DataLoader basics in PyTorch

Introduction

DataLoader helps you load data in small batches so your computer can train models faster and use less memory.

When you have a large dataset that doesn't fit into memory all at once.

When you want to train a model in steps using small groups of data (batches).

When you want to shuffle your data to make training better.

When you want to load data in parallel to speed up training.

When you want to easily loop over your dataset during training.

Syntax

PyTorch

torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0)

dataset: Your data wrapped in a Dataset object.

batch_size: Number of samples per batch.

Examples

Loads data in batches of 4 without shuffling.

PyTorch

loader = DataLoader(my_dataset, batch_size=4)

Loads data in batches of 8 and shuffles the data each time.

PyTorch

loader = DataLoader(my_dataset, batch_size=8, shuffle=True)

Loads data in batches of 2 using 2 worker threads for faster loading.

PyTorch

loader = DataLoader(my_dataset, batch_size=2, num_workers=2)

Sample Model

This code creates a small dataset of 10 samples, wraps it in a DataLoader with batch size 3 and shuffling enabled. It then prints each batch's features and labels.

PyTorch

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

# Create sample data tensors
features = torch.arange(10).float().reshape(-1, 1)  # 10 samples, 1 feature each
labels = torch.arange(10).float()  # 10 labels

# Wrap data in TensorDataset
dataset = TensorDataset(features, labels)

# Create DataLoader with batch size 3 and shuffle=True
loader = DataLoader(dataset, batch_size=3, shuffle=True)

# Loop over DataLoader and print batches
for batch_idx, (x, y) in enumerate(loader):
    print(f"Batch {batch_idx + 1}:")
    print("Features:", x.squeeze().tolist())
    print("Labels:", y.tolist())
    print()

Important Notes

Setting shuffle=True helps the model learn better by mixing data order each time.

Using num_workers > 0 loads data in parallel, speeding up training on machines with multiple CPU cores.

Batch size affects memory use and training speed; smaller batches use less memory but may train slower.

Summary

DataLoader splits your dataset into small batches for easier training.

It can shuffle data and load it in parallel to improve training quality and speed.

Use DataLoader to loop over your data smoothly during model training.

Practice

1. What is the main purpose of using DataLoader in PyTorch?

easy

A. To split the dataset into batches and optionally shuffle it for training

B. To define the neural network architecture

C. To calculate the loss during training

D. To save the trained model to disk

Solution

1. Step 1: Understand DataLoader's role

   DataLoader helps by splitting data into smaller batches and can shuffle data to improve training.

2. Step 2: Compare options

   Options B, C, and D relate to other parts of training, not data loading.

3. Final Answer:

   To split the dataset into batches and optionally shuffle it for training -> Option A

4. Quick Check:

   DataLoader = batching and shuffling [OK]

Hint: DataLoader = batch + shuffle, not model or loss [OK]

Common Mistakes:

• Confusing DataLoader with model definition
• Thinking DataLoader calculates loss
• Assuming DataLoader saves models

2. Which of the following is the correct way to create a DataLoader for a dataset named my_dataset with batch size 32 and shuffling enabled?

easy

A. loader = DataLoader(my_dataset, batch_size=32, shuffle=True)

B. loader = DataLoader(my_dataset, batch=32, shuffle=True)

C. loader = DataLoader(my_dataset, batch_size=32, shuffle=False)

D. loader = DataLoader(my_dataset, size=32, shuffle=True)

Solution

1. Step 1: Recall DataLoader parameters

   The correct parameter for batch size is batch_size, and shuffle is a boolean.

2. Step 2: Check each option

   loader = DataLoader(my_dataset, batch_size=32, shuffle=True) uses correct parameter names and values. Options B and D use wrong parameter names. loader = DataLoader(my_dataset, batch_size=32, shuffle=False) disables shuffling.

