DataLoader
PyTorch DataLoader is an iterable utility that retrieves dataset samples, optionally shuffles and transforms them, and groups them into batches.
Deep Learning with PyTorch, Second Edition
A PyTorch abstraction that batches and loads samples from datasets.
- How it is used
- Supplies minibatches from datasets to training and validation loops.
- How deep it goes
- Explains dataset sampling, batch collation, shuffling, and worker-based loading through code; leaves worker-count and memory limits to experimentation.
- Examples
- Uses 64-image CIFAR minibatches, CT-scan batch tuples, and a worker-count timing exercise.
- What you can do after
- Apply it via PyTorch DataLoader to batch and shuffle datasets for training, then configure workers for background loading.
Read how Deep Learning with PyTorch, Second Edition explains DataLoader →
Inside Deep Learning: Math, Algorithms, Models
A PyTorch utility that prepares batches of examples from training or testing data for iteration during model execution.
- How it is used
- Prepares shuffled or ordered batches for model training and evaluation while overlapping data fetching with GPU computation.
- How deep it goes
- Explains Dataset separation, shuffling, batching, worker preloading, and memory implications without detailing DataLoader internals.
- Examples
- Uses toy regression, augmented MNIST, and a largest-digit classification dataset with explicit loader configurations.
- What you can do after
- Operate/configure PyTorch data iteration with shuffling, batching, worker preloading, augmentation, and deterministic test evaluation.
Read how Inside Deep Learning: Math, Algorithms, Models explains DataLoader →
Learn Generative AI with PyTorch
A PyTorch utility that wraps a dataset or iterable to provide batched access, optional shuffling, and iteration during training.
- How it is used
- Creates batched data iterators for classification, generative modeling, and image-based training workflows.
- How deep it goes
- Explains batch size, shuffling, iteration, and memory considerations, but defers advanced loader customization.
- Examples
- Uses Fashion MNIST classification, numerical-pair generation, labeled glasses images, and CycleGAN hair-image training.
- What you can do after
- Apply it via PyTorch to construct shuffled batch iterators for training, validation, testing, and generative-model inputs.
Read how Learn Generative AI with PyTorch explains DataLoader →
Build a Large Language Model (From Scratch)
A component that produces batches of data for model training or experimentation, typically by sampling and collating examples.
- How it is used
- Organizes prepared examples into batches consumed by GPT fine-tuning and classification training, validation, and testing.
- How deep it goes
- Demonstrates configuration, iteration, device-aware collation, and output shapes without implementing Dataset internals.
- Examples
- Uses spam-message loaders with padded token IDs and instruction loaders with custom-collated input-target tensors.
- What you can do after
- Operate PyTorch DataLoader instances for shuffled training and deterministic validation or testing batches.
Read how Build a Large Language Model (From Scratch) explains DataLoader →
Related concepts
- batch sizeconfigured by · Batch size is the number of items grouped together for processing or computation as a single batch.
- training loopused by · A training loop is an iterative procedure that processes training data, evaluates a model objective, and updates model parameters to improve performance.
- PyTorchuses · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
- training datasetuses · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
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