What Is PyTorch?

Key takeaways

PyTorch, based on the Python programming language, is an open-source framework that allows you to develop deep learning models.

• In addition to Python, PyTorch also supports a C++ language interface for creating applications.

• A few tools the PyTorch ecosystem offers are flair, PyTorchVideo, and TorchText.

• You can use PyTorch to increase the efficiency of your prototypes while working as a data scientist.

Explore the Python framework PyTorch for deep learning, including how it operates, who uses PyTorch, and how you can install it. If you’re ready to begin a career in AI, enroll in the IBM AI Engineering Professional Certificate, where in as little as four months, you can learn about Python programming, PyTorch, data science, and more.

What is PyTorch?

PyTorch, developed by Facebook's AI research lab and released in 2016 [1], is a Python library for developing artificial intelligence. Providing the benefits of Torch's efficient back-end libraries and Python's intuitive front-end, PyTorch offers user-friendliness, fast prototyping, and a vast community for support, making it popular for use in artificial intelligence applications, including natural language processing and deep learning.

The use of artificial intelligence is growing in today's market, and this trend only looks to continue. Industries such as health care, education, retail, manufacturing, finance, customer service, and many more actively utilize AI technology to improve their offerings. Even many of the products and devices you likely use regularly feature artificial intelligence. For example, streaming services use AI to make recommendations for shows and movies based on your preferences, and navigation applications like Google Maps use AI to find the optimal route to your destination. Building applications such as these and many other AI tasks becomes much more manageable with the help of libraries and frameworks such as PyTorch.

What is PyTorch used for?

This open-source, Python-based framework enables you to build deep learning models. Python is a beginner-friendly language, adding to the convenience of PyTorch. Although Python is relatively simple to use compared to other programming languages, it’s a powerful language and widely used for developing artificial intelligence. 

One particular benefit of PyTorch is that it enables you to prototype and experiment with deep learning models quickly. Because of this, it’s especially useful when working on smaller projects. The deep learning models you develop with PyTorch can be used in applications in several areas, including natural language processing, computer vision, and reinforcement learning. 

To start working with PyTorch, you first need to have Python installed and one of the supported package managers, pip, and Anaconda. 

Read more: Easiest Programming Languages to Learn for Front-End Development

What is a deep learning model?

Inspired by the human brain, deep learning models go through training and learn similarly to human beings. Deep learning models can identify key insights and develop predictions based on data patterns found in sources such as images, text, and audio. 

What separates deep learning from other machine learning models is the number of neural network layers within the model. In the case of deep learning models, they contain at least three layers. Another differentiator of deep learning models from some standard machine learning models is the ability to process unstructured data, allowing the models to learn without needing as much human assistance.

PyTorch features

PyTorch has numerous features that make it effective in supporting efficient prototyping and experimentation for machine learning models:

• You can perform modifications to create dynamic neural networks efficiently through reverse-mode auto-differentiation.

• PyTorch supports a C++ interface in addition to Python for creating applications.

• The TorchServe tool in PyTorch allows you to build scalable models through the cloud. 

• PyTorch can extend its API to use machine learning in both iOS and Android applications.

• PyTorch has an entire community of researchers and developers who create libraries to further the reach of PyTorch into more facets of artificial intelligence. 

• The computational work performed in PyTorch can extend across multiple CPUs and GPUs for distributed training and parallel processing.

• Data loading and processing from several sources and data types is possible in PyTorch with the help of tools such as the torchvision package.

PyTorch vs. TensorFlow: How do they differ? 

TensorFlow is Google’s open-source platform for machine learning, featuring libraries and tools built on Python and Java. Both PyTorch and TensorFlow are popular choices for deep learning applications.

TensorFlow requires explicit programming, making it more challenging to learn. In contrast, PyTorch offers a smoother learning curve and better debugging support. TensorFlow is ideal for large-scale projects, while PyTorch is more suitable for smaller, research-oriented tasks or prototyping.

How PyTorch works

PyTorch operates through tensors and graphs. Tensors work by numerically representing non-numerical data, such as the data provided through images and text. This allows the model to manipulate and store various inputs and outputs. Graphs direct how data moves through the neural networks within deep learning models, with directions on what operations to perform at the different nodes within the neural networks. 

The PyTorch ecosystem

Another valuable feature of PyTorch is the PyTorch ecosystem. The PyTorch ecosystem is a collection of libraries and tools that further support the development of artificial intelligence. Within the PyTorch ecosystem, you can find open-source libraries that members of the PyTorch community contribute to, leading to accelerated growth and support for AI. Here are some examples of tools and libraries the PyTorch ecosystem offers:

• Flair is used for natural language processing in a straightforward framework.

• PyTorchVideo is for video-centric deep learning and a better understanding of research. 

• Determined is for more efficient model training and sharing GPU resources.

• Glow improves deep learning performance on different platforms.

• Horovod is a deep learning framework library for distributed deep learning.

• TorchText processes natural language using pre-trained models.

• Torchvision provides data sets and the necessary architecture for object detection and image classification.

• MONAI is a health care-specific tool for creating image-based training workflows.

Who uses PyTorch?

PyTorch is a relevant and valued skill for professionals across a number of positions and in several fields. As a deep learning engineer, much of the modeling you will be doing will likely occur in Python, where PyTorch is a valuable tool. PyTorch is important for data science as well. Data scientists benefit from the prototyping efficiency of PyTorch, in addition to its compatibility with other tools and simple debugging process. For software developers, PyTorch has several tools in its ecosystem that you can utilize to develop artificial intelligence models.

Some examples of businesses implementing PyTorch include Salesforce for natural language processing and Amazon Advertising. Additionally, Stanford University uses PyTorch for research purposes to establish algorithmic approaches.

Stay current on programming-related trends and job opportunities 

Keep up with job opportunities and trends related to Python by joining Career Chat on LinkedIn. Explore these other free resources:

• Watch on YouTube: How Long Does It Really Take to Learn Python?

• Discover Python keywords: Python Syntax Cheat Sheet

• Learn the terminology: Python Glossary: Essential Terms and Definitions

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