PyTorch vs PyTorch Lightning: A Practical Exploration

Author(s): Talha Nazar

Originally published on Towards AI.

PyTorch has become a household name among developers and researchers in the ever-evolving world of deep learning. Its dynamic computational graph, flexibility, and extensive community support have made it a go-to framework for building everything from simple neural networks to complex state-of-the-art models. However, with flexibility comes the responsibility of writing a fair amount of boilerplate code — especially regarding training loops, logging, and distributed learning. That’s where PyTorch Lightning steps in, offering a structured, high-level interface that automates many of the lower-level details.

In this story, we’ll deeply dive into what differentiates plain PyTorch from PyTorch Lightning, highlight their key distinctions with hands-on examples, and examine how each approach might fit into your workflow. We’ll also include a flowchart comparing training pipelines, relevant citations for deeper study, and links to helpful videos, so you can embark on a guided exploration of these two frameworks.

Table of Contents

1. Background: PyTorch Essentials
2. Introducing PyTorch Lightning
3. One-to-One Differences
4. Hands-On Examples
5. Flowchart Comparison
6. Best Practices & Use Cases
7. Helpful Resources & Citations
8. Conclusion

1. Background: PyTorch Essentials

Before we compare PyTorch to PyTorch Lightning, it’s important to recap what makes PyTorch so appealing in the first place.

1.1 Dynamic Computation Graph

PyTorch uses a dynamic computational graph, which means the graph is generated on the fly, allowing developers to write Python code that feels more natural and more intuitive for debugging. In older frameworks (like the early days of TensorFlow), you had to define a static graph before running it, which introduced complexity when working with dynamic inputs or specialized architectures.

1.2 Pythonic API

PyTorch is deeply integrated with Python. This synergy makes it particularly developer-friendly, as you can leverage native Python features and debugging tools. The code flows seamlessly, making experimentation straightforward.

1.3 Granular Control

With great power comes great responsibility. In vanilla PyTorch, you’re in charge of writing the training loop, updating weights (optimizers, schedulers), moving data to/from devices, and handling any special logging or callbacks yourself. This is ideal if you want fine-grained control or are building highly specialized research models.

2. Introducing PyTorch Lightning

Developed to reduce boilerplate and foster best practices, PyTorch Lightning is often described as a lightweight wrapper on top of PyTorch. Instead of reinventing the wheel, it focuses on streamlining the training process:

1. Removes Boilerplate: You no longer have to write your training loop from scratch; PyTorch Lightning Trainer handles it.
2. Enforces Structure: Encourages a modular approach to building neural networks. You define a LightningModule that contains your model architecture, your training_step, validation_step, and other steps if needed.
3. Built-in Features: Built-in logging (via Lightning’s loggers), distributed training support, checkpointing, early stopping, and more.

Rather than limiting you, PyTorch Lightning preserves PyTorch's underlying flexibility. If you need to dive deeper, you can override methods or incorporate custom logic without losing the benefits of the framework’s structure.

3. One-to-One Differences

3.1 Training Loops & Boilerplate

PyTorch:

• You manually write your training, validation, and testing loops.
• You must keep track of batch iterations, forward passes, backpropagation, optimizers, and logging if needed.

PyTorch Lightning:

• You implement methods like training_step(), validation_step(), and configure_optimizers() inside a LightningModule.
• The Trainer orchestrates the loop, calls these methods under the hood, and abstracts the repetitive aspects (e.g., for batch in train_loader: ...).

Benefit: In Lightning, you can focus on the logic (how to train) rather than the scaffolding (where to place your loops, how to log, etc.).

3.2 Logging & Experiment Tracking

PyTorch:

• Typically done via custom solutions: tensorboardX, logging libraries, or manual print statements.
• You handle code for saving metrics, writing to logs, or generating TensorBoard visualizations.

PyTorch Lightning:

• Integrated loggers: TensorBoard, Comet, MLflow, Neptune, etc.
• Simple calls like self.log('train_loss', loss, on_step=True) handle metric logging behind the scenes.
• Built-in checkpointing that automatically saves your best or latest model based on validation metrics.

Benefit: Logging and checkpointing become near-automatic, encouraging better reproducibility.

3.3 Distributed & Multi-GPU Support

PyTorch:

• Requires nn.DataParallel or more advanced approaches like DistributedDataParallel.
• You must carefully handle device allocation, batch splitting, and synchronization in your code.

PyTorch Lightning:

• Launch multiple processes or multi-GPU training via a single argument (e.g., Trainer(gpus=2, accelerator='gpu')).
• Lightning manages distributed sampling, gradient synchronization, etc.

Benefit: It simplifies HPC (high-performance computing) or multi-GPU usage, letting you focus on the model rather than the details of parallelization.

3.4 Code Organization

PyTorch:

• Flexible, but can become messy if you don’t enforce consistent code structures.
• A typical pattern is to keep model definitions in one file, and training logic in another, but you’re free to do as you please.

