Knet

Knet is the Koç University deep learning framework that supports GPU operation and automatic differentiation using dynamic computational graphs for models defined in plain Julia. Knet uses dynamic computational graphs generated at runtime for automatic differentiation of (almost) any Julia code.

This allows machine learning models to be implemented by defining just the forward calculation using the full power and expressivity of Julia. The implementation can use helper functions, loops, conditionals, recursion, closures, tuples and dictionaries, array indexing, concatenation and other high level language features, some of which are often missing in the restricted modeling languages of static computational graph systems like Theano, Torch, Caffe and Tensorflow.

GPU operation is supported by simply using the KnetArray type instead of regular Array for parameters and data.Knet builds a dynamic computational graph by recording primitive operations during forward calculation. Only pointers to inputs and outputs are recorded for efficiency.

Therefore array overwriting is not supported during forward and backward passes. This encourages a clean functional programming style. High performance is achieved using custom memory management and efficient GPU kernels.

Knet relies on the AutoGrad package and the KnetArray data type for its functionality and performance. AutoGrad computes the gradient of Julia functions and KnetArray implements high performance GPU arrays with custom memory management.

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