PyTorch training is a connected loop: represent examples as tensors, load them in batches, pass them through a model, measure prediction error, use autograd to calculate gradients, and let an optimizer update the model’s parameters. Once you understand how those parts fit together, a training script becomes much easier to read and adapt.
This guide assumes you know basic Python and have some familiarity with neural networks. PyTorch’s beginner workflow follows the same progression, from tensors and data loading through optimization and saving a model.
1. Tensors carry data through a PyTorch model
A tensor is PyTorch’s general-purpose container for numbers arranged in one or more dimensions. It can represent an input, a batch of inputs, a model’s prediction, a loss value, or a learnable parameter. Tensors resemble arrays in libraries such as NumPy, but PyTorch tensors can also participate in automatic differentiation and, when supported by the installed software and hardware, run on an accelerator.
Three properties matter constantly: shape, dtype, and device. Shape describes the dimensions; dtype describes the kind of values, such as floating-point numbers or integers; device identifies where the tensor resides, such as CPU or an accelerator. Operations generally require compatible shapes, types, and devices. PyTorch’s tensor introduction explains how tensors work and how they relate to accelerator execution and gradient tracking.
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- Shape: a feature batch might have shape
[batch_size, feature_count]; an image batch commonly includes batch, channel, height, and width dimensions. - Dtype: model inputs and weights are commonly floating point, while class labels for classification losses may be integer values. Follow the requirements of the operation or loss you use.
- Device: a model and the tensors passed to it need to be placed compatibly. Moving the model alone does not automatically move separately created input tensors.
2. Dataset and DataLoader have different jobs
A Dataset describes how to access an individual example and its corresponding label. A DataLoader iterates over that dataset and can assemble examples into batches for a training loop. In short, the dataset answers “what is one item?” and the loader answers “how do I iterate through items?”
Keeping those responsibilities separate makes it easier to change batch size or iteration behavior without rewriting the model. A dataset may also apply preprocessing or transforms as it returns examples. PyTorch’s data loading guide covers datasets, loaders, and transforms.
3. An nn.Module organizes a model
Most PyTorch models are defined by subclassing torch.nn.Module. Put layers and other learnable components in __init__, and describe how data flows through them in forward. The model can then be called like a function: PyTorch runs the forward computation and returns its output.
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Here is a compact example for a classification model whose input is a batch of feature vectors:
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import torch
from torch import nn
class Classifier(nn.Module):
def __init__(self, input_features, hidden_features, classes):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(input_features, hidden_features),
nn.ReLU(),
nn.Linear(hidden_features, classes),
)
def forward(self, x):
return self.layers(x)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = Classifier(input_features=20, hidden_features=64, classes=3).to(device)This device selection is a minimal CUDA-or-CPU example, not a complete accelerator-detection strategy. PyTorch’s current quickstart also discusses options such as MPS, MTIA, and XPU; which devices are available depends on the machine and installed PyTorch build. See the official quickstart for the broader workflow.
4. Forward computation and autograd connect predictions to gradients
Calling model(inputs) performs the forward computation: the inputs pass through the layers and produce predictions. During training, PyTorch tracks eligible operations in a computation graph when gradient tracking is enabled. If the resulting loss depends on model parameters, calling loss.backward() uses the chain rule to calculate derivatives of the loss with respect to those parameters.
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Those derivatives are stored on parameters in their .grad attributes. A crucial detail is that gradients accumulate: another backward call adds to existing gradients rather than replacing them. Unless accumulation is intentional, clear gradients before calculating those for the next update. PyTorch’s autograd guide explains graph tracking, backward computation, and gradients.
5. A training step turns gradients into a parameter update
A loss function measures how far predictions are from the target for the task. The optimizer uses the resulting gradients to update the model’s registered parameters. The learning rate is an explicit optimizer setting that controls the update scale; it is one of the choices that can materially affect training.
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A standard training step follows this order:
- Predict: pass a batch of inputs through the model.
- Measure error: compare predictions with the batch’s labels using an appropriate loss.
- Clear old gradients: call
optimizer.zero_grad(). - Calculate gradients: call
loss.backward(). - Update parameters: call
optimizer.step().
For example, with integer class labels and a model that returns one score per class, the core update can look like this:
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loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
for inputs, labels in train_loader:
inputs = inputs.to(device)
labels = labels.to(device)
predictions = model(inputs)
loss = loss_fn(predictions, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()The example uses cross-entropy loss and stochastic gradient descent (SGD); they are not universal defaults for every problem. PyTorch’s optimization tutorial demonstrates the update loop, while its quickstart also names Adam and RMSprop. Choose a loss that matches the task and compare optimizers based on the problem, training behavior, tuning needs, and computational constraints rather than assuming one is always best.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Evaluation and inference are part of the workflow
Training updates parameters; evaluation checks how the resulting model performs on data that is not being used to make those updates. Keep the roles of training and evaluation data distinct so that evaluation gives useful information about generalization.
For inference, provide inputs in the format and on the device the model expects, and avoid computing gradients when they are unnecessary. The beginner workflow treats using a trained model as part of the path from data to saved model; consult the PyTorch beginner overview for that end-to-end sequence.
7. Saving and loading complete the loop
A useful workflow preserves a trained model so it can be used again rather than retrained each time. PyTorch’s beginner path includes saving and loading as its final step. The exact serialization code and recommended handling depend on the PyTorch version and what you need to restore, so use the current documentation for your installed version rather than treating a short example as a universal persistence recipe.
For a structured introduction beyond the free official tutorials, Manning lists Deep Learning with PyTorch, Second Edition, published in February 2026. Its publisher description covers hands-on projects and topics including tensors, data loading, automatic differentiation, hardware acceleration, and neural-network systems.
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