Enabling PyTorch acceleration using CUDA

One of the main benefits of PyTorch is its ability to enable acceleration through the use of a graphics processing unit (GPU). Deep learning is a computational task that is easily parallelizable, meaning that the calculations can be broken down into smaller tasks and calculated across many smaller processors. This means that instead of needing to execute the task on a single CPU, it is more efficient to perform the calculation on a GPU.

GPUs were originally created to efficiently render graphics, but since deep learning has grown in popularity, GPUs have been frequently used for their ability to perform multiple calculations simultaneously. While a traditional CPU may consist of around four or eight cores, a GPU consists of hundreds of smaller cores. Because calculations can be executed across all these cores simultaneously, GPUs can rapidly reduce the time taken to perform deep learning tasks.

Consider a single pass within a neural network...