How can I parallelize data loading using PyTorch s DataLoader to accelerate the training of generative models

Question

With the code, can you explain how I can parallelize data loading using PyTorch's DataLoader to accelerate the training of generative models?

Answer 1

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.