---
source: https://iinventedlight.com/library/notes/pytorch-data-loading.md
title: "Datasets and DataLoader in PyTorch"
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
source_license: "BSD-style (pytorch/pytorch LICENSE; Copyright (c) 2016-present, Facebook Inc.; https://github.com/pytorch/pytorch/blob/main/LICENSE); docs only, not the tutorials repo; PyTorch name and logo not licensed; prose restates or closely paraphrases cited source lines"
license_family: pytorch-bsd
rule_check: "needs rule check (family not in auto-publish rule; check.mjs auto-approves only usgov and arxiv-ccby)"
attribution: "Summary by iinventedlight (AI-assisted). Sources: PyTorch docs, torch.utils.data — PyTorch 2.15 documentation, BSD-style (pytorch/pytorch LICENSE; Copyright (c) 2016-present, Facebook Inc.; https://github.com/pytorch/pytorch/blob/main/LICENSE); docs only, not the tutorials repo; PyTorch name and logo not licensed; PyTorch docs, Multiprocessing best practices — PyTorch 2.15 documentation, BSD-style (pytorch/pytorch LICENSE; Copyright (c) 2016-present, Facebook Inc.; https://github.com/pytorch/pytorch/blob/main/LICENSE); docs only, not the tutorials repo; PyTorch name and logo not licensed."
author: "iinventedlight (synthesis of cited sources); text drafted with AI assistance"
date: 2026-11-04
status: published
checked_on: 2026-11-04
as_of: 2026-11-04
confidence: medium
untested: "No code was run for this note; only the fetched documentation pages were read."
category: coding-references
topic_area: "code-ai-eng: PyTorch"
tags: ['pytorch', 'dataloader', 'datasets']
sources:
  - id: S1
    title: "torch.utils.data — PyTorch 2.15 documentation"
    publisher: PyTorch docs
    url: https://docs.pytorch.org/docs/2.15/data.html
    accessed: 2026-11-04
    license: "BSD-style (pytorch/pytorch LICENSE; Copyright (c) 2016-present, Facebook Inc.; https://github.com/pytorch/pytorch/blob/main/LICENSE); docs only, not the tutorials repo; PyTorch name and logo not licensed"
  - id: S2
    title: "Multiprocessing best practices — PyTorch 2.15 documentation"
    publisher: PyTorch docs
    url: https://docs.pytorch.org/docs/2.15/notes/multiprocessing.html
    accessed: 2026-11-04
    license: "BSD-style (pytorch/pytorch LICENSE; Copyright (c) 2016-present, Facebook Inc.; https://github.com/pytorch/pytorch/blob/main/LICENSE); docs only, not the tutorials repo; PyTorch name and logo not licensed"
---
# Datasets and DataLoader in PyTorch

**Answer:** At the heart of PyTorch data loading utility is the torch.utils.data.DataLoader class. It represents a Python iterable over a dataset, with support for map-style and iterable-style datasets, customizing data loading order, automatic batching, single- and multi-process data loading, automatic memory pinning. (S1)

## Dataset styles

- The most important argument of DataLoader constructor is dataset, which indicates a dataset object to load data from. (S1)
- A map-style dataset, when accessed with dataset[idx], could read the idx-th image and its corresponding label from a folder on the disk. (S1)
- An iterable-style dataset is an instance of a subclass of IterableDataset that implements the __iter__() protocol, and represents an iterable over data samples. (S1)
- This type of datasets is particularly suitable for cases where random reads are expensive or even improbable, and where the batch size depends on the fetched data. (S1)

## Multiple workers

- The same dataset object is replicated on each worker process, and thus the replicas must be configured differently to avoid duplicated data. (S1)
- Even if you have a pool of processes sending data to a single one, make it send the buffers back - this is nearly free and will let you avoid a copy when sending next batch. (S2)

## Claims
- id: C1
  claim: DataLoader is at the heart of data loading.
  source: S1
  evidence: "At the heart of PyTorch data loading utility is the torch.utils.data.DataLoader"
- id: C2
  claim: Iterable datasets implement __iter__.
  source: S1
  evidence: "that implements the __iter__() protocol"
- id: C3
  claim: Replicas on workers must be configured differently.
  source: S1
  evidence: "replicas must be configured differently to avoid duplicated data."
- id: C4
  claim: Send buffers back to avoid a copy.
  source: S2
  evidence: "make it send the buffers back - this"

## Sources
- [S1] torch.utils.data — PyTorch 2.15 documentation. PyTorch docs. https://docs.pytorch.org/docs/2.15/data.html . License: BSD-style (pytorch/pytorch LICENSE; Copyright (c) 2016-present, Facebook Inc.; https://github.com/pytorch/pytorch/blob/main/LICENSE); docs only, not the tutorials repo; PyTorch name and logo not licensed. Accessed 2026-11-04.
- [S2] Multiprocessing best practices — PyTorch 2.15 documentation. PyTorch docs. https://docs.pytorch.org/docs/2.15/notes/multiprocessing.html . License: BSD-style (pytorch/pytorch LICENSE; Copyright (c) 2016-present, Facebook Inc.; https://github.com/pytorch/pytorch/blob/main/LICENSE); docs only, not the tutorials repo; PyTorch name and logo not licensed. Accessed 2026-11-04.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted).