PyTorch gets its name from the Torch library, which is another open-source deep learning framework written in the Lua programming language. The name PyTorch emphasizes the library’s Python-friendly nature and its roots in the Torch project.
PyTorch was originally created by the Facebook AI Research (FAIR) team in 2017. In 2022, the administration of the PyTorch project was handed over to the neutral PyTorch Foundation. As part of the Linux Foundation, the PyTorch Foundation helps coordinate the future development of the library and its ecosystem.
PyTorch benefits
PyTorch offers multiple benefits that have made it one of the favorite frameworks for AI programmers. Some of the most notable aspects of the library that make it suitable for all kinds of machine learning use cases include:
1. Python integration
PyTorch is built on Python , a widely used and beginner-friendly programming language. This makes it straightforward and simple to master, even for new AI developers with no prior experience in deep learning. PyTorch is also designed to work smoothly within the Python ecosystem and integrates well with popular and powerful Python libraries like NumPy, SciPy, and others.
2. Easy to debug
PyTorch supports dynamic computation graphs, which allows developers to build and modify them on the fly. Furthermore, it also benefits from Python’s debugging tools. These features help make debugging PyTorch models more manageable and efficient, which is particularly useful when prototyping and experimenting with different AI models.
3. GPU acceleration
PyTorch supports GPU acceleration through CUDA, which is Nvidia ’s parallel computing platform. This support helps accelerate the training of machine learning models and is particularly essential for deep learning tasks that often require substantial computational power.
4. Distributed training
PyTorch supports distributed training, which allows for efficient training and inference on different hardware, such as CPUs, and GPUs. By distributing the workload across multiple processors, or machines, PyTorch maximizes resource utilization and significantly reduces the model training time.
5. Pre-trained models
PyTorch provides a wide range of pre-trained models, such as VGG, ResNet, MobileNet, and others. Developers can easily use and integrate these pre-trained models into their existing PyTorch projects. Moreover, these models are pre-trained on large datasets, which can significantly reduce the amount of data and training time required for a model to achieve good performance. By fine-tuning a pre-trained model, developers can also achieve better performance as compared to training a model from scratch, especially when their dataset is limited.
6. Extensive ecosystem
Thanks to its being open source, PyTorch has amassed a massive global community of users and contributors. It boasts a rich ecosystem of libraries tailored for specific tasks, such as torchvision for computer vision tasks, torchaudio for audio-related tasks, torchtext for natural language processing (NLP) , and more.
Key elements of PyTorch
PyTorch has three essential components that help in the fast and efficient development of complex neural network models:
1. Tensors
Tensors are the fundamental building blocks in PyTorch, which are often thought of as being similar to multidimensional arrays. These tensors essentially help represent any type of data, including images, audio, and video in the form of numerical. Tensors are a core PyTorch data type that stores and manipulates the inputs and outputs of a model, as well as the model’s parameters. They are better at leveraging GPU capabilities for faster calculations.
2. Modules
Thanks to PyTorch’s plethora of modules, developers can quickly build deep learning models with just a few lines of code, without laboring to construct them manually. Modules such as nn and optim provide a structured way to define the architecture of neural networks , as well as various optimization algorithms that come in handy while training models. There’s also the autograd module that greatly simplifies the training of neural networks through the process of backpropagation.
3. Dynamic Computation Graphs
Deep learning models are represented in PyTorch as Dynamic Computation Graphs (DCGs). Unlike with pre-constructed static graphs, the structure of the neural network is built and modified on the fly as you run your code in DCGs. This is particularly useful for implementing complex models and for those that need to adapt their architecture based on input. DCGs also simplify debugging as they allow developers to inspect the model during execution.
PyTorch use cases
Its many benefits that help speed up model development and training, make PyTorch a popular deep learning framework with AI developers for a wide variety of tasks.
“PyTorch and TensorFlow are two of the most popular deep learning frameworks, both widely used for building and training machine learning models,” says Jad Khalife, Director of Sales Engineering, Middle East & Turkey at Dataiku. “While they share many similarities, they differ significantly in design philosophy, usability, and ecosystem.”
Khalife thinks PyTorch is preferred in the academic community due to its simplicity and flexibility. He also believes its dynamic graph makes it easier to prototype and experiment with models.
NLP is one of the fields where PyTorch is widely used. The library is often used to develop models for machine translation, sentiment analysis, and voice recognition and synthesis. In fact, many AI developers look at PyTorch as the preferred tool for many NLP tasks, especially those that require training complex neural network models on large amounts of textual data.
Then there is its GPU acceleration advantage that has helped PyTorch position itself as a leading choice for complex computer vision applications. Developers popularly use the library to process and classify images, and videos, detect objects in real time, and much more.
It is also often used for reinforcement learning tasks to help build models that can learn from interaction with the environment, and make decisions based on rewards and penalties. This makes PyTorch useful for applications like robotics, and autonomous driving where the model can learn to react to its environment.
