You can parallelize data loading in PyTorch using the DataLoader by setting the num_workers parameter. This utilizes multiple CPU threads to load and preprocess data concurrently, accelerating training. Here is the code showing how:

In the above code, we are using the following:
- num_workers: Number of CPU threads for data loading. Adjust based on your CPU cores (e.g., num_workers = 4 for a 4-core CPU).
- pin_memory: Set pin_memory=True if using a GPU to speed up memory transfers.
Hence, this setup maximizes data pipeline throughput, keeping the GPU busy during training.