TensorFlow or PyTorch: What’s the Difference?

The global artificial intelligence (AI) market is projected to reach a $1.01 trillion valuation by 2031 [1]. With this comes the growing demand for professionals with AI skills as businesses look to capitalize on this transformative technology. One of the fundamental areas of AI for professionals to acquire skills and knowledge is deep learning, which helps power various forms of AI, like natural language processing and computer vision. TensorFlow and PyTorch are popular frameworks for performing deep learning tasks. Although some similarities exist, each possesses unique traits that make it ideal for certain deep learning applications. 

Keep reading to learn about the differences between TensorFlow and PyTorch to help you decide which is better suited for your deep learning project.

What is TensorFlow, and what is it used for?

Google developed this machine learning framework in 2015. Because of its significant computational power and scalability, TensorFlow sets itself apart from other machine learning frameworks and is well-suited for deep learning. Its flexibility can help you develop machine learning models that run on various environments, including the web, desktops, mobile devices, and even the cloud. Additionally, TensorFlow's relative ease of use makes it accessible for beginners while providing value for experts developing complex models.

You can use TensorFlow’s various tools to prepare data for processing, including eliminating bias from your data, training and building models, and monitoring their effectiveness. Simply put, you can use TensorFlow to support your efforts in building machine learning and deep learning models, from the beginning all the way through to the end. Throughout several industries, businesses and organizations rely on TensorFlow to help build their AI systems, for example:

• Health care organizations: TensorFlow can help build AI models to identify diseases in medical images, aiding in patient diagnosis.

• Financial institutions: TensorFlow can help identify fraud and assess risk, whether analyzing the stock market or evaluating investment opportunities. 

• Customer service: TensorFlow can help train chatbots, which rely on natural language processing, to understand human language and respond intelligently.

How does TensorFlow work?

TensorFlow provides features supporting data preprocessing, training, and building machine learning models. The high-level Keras API that runs on TensorFlow adds to its beginner-friendly nature and powerful performance. Keras also enables efficient debugging, concise code, and simplified maintenance and deployment of your models. Other valuable resources TensorFlow offers include TensorBoard, which enables you to visualize essential metrics when building your models, and TensorFlow Hub, where you can access pre-trained machine learning models.

Pros and cons of TensorFlow

A main benefit of developing machine learning and deep learning models with TensorFlow is that it simplifies these processes and the various steps that go into them. Whether training, building, debugging, or deploying, TensorFlow can help. Additionally, TensorFlow’s scalability makes it an excellent option for building larger, more complex models that require massive amounts of data and computational power.

Although TensorFlow performs well in these larger projects, it may not be as suitable in other smaller-scale environments. For example, other machine learning frameworks can outperform TensorFlow if the scope of your project entails the need for fast prototyping and research.

What is PyTorch, and what is it used for?

PyTorch is a deep learning framework introduced by Facebook in 2016. It integrates Python tools with Python's user-friendly front-end, providing an accessible platform for developing deep learning models. PyTorch is particularly popular in research-based environments such as academia, where it can help you quickly build prototypes and transform your research into functional AI models at scale. 

You can use PyTorch to develop models that work across different types of CPUs and platforms, like mobile devices and the cloud. Some of the different forms of AI models PyTorch can help build include generative AI, natural language processing, and computer vision. Take a look at some examples of how PyTorch has helped businesses solve real-world problems:

• IBM Research: IBM’s research and development branch utilizes PyTorch for computer vision when developing geospatial computer vision models, ultimately helping to perform tasks like monitoring deforestation and observing the effects of climate change.

• Intel: This technology enterprise uses PyTorch as a staple in its effort to develop AI Playground and implement generative AI into gaming experiences. It enabled the organization to bring the product to market quickly.

• Duolingo: This popular language learning platform uses PyTorch to create custom deep learning algorithms for natural language processing. These models can handle up to 30 million data points at one time.

How does PyTorch work?

PyTorch provides various tools and libraries enabling you to perform various tasks. For example, the TorchVision library contains model architecture and data sets for computer vision projects, while TorchText includes data sets for natural language processing. If you’re building models at scale, TorchServe helps you deploy models in any environment. The PyTorch ecosystem features these tools, and many more, providing the support you need for the different tasks you will encounter while developing deep learning models. 

Pros and cons of PyTorch

PyTorch’s relationship with Python is beneficial, especially if you have previous experience with Python, as it will make learning PyTorch much simpler. Additionally, PyTorch supports a C++ interface that you can use to build C++ applications. These traits, alongside the widespread adoption of PyTorch and an extensive collection of tools and libraries, help make it one of the premier deep learning frameworks.

However, PyTorch lacks a visual interface, allowing you to implement a third-party library or create custom scripts. Visual interfaces help develop insights during the training process and for efficiently debugging and monitoring neural networks. 

TensorFlow or PyTorch: which should you choose?

When it comes to building deep learning models, you can’t go wrong with either TensorFlow or PyTorch. However, one is potentially more optimal than the other depending on the focus of your project. If you are building a large-scale product and looking for support throughout the development process, TensorFlow can help you reach your goals. If you will be researching and prototyping while looking to transform your ideas into workable deep learning models as quickly as possible, PyTorch is likely the best option for you.

It’s even possible to get the best of both TensorFlow and PyTorch. You can convert models between both frameworks using Open Neural Network Exchange (ONNX). This allows you to access and capitalize on the strengths of TensorFlow and PyTorch to reap the benefits of each within your deep learning model.

Explore deep learning libraries on Coursera

TensorFlow and PyTorch are two widely utilized deep learning libraries that you can use to generate models for tasks like natural language processing, computer vision, and more. On Coursera, you can access expert-led courses to help you advance your abilities in using deep learning libraries. For example, DeepLearning.AI’s Deep Learning Specialization allows you to practice building and training neural networks with TensorFlow. 

You can also earn an IBM AI Engineering Professional Certificate, where you can learn to build deep learning models and neural networks with TensorFlow and PyTorch, implement supervised and unsupervised training, and more.