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PyTorch is a popular open-source machine learning framework based on Python and optimized for GPU-accelerated computing. Originally developed by developed by Meta AI in 2016 and now part of the Linux Foundation, PyTorch has quickly become one of the most widely used frameworks for deep learning research and applications.

Unlike some other frameworks like TensorFlow, PyTorch uses dynamic computation graphs which allow for greater flexibility and debugging capabilities. The key benefits of PyTorch include:

PyTorch Lightning is a lightweight wrapper built on top of PyTorch that further simplifies the process of researcher workflow and model development. With Lightning, data scientists can focus more on designing models rather than boilerplate code. Key advantages of Lightning include:

By combining the power and flexibility of PyTorch with the high-level APIs of Lightning, developers can quickly build scalable deep learning systems and iterate faster.

To start using PyTorch and Lightning, you'll first need to install a few prerequisites:

It's recommended to use Anaconda for setting up a Python environment for data science and deep learning workloads. Follow the steps below:

Verify that PyTorch is installed correctly by running a quick test in Python:

This will print out a random 3x3 tensor, confirming PyTorch is working properly.

With PyTorch installed, we can now install Lightning using pip:

pip install lightning-ai

Let's confirm Lightning is set up correctly:

This should print out the version number, such as 0.6.0.

Now we're ready to start building deep learning models.

PyTorch uses tensors, similar to NumPy arrays, as its core data structure. Tensors can be operated on by GPUs and support automatic differentiation for building neural networks.

Let's define a simple neural network for image classification:

This defines a convolutional neural network with two convolutional layers and three fully connected layers for classifying 10 classes. The forward() method defines how data passes through the network.

We can now train this model on sample data using Lightning.

Lightning provides a LightningModule class to encapsulate PyTorch model code and the training loop boilerplate. Let's convert our model:

The training_step() defines the forward pass and loss calculation. We configure an Adam optimizer with learning rate 0.02.

Now we can train this model easily:

The Trainer handles the epoch looping, validation, logging automatically. We can evaluate the model on test data:

For comparison, here is the network and training loop code in pure PyTorch:

Lightning makes PyTorch model development incredibly fast and intuitive.

Lightning provides many built-in capabilities for hyperparameter tuning, preventing overfitting, and model management.

We can optimize hyperparameters like learning rate using Lightning's tuner module:

This performs a Bayesian search over the hyperparameter space.

Strategies like dropout layers and early stopping can reduce overfitting:

Lightning makes it simple to save and reload models:

This preserves the full model state and hyperparameters.

Both PyTorch and PyTorch Lightning are powerful libraries for deep learning, but they serve different purposes and offer unique features. While PyTorch provides the foundational blocks for designing and implementing deep learning models, PyTorch Lightning aims to simplify the repetitive parts of model training, thereby accelerating the development process.

Here is a summary of the key differences between PyTorch and PyTorch Lightning:

PyTorch is renowned for its flexibility, particularly with dynamic computation graphs, which is excellent for research and experimentation. However, this flexibility often comes at the cost of writing more boilerplate code, especially for the training loop, distributed training, and hyperparameter tuning. On the other hand, PyTorch Lightning abstracts away much of this boilerplate while still allowing full customization and access to the lower-level PyTorch APIs when needed.

If you're starting a project from scratch or conducting complex experiments, PyTorch Lightning can save you a lot of time. The LightningModule class streamlines the training process, automates logging, and even simplifies distributed training. This allows you to focus more on your model architecture and less on the repetitive aspects of model training and validation.

In summary, PyTorch offers more granular control and is excellent for researchers who need that level of detail. PyTorch Lightning, however, is designed to make the research-to-production cycle smoother and faster, without taking away the power and flexibility that PyTorch provides. Whether you choose PyTorch or PyTorch Lightning will depend on your specific needs, but the good news is that you can easily switch between the two or even use them in tandem for different parts of your project.

In this article, we covered the basics of using PyTorch and PyTorch Lightning for deep learning:

With these foundations you can start building and training advanced models like CNNs, RNNs, GANs and more. The active open source community also offers Lightning support and additions like Bolt, a component and optimization library.

Happy deep learning!

Matthew Mayo (@mattmayo13) holds a Master's degree in computer science and a graduate diploma in data mining. As Editor-in-Chief of KDnuggets, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.

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Getting Started with PyTorch in 5 Steps - KDnuggets