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A python package to study the theory of Deep Neural Networks

Project description

akid is a python package written for doing research in Neural Network (NN). It also aims to be production ready by taking care of concurrency and communication in distributed computing (which depends on the utilities provided by PyTorch and Tensorflow). It could be seen as a user friendly front-end to torch, or tensorflow, like Keras. It grows out of the motivation to reuse my old code, and in the long run, to explore alternative framework for building NN. It supports two backends, i.e., Tensorflow and Pytorch. If combining with GlusterFS, Docker and Kubernetes, it is able to provide dynamic and elastic scheduling, auto fault recovery and scalability (which is not to brag the capability of akid, since the features are not features of akid but features thanks to open source (and libre software), but to mention the possibilities that they can be combined.).

See for documentation. The document is dated, and has not been updated to include new changes e.g., the PyTorch backend. But the backbone design is the same, and main features are there.

NOTE: the PyTorch end support has been way ahead of Tensorflow support now ...

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