dist-keras | Distributed Deep Learning , with a focus

dist-keras is a Python library typically used in Big Data, Deep Learning, Pytorch, Tensorflow, Spark, Hadoop applications. dist-keras has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Distributed Keras is a distributed deep learning framework built op top of Apache Spark and Keras, with a focus on "state-of-the-art" distributed optimization algorithms. We designed the framework in such a way that a new distributed optimizer could be implemented with ease, thus enabling a person to focus on research. Several distributed methods are supported, such as, but not restricted to, the training of ensembles and models using data parallel methods. Most of the distributed optimizers we provide, are based on data parallel methods. A data parallel method, as described in [1], is a learning paradigm where multiple replicas of a single model are used to optimize a single objective. Using this approach, we are able to dignificantly reduce the training time of a model. Depending on the parametrization, we also observed that it is possible to achieve better statistical model performance compared to a more traditional approach (e.g., like the SingleTrainer implementation), and yet, spending less wallclock time on the training of the model. However, this is subject to further research. Attention: A rather complete introduction to the problem of Distributed Deep Learning is presented in my Master Thesis Furthermore, the thesis describes includes several novel insights, such as a redefinition of parameter staleness, and several new distributed optimizers such as AGN and ADAG.