pytorch-lightning

Simplify PyTorch model training by using high-level APIs to define models and training loops with minimal boilerplate. This approach abstracts away complex engineering code, allowing you to focus directly on your research and model logic.

Distributed PyTorch training across multiple GPUs is best handled using built-in strategies like DDP, FSDP, and DeepSpeed. These distributed strategies manage hardware communication automatically to ensure efficient training on multiple machines.

Add early stopping and checkpointing to PyTorch training by using built-in callbacks. These callbacks hook into the training loop to monitor metrics, automatically save model states, and halt training when performance stops improving.

You need PyTorch and Lightning installed to use high-level training APIs. These dependencies provide the foundational tensor operations and the structural framework required to support the simplified training loop.

You can use Hugging Face Transformers with a simplified PyTorch training API. The framework supports transformer models, allowing you to integrate them into the LightningModule for efficient training and distributed strategies.