How to use Deep Lake's new dataloader built and optimized in C++
Deep Lake offers an optimized implementation of its dataloader built in C++. The C++ dataloader is 3-5X faster in many applications, and it supports distributed training. The C++ and Python dataloaders can be used interchangeably, and their syntax varies as shown below.
train_loader = ds_train.pytorch(num_workers = 8,
transform = transform,
batch_size = 32,
shuffle = True)
train_loader = ds.dataloader()\
.pytorch(tensors=['images', 'labels'], num_workers = 8)