Table of Contents
What Data Handling Means in PyTorch
Training a neural network is not only about defining a model and an optimizer. Most practical problems are limited by how well you can feed clean, correctly shaped, correctly typed batches into the training loop. In PyTorch, data handling is the part of your code that turns raw files or in memory arrays into mini-batches of tensors that your model can consume reliably and efficiently. This chapter gives a map of the data pipeline pieces you will use, and how they fit together, without yet diving into the details of each component.
The Core Data Pipeline: From Raw Data to Batches
A typical PyTorch data pipeline has three layers. The first layer is your raw data source, such as CSV files, image folders, audio clips, or pre-tokenized text stored on disk. The second layer is a dataset object that knows how to locate one example by index and return it in a consistent format. The third layer is a data loader that repeatedly asks the dataset for examples, groups them into batches, and optionally shuffles and parallelizes the work.
Conceptually, you can think of it as a function that maps an integer index to one training example, then a batching step that maps a list of examples to a single batch. The dataset is responsible for example level logic, and the data loader is responsible for iteration and batching logic.
What Your Model Expects From the Pipeline
The training loop typically expects each batch to contain input tensors and target tensors, already on the CPU and with consistent shapes inside the batch. Later, the training loop moves the batch to the chosen device. The pipeline should therefore enforce a few invariants.
Within a batch, every tensor that will be stacked must have the same shape. If examples have variable lengths or sizes, you must pad, crop, or otherwise convert them to a uniform shape during batching.
Inputs and targets must have appropriate dtypes. Many losses require specific types, for example classification targets are usually integer class indices with dtype torch.long, while regression targets are usually floating point.
Where Preprocessing and Transforms Belong
Preprocessing includes operations such as normalization, resizing, tokenization, converting categories to indices, and turning arrays into tensors. In PyTorch, these operations often live close to the dataset, because they are naturally defined per example. Some operations belong at batching time instead, especially when they depend on the maximum length within the current batch, as with sequence padding.
A useful mental rule is that anything you can do independently for each example can be done when retrieving that example. Anything that requires looking at multiple examples at once, such as dynamic padding or mixing examples, belongs in the batch collation step.
The Role of Collation and Why It Matters
Batching is more than stacking tensors. When examples are dictionaries, tuples, or variable-length sequences, you need a collation rule that defines how to merge a list of examples into one batch. PyTorch has a default collation behavior that works for many simple cases, but you will often customize it for text and some structured data.
You should treat the collation step as part of your model contract. If your model expects inputs shaped like (batch_size, channels, height, width) or (batch_size, sequence_length), it is the collation logic that guarantees those shapes.
Shuffling, Sampling, and Class Imbalance at the Data Level
The order and frequency with which examples appear can change training outcomes significantly. Shuffling breaks unwanted correlations that may exist in the dataset ordering. Sampling strategies can address class imbalance by showing rare classes more often. These choices belong in the data loading configuration rather than inside the model.
A common pattern is to separate the dataset content from the sampling policy. The dataset defines what examples exist and how to load them. The data loader and its sampling configuration define how you iterate over them during training.
Handling Different Data Modalities
Although PyTorch uses tensors for everything, the steps to get to tensors differ by modality. Tabular data often requires type conversion, missing value handling, and encoding categorical variables. Text requires tokenization and padding. Images require decoding, resizing, and normalization. The pipeline should be designed so that by the time a batch reaches the model, all modality specific complexity has already been resolved into tensors with predictable shapes and dtypes.
One practical implication is that your dataset item should return a consistent structure, such as (x, y) or {"inputs": x, "targets": y}, regardless of modality. This makes the training loop stable and reusable.
Performance as a First-Class Concern
Data pipelines can silently become the bottleneck. If the GPU is waiting for batches, training slows down even if the model is small. Many performance issues come from doing expensive Python work per example, repeatedly decoding or transforming data without caching, or using single-process loading for heavy preprocessing.
You will later learn specific tools such as multi-worker loading and pinned memory. For now, the key idea is that data handling is part of training performance, not an afterthought.
If your training loop is slow, do not assume the model is the cause. A data pipeline that cannot keep up will waste compute and can make experiments misleading.
How This Section Fits With the Rest of the Course
This chapter is the entry point for the full data handling section. The next chapters will cover the concrete building blocks, including datasets and data loaders, preprocessing and transforms, custom collation, shuffling and sampling strategies, modality specific workflows, and basic performance tuning. The goal is that by the end of this section, you can build a reliable input pipeline that produces correct batches, supports validation and testing splits, and scales to larger datasets without rewriting your training loop.