Efficient Metric Collection in PyTorch: Avoiding the Performance Pitfalls of TorchMetrics | Towards Data Science

Metric collection is an essential part of every machine learning project, enabling us to track model performance and monitor training progress. Ideally, metrics should be collected and computed without introducing any additional overhead to the training process. However, just like other components of the training loop, inefficient metric computation can introduce unnecessary overhead, increase training-step […]