DataLoader (PyTorch)

A DataLoader is a component (commonly in PyTorch) that efficiently loads and processes data for training machine learning models. It abstracts the complexity of batching, shuffling, and parallel data loading.

Key Features

1. Batching: Groups multiple data samples into a single batch (tensor) for efficient parallel processing by the GPU.
2. Shuffling: Randomizes the order of data to prevent the model from learning order-dependent patterns.
3. Parallel Loading: Uses multiple worker threads (num_workers) to prepare data in the background, speeding up the training pipeline.

Sample Code

In Python (PyTorch), a DataLoader is created from a Dataset Class instance.

dataloader = DataLoader(
    dataset,
    batch_size=4,
    shuffle=True,
    drop_last=True,
    num_workers=0
)

The DataLoader relies on the dataset’s __getitem__ method to fetch individual samples.