PyTorch Lightning:

• Enforces a best-practice structure: one class for your LightningModule, your data module or data loaders, and a Trainer for orchestrating runs.
• This can create more maintainable code in production scenarios.

4. Hands-On Examples

To better illustrate, let’s consider a simple feedforward network on a dummy dataset. We’ll look at a minimal PyTorch approach and then the equivalent in PyTorch Lightning. While the following snippets are simplified, they showcase the typical differences in code structure.

4.1 Minimal Training Loop in PyTorch

import torch
import torch.nn as nn
import torch.optim as optim

# dataset (features, labels)
X = torch.randn(100, 10) 
y = torch.randint(0, 2, (100,)) 

# Simple feedforward model
model = nn.Sequential(
 nn.Linear(10, 16),
 nn.ReLU(),
 nn.Linear(16, 2)
)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-3)

# Training loop
epochs = 5
for epoch in range(epochs):
 optimizer.zero_grad()
 outputs = model(X)
 loss = criterion(outputs, y)
 loss.backward()
 optimizer.step()

 # Validation step (just a demonstration - not a separate set)
 with torch.no_grad():
 val_outputs = model(X)
 val_loss = criterion(val_outputs, y)

 # Logging
 print(f"Epoch: {epoch+1}, Train Loss: {loss.item():.4f}, Val Loss: {val_loss.item():.4f}")

Key Observations:

• Manually zeroing gradients, computing forward pass, backpropagating, and logging.
• If you want to separate training vs. validation sets, you must add additional code.
• No built-in checkpointing or advanced features unless you code them yourself.

4.2 Equivalent Training in PyTorch Lightning

import torch
import torch.nn as nn
import torch.optim as optim
import pytorch_lightning as pl
from torch.utils.data import TensorDataset, DataLoader

class SimpleModel(pl.LightningModule):
 def __init__(self):
 super(SimpleModel, self).__init__()
 self.model = nn.Sequential(
 nn.Linear(10, 16),
 nn.ReLU(),
 nn.Linear(16, 2)
 )
 self.criterion = nn.CrossEntropyLoss()

 def forward(self, x):
 return self.model(x)

 def training_step(self, batch, batch_idx):
 X, y = batch
 outputs = self.forward(X)
 loss = self.criterion(outputs, y)
 self.log("train_loss", loss)
 return loss

 def validation_step(self, batch, batch_idx):
 X, y = batch
 outputs = self.forward(X)
 loss = self.criterion(outputs, y)
 self.log("val_loss", loss)

 def configure_optimizers(self):
 return optim.Adam(self.parameters(), lr=1e-3)

Key Observations:

• No manual loop for epochs, and no manual zeroing of gradients.
• Separate training_step and validation_step.
• Logging is done self.log("train_loss", loss) automatically and integrated with Lightning’s system.

5. Flowchart Comparison

Below is a simplified illustration of how training in each framework typically flows:

VS

6. Best Practices & Use Cases

6.1 When to Stick With Plain PyTorch

1. Research Prototypes: If you’re experimenting with brand-new architectures, where you might alter the training loop frequently.
2. Full Control: You need to do something highly custom, like modifying gradient updates each iteration or implementing exotic optimization procedures that might not fit neatly into Lightning’s callback structure.

6.2 When to Use PyTorch Lightning

1. Production & Team Projects: If you need consistent, readable code to onboard multiple developers.
2. Distributed Training or Multi-GPU: Lightning drastically reduces the overhead for multi-GPU or multi-node training.
3. Rapid Experimentation: If you value the speed of building experiments with minimal boilerplate, integrated logging, and easy debugging.

6.3 Hybrid Approach

It’s not always a binary decision. Some teams prototype in plain PyTorch, then migrate stable code to Lightning for production. You might also write custom loops in Lightning by overriding certain hooks if you need partial automation and partial custom logic.

7. Helpful Resources & Citations

1. Official PyTorch Documentation
2. PyTorch Lightning Official Docs
3. PyTorch Lightning YouTube Tutorial
4. GitHub Repos
5. Research Paper

8. Conclusion

Choosing between PyTorch and PyTorch Lightning ultimately comes down to how much you value flexibility versus automation. PyTorch offers an unparalleled level of control, which is ideal for cutting-edge research or scenarios where you need to heavily customize training loops. PyTorch Lightning, on the other hand, wraps this power in a structured, consistent interface that reduces boilerplate code, simplifies multi-GPU training, and encourages best practices like built-in logging and modular design.

For many data scientists and machine learning engineers working on production-level code, Lightning can help maintain readability, reproducibility, and efficiency. If you’re a researcher or enjoy micromanaging every aspect of the training process, you may continue to prefer vanilla PyTorch. Indeed, the real beauty here is that PyTorch Lightning is still powered by PyTorch: if you ever need to poke under the hood, the freedom is still there.

Thank you for reading! If you enjoyed this story, please consider giving it a clap, leaving a comment to share your thoughts, and passing it along to friends or colleagues who might benefit. Your support and feedback help me create more valuable content for everyone.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI