks
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ChainerPowerful, flexible, and intuitive framework for neural networks
EquinoxNeural network library for building flexible models with JAX
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NeupyPython library for neural networks and deep learning

Read our verdict in the software roundup.


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