TorchMetrics

TorchMetrics is a really nice and convenient library that lets us compute the performance of models in an iterative fashion. It's designed with PyTorch (and PyTorch Lightning) in mind, but it is a general-purpose library compatible with other libraries and workflows. This iterative computation is useful if we want to track a model during iterative training or evaluation on minibatches (and optionally across on multiple GPUs). In deep learning, that's essentially *all the time*. However, when using TorchMetrics, one common question is whether we should use `.update()` or `.forward()`? (And that's also a question I certainly had when I started using it.). Here's a hands-on example and explanation.