How to Set Batch Size in PyTorch DataLoader

In PyTorch, you set the batch size by passing the batch_size parameter when creating a DataLoader. For example, DataLoader(dataset, batch_size=32) loads data in batches of 32 samples.

Syntax

The DataLoader constructor accepts a batch_size argument that defines how many samples are grouped together in one batch. This controls how many data points the model processes before updating weights.

• dataset: Your dataset object.
• batch_size: Number of samples per batch (integer).
• shuffle: Whether to shuffle data each epoch (optional).

python

from torch.utils.data import DataLoader

dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

Example

This example shows how to create a simple dataset and use DataLoader with a batch size of 4. It prints the shape of each batch to demonstrate batching.

python

import torch
from torch.utils.data import DataLoader, TensorDataset

# Create dummy data: 10 samples, each with 3 features
features = torch.arange(30).reshape(10, 3).float()
labels = torch.arange(10)

dataset = TensorDataset(features, labels)

# Create DataLoader with batch_size=4
loader = DataLoader(dataset, batch_size=4, shuffle=False)

for batch_idx, (x, y) in enumerate(loader):
    print(f"Batch {batch_idx + 1}")
    print("Features shape:", x.shape)
    print("Labels:", y)
    print()

Output

Batch 1
Features shape: torch.Size([4, 3])
Labels: tensor([0, 1, 2, 3])

Batch 2
Features shape: torch.Size([4, 3])
Labels: tensor([4, 5, 6, 7])

Batch 3
Features shape: torch.Size([2, 3])
Labels: tensor([8, 9])

Common Pitfalls

Common mistakes when setting batch size include:

• Setting batch_size larger than the dataset size, which results in fewer batches.
• Forgetting to set shuffle=True during training, which can reduce model generalization.
• Using batch size 1 unintentionally, which can slow down training.

Always choose a batch size that fits your memory and training speed needs.

python

from torch.utils.data import DataLoader

# Wrong: batch_size larger than dataset size
loader_wrong = DataLoader(dataset, batch_size=20)
print(f"Number of batches with batch_size=20: {len(loader_wrong)}")

# Right: batch_size smaller or equal to dataset size
loader_right = DataLoader(dataset, batch_size=5)
print(f"Number of batches with batch_size=5: {len(loader_right)}")

Output

Number of batches with batch_size=20: 1
Number of batches with batch_size=5: 2

Quick Reference

Summary tips for setting batch size in DataLoader:

• Use batch_size to control samples per batch.
• Typical batch sizes are powers of 2 (e.g., 16, 32, 64).
• Smaller batch sizes use less memory but may train slower.
• Set shuffle=True for training data to improve learning.

Key Takeaways

Set batch size by passing the batch_size parameter to DataLoader.

Choose batch size based on memory limits and training speed.

Use shuffle=True during training to improve model performance.

Batch size larger than dataset size results in fewer batches.

Typical batch sizes are 16, 32, or 64 for balanced training.