Create a DataLoader

1. Background Knowledge

PyTorch Data Pipeline Fundamentals PyTorch training relies on a Dataset → DataLoader → Model pipeline. The Dataset class defines how to access individual samples via getitem(idx) and len(). The DataLoader automates batching, shuffling, and parallel loading.

Key Concepts:

• Batching: Stack N samples into tensors of shape (batch_size, *sample_shape)
• Collation: default_collate() automatically stacks tensors, lists, or dicts
• Iterators: DataLoader is iterable; next(iter(loader)) yields first batch
• No shuffling: shuffle=False preserves dataset order (important for testing)

Mathematical Perspective: For dataset size $N$ and batch size $B$, DataLoader yields $\lceil N/B \rceil$ batches. Last batch may be smaller unless drop_last=True.

2. Algorithm Approach

Core Algorithm: Wrapper pattern

Input: Dataset D, batch_size B
Output: DataLoader instance
1. Initialize DataLoader(D, batch_size=B, shuffle=False, num_workers=0)
2. Return loader

DataLoader Parameters (most relevant):

Collation Flow:

[sample1, sample2,...] → default_collate() → (features_batch, labels_batch)

3. Step-by-Step Strategy

from torch.utils.data import DataLoader

def create_dataloader(dataset, batch_size):
 # Step 1: Create DataLoader with exact parameters
 loader = DataLoader(
 dataset,
 batch_size=batch_size, # Required parameter
 shuffle=False # No shuffling per problem spec
 )
 return loader