  In order to contrast the explosion in size of state-of-the-art machine
learning models that can be attributed to the empirical advantages of
over-parametrization, and due to the necessity of deploying fast, sustainable,
and private on-device models on resource-constrained devices, the community has
focused on techniques such as pruning, quantization, and distillation as
central strategies for model compression. Towards the goal of facilitating the
adoption of a common interface for neural network pruning in PyTorch, this
contribution describes the recent addition of the PyTorch torch.nn.utils.prune
module, which provides shared, open source pruning functionalities to lower the
technical implementation barrier to reducing model size and capacity before,
during, and/or after training. We present the module's user interface,
elucidate implementation details, illustrate example usage, and suggest ways to
extend the contributed functionalities to new pruning methods.
