Training Compiled PyTorch 2.0 with PyTorch Lightning

PyTorch 2.0 introduces `torch.compile`, a compiler-based optimization feature intended to improve model training and inference performance while preserving PyTorch’s Pythonic workflow and backward compatibility. Reported tests across 163 open-source models on NVIDIA A100 GPUs found average AMP training speedups of 51%, while benchmark examples using TorchVision ResNet models showed gains of roughly 69–74% over eager execution. PyTorch Lightning 2.0 supports compilation directly by allowing developers to compile a LightningModule, including its training, validation, and prediction steps, with a single call to `torch.compile`. The tutorial demonstrates this process by loading CIFAR-10, defining a ResNet-18 LightningModule with training and validation logic, compiling it in `reduce-overhead` mode, and training it through Lightning’s Trainer, which can automatically choose available accelerators and devices.

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