pytorch-lightning

Simplify PyTorch model training by using a high-level API that eliminates boilerplate and abstracts training loops. This approach streamlines development, allowing you to focus on model logic rather than repetitive code.

Run distributed training across multiple GPUs using built-in support for DDP, FSDP, and DeepSpeed. This enables scalable training on single or multiple nodes without managing underlying data distribution and synchronization complexities.

Yes, you need PyTorch and Transformers installed, as the distributed training support requires PyTorch and respective libraries to function. These dependencies provide the foundational tensors and model architectures for training.

Yes, you can use fully sharded data parallelism (FSDP) for large machine learning models. The high-level API includes built-in FSDP support to efficiently shard model parameters across multiple GPUs, reducing memory constraints.

Start training a machine learning model by executing your training script, such as running `python train.py`. The high-level abstractions handle the underlying training loop, allowing quick deployment of your model.