Kelp.Net was developed strongly around the Caffe style of development and supports many of its features.

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (approximately 2 ms per image). By separating model representation and actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment, from prototyping machines to cloud environments.