A convenient utility function can be defined to return the requested $$i^{	extrm{th}}$$ GPU if it exists, or fall back to returning the CPU if the requested GPU is not available. This allows deep learning code to run smoothly regardless of the specific hardware configuration.

Fallback GPU Allocation Function

A utility function can be defined to return a list of all available GPUs. If no GPUs exist on the system, the function should return a list containing only the CPU device. This is useful for distributing workloads across all available hardware.

Retrieving All Available GPUs Function

The number of available GPUs on a system can be queried in PyTorch using the function torch.cuda.device_count().

Claude

CUDA helps keep track of which GPU is being used and ensures the tensors allocated will be on that device. It allows you to leverage GPU computational speed for certain tensor operations but also has functionality that allows you to move the tensor between the CPU and GPU.

CUDA in PyTorch

Dive into Deep Learning

In PyTorch, a single GPU can be indicated by torch.device('cuda'). Unlike the CPU device, a GPU device only represents one specific graphics card and its corresponding memory, not all GPUs on the system.

Learn Before

Related

Learn After