
CUDA graphs support in PyTorch is just one more example of a long collaboration between NVIDIA and Facebook engineers. torch.cuda.amp, for example, trains with half precision while maintaining the network accuracy achieved with single precision and automatically utilizing tensor cores wherever possible. AMP delivers up to 3X higher performance than FP32 with just a few lines of code change. Similarly, NVIDIA’s Megatron-LM was trained using PyTorch on up to 3072 GPUs. In PyTorch, one of the most performant methods to scale-out GPU training is with torch.nn.parallel.DistributedDataParallel coupled with the NVIDIA Collective Communications Library (NCCL) backend.
CUDA Graphs