What is model pruning in PyTorch?

What is model pruning in PyTorch?

Model pruning in PyTorch is a technique to reduce the size of a neural network by removing weights that are deemed unnecessary.

How does pruning affect model performance?

Pruning can improve inference speed and reduce memory usage, but it may also lead to a slight decrease in accuracy if not done carefully.

Can you explain the different types of pruning methods?

Sure! Common methods include weight pruning, neuron pruning, and structured pruning, each targeting different aspects of the model.

How do I implement pruning in my PyTorch model?

You can use the torch.nn.utils.prune module, which provides various pruning methods and utilities to apply them to your model.