akid

"akid is a python package written for doing research in Neural Network. It also aims to be production ready by taking care of concurrency and communication in distributed computing. It is built on Tensorflow. If combining with GlusterFS, Docker and Kubernetes, it is able to provide dynamic and elastic scheduling, auto fault recovery and scalability.
It aims to enable fast prototyping and production ready at the same time. More specifically, it

• supports fast prototyping
  • built-in data pipeline framework that standardizes data preparation and data augmentation.
  • arbitrary connectivity schemes (including multi-input and multi-output training), and easy retrieval of parameters and data in the network
  • meta-syntax to generate neural network structure before training
  • support for visualization of computation graph, weight filters, feature maps, and training dynamics statistics.
• be production ready
  • built-in support for distributed computing
  • compatibility to orchestrate with distributed file systems, docker containers, and distributed operating systems such as Kubernetes. (This feature mainly is a best-practice guide for K8s etc, which is under experimenting and not available yet.)

The name comes from the Kid saved by Neo in Matrix, and the metaphor to build a learning agent, which we call kid in human culture.

http://akid.readthedocs.io/en/latest/index.html

https://github.com/shawnLeeZX/akid/

https://arxiv.org/abs/1701.00609

http://hgpu.org/?p=16890