The key points of the text cover how to write and launch multi-node distributed PyTorch applications, with a focus on using `torch.distributed.launch`, `torchrun`, and `mpirun` methods. The tutorial assumes readers have some experience with PyTorch and data parallelization. It explains how to assign GPUs to each process, facilitate communication between processes, and wrap up the model and dataset in the context of PyTorch DDP. The tutorial also covers how to set environment variables such as `WORLD_SIZE`, `WORLD_RANK`, and `LOCAL_RANK` using different methods including `torch.distributed.launch`, `torchrun`, and `mpirun`. Additionally, it discusses the importance of scaling efficiency when running a distributed training job across multiple nodes and provides reference performance on Lambda Cloud.