Deep Learning with PyTorch

Loading PyTorch tensors as batches

It is a common practice in deep learning or machine learning to batch samples of images, as modern graphics processing units (GPUs) and CPUs are optimized to run operations faster on a batch of images. The batch size generally varies depending on the kind of GPU we use. Each GPU has its own memory, which can vary from 2 GB to 12 GB, and sometimes more for commercial GPUs. PyTorch provides the DataLoader class, which takes in a dataset and returns us a batch of images. It abstracts a lot of complexities in batching, such as the usage of multi-workers for applying transformation. The following code converts the previous train and valid datasets into data loaders:

train_data_gen =   torch.utils.data.DataLoader(train,batch_size=64,num_workers=3) ...