A detailed example of how to generate your data in parallel with PyTorch Star

Abstraction: Using PyTorch Dataset and DataLoader with multicore parallel data generation

Key points:

• Naive approach of loading full dataset into memory is infeasible for large datasets; single-core generators create training bottlenecks
• Solution: subclass torch.utils.data.Dataset implementing __len__ and __getitem__ to load samples on-demand per index
• DataLoader wraps the custom Dataset with key arguments: batch_size, shuffle, and num_workers for parallel batch generation
• num_workers sets the number of CPU processes generating batches in parallel; high enough value ensures GPU (not CPU) is the bottleneck
• torch.backends.cudnn.benchmark = True and transferring batches to GPU with .to(device) are recommended optimizations
• Pattern uses a partition dict (train/validation ID lists) and a labels dict for clean, modular dataset management

Connections: Pytorch · Stanford · Deep Learning · Data Pipelines · Parallel Computing

Source: https://stanford.edu/%7Eshervine/blog/pytorch-how-to-generate-data-parallel