3. Final Answer:

   loader = DataLoader(my_dataset, batch_size=32, shuffle=True) -> Option A

4. Quick Check:

   batch_size and shuffle=True correct [OK]

Hint: Use batch_size= and shuffle= for DataLoader [OK]

Common Mistakes:

• Using batch instead of batch_size
• Setting shuffle to False when shuffling is needed
• Using size instead of batch_size

3. Given the following code, what will be the output printed?

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

x = torch.arange(10)
y = torch.arange(10) * 2
dataset = TensorDataset(x, y)
loader = DataLoader(dataset, batch_size=4, shuffle=False)

for batch in loader:
    print(batch)

medium

A. Only one batch with all data

B. (tensor([0, 1, 2, 3]), tensor([0, 2, 4, 6])) then (tensor([4, 5, 6, 7]), tensor([8, 10, 12, 14])) then (tensor([8, 9]), tensor([16, 18])) but shuffled

C. Error because shuffle=False is invalid

D. (tensor([0, 1, 2, 3]), tensor([0, 2, 4, 6])) then (tensor([4, 5, 6, 7]), tensor([8, 10, 12, 14])) then (tensor([8, 9]), tensor([16, 18]))

Solution

1. Step 1: Understand DataLoader batching without shuffle

   DataLoader splits data into batches of size 4, no shuffling means order is preserved.

2. Step 2: Calculate batches

   Dataset has 10 items, batches are: first 4 (0-3), second 4 (4-7), last 2 (8-9). Corresponding y values are double x.

3. Final Answer:

   (tensor([0, 1, 2, 3]), tensor([0, 2, 4, 6])) then (tensor([4, 5, 6, 7]), tensor([8, 10, 12, 14])) then (tensor([8, 9]), tensor([16, 18])) -> Option D

4. Quick Check:

   Batch size 4, no shuffle = ordered batches [OK]

Hint: No shuffle means batches keep original order [OK]

Common Mistakes:

• Assuming shuffle=True by default
• Expecting all data in one batch
• Thinking shuffle=False causes error

4. What is wrong with this DataLoader usage?

from torch.utils.data import DataLoader
loader = DataLoader(batch_size=16, shuffle=True)
for batch in loader:
    print(batch)

medium

A. batch_size should be a string, not integer

B. shuffle cannot be True

C. Missing dataset argument when creating DataLoader

D. for loop syntax is incorrect

Solution

1. Step 1: Check DataLoader parameters

   DataLoader requires a dataset as the first argument; it's missing here.

2. Step 2: Validate other parts

   batch_size as integer and shuffle=True are valid. For loop syntax is correct.

3. Final Answer:

   Missing dataset argument when creating DataLoader -> Option C

4. Quick Check:

   Dataset argument required [OK]

Hint: Always pass dataset first to DataLoader [OK]

Common Mistakes:

• Forgetting to pass dataset to DataLoader
• Thinking batch_size must be string
• Believing shuffle=True is invalid

5. You have a dataset of 103 samples and want to train a model using batches of size 20 with shuffling enabled. How many batches will the DataLoader produce per epoch, and what happens to the last batch?

hard

A. 5 batches; the last batch is dropped because it's smaller

B. 6 batches; the last batch has 3 samples

C. 6 batches; the last batch is padded to 20 samples

D. 5 batches; all batches have exactly 20 samples

Solution

1. Step 1: Calculate number of batches

   103 samples divided by batch size 20 gives 5 full batches (20*5=100) plus 1 partial batch with 3 samples.

2. Step 2: Understand default DataLoader behavior

   By default, DataLoader includes the last smaller batch unless drop_last=True is set, so last batch has 3 samples.

3. Final Answer:

   6 batches; the last batch has 3 samples -> Option B

4. Quick Check:

   103/20 = 5 full + 1 partial batch [OK]

Hint: Last batch included unless drop_last=True [OK]

Common Mistakes:

• Assuming last batch is dropped by default
• Thinking last batch is padded automatically
• Calculating wrong number of batches