Amazon says that a single configuration change is sufficient to switch hardware, eliminating the need to rewrite and revalidate models when moving to Trainium. Today, 85% of AI researchers and developers write their code in PyTorch. Until now, the hardware that code could run on was far more limited than the code itself, and expanding to new chips meant rewriting and revalidating work that took months to perfect.
According to the TorchNeuron documentations, when leveraging PyTorch's automatic device detection through torch.accelerator, the only required change is replacing .to('cuda') to .to('neuron'). The backend also natively supports distributed training and PyTorch parallelism APIs, including DDP, FSDP2, DTensor, and others. In addition, TorchNeuron provides native support for Torch streams and events through torch_neuronx.Stream and torch_neuronx.Event.
