Condensa is a framework for programmable model compression in Python. It comes with a set of built-in compression operators which may be used to compose complex compression schemes targeting specific combinations of DNN architecture, hardware platform, and optimization objective. To recover any accuracy lost during compression, Condensa uses a constrained optimization formulation of model compression and employs an Augmented Lagrangian-based algorithm as the optimizer.

Tensor methods generalize matrix algebraic operations to higher-orders, and can help deep neural networks better preserve and leverage local structure. TensorLy-Torch is a PyTorch library that builds on top of TensorLy and provides out-of-the-box tensor layers. It comes with all batteries included and tries to make it as easy as possible to use tensor methods within your deep networks.

Nitro is a programmer-directed code variant tuning framework, jointly developed by the University of Utah and NVIDIA Research. It utilizes machine learning-based classification to automatically find the best implementation (variant) of a computation for a given input. Nitro provides C++ and Python interfaces for programmers to specify variants, input dataset features, and constraints.