Talal Zaman Abbasi - Software Engineer
CEO Bit Technologies - Best Digital Marketing Specialist in Pakistan
Talal Zaman Abbasi - Software Engineer
CEO Bit Technologies - Best Digital Marketing Specialist in Pakistan

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PyTorch vs TensorFlow – Which is Better for Deep Learning Projects?

January 10, 2024 Design
PyTorch vs TensorFlow – Which is Better for Deep Learning Projects?


In this article, we’ll look at two popular deep learning libraries — PyTorch and TensorFlow – and see how they compare.

If you are getting started with deep learning, the available tools and frameworks will be overwhelming. Industry experts may recommend TensorFlow while hardcore ML engineers may prefer PyTorch.

Both these frameworks are powerful deep-learning tools. While TensorFlow is used in Google search and by Uber, Pytorch powers OpenAI’s ChatGPT and Tesla’s autopilot.

Choosing between these two frameworks is a common challenge for developers. If you’re in this position, in this article we’ll compare TensorFlow and PyTorch to help you make an informed choice.

Understanding PyTorch and TensorFlow

Let’s start by getting to know our contenders better.

PyTorch, created by Facebook’s AI Research lab, has gained recognition for its simplicity and user-friendliness. Pytorch can efficiently handle dynamic computational graphs.

A computation graph is a visual representation of mathematical operations and their relationships. It’s like a flowchart that shows how data flow through the deep learning model.

Training neural networks involves a lot of computations. So computation graphs help computers organize and execute calculations efficiently when training neural networks.

PyTorch is easy to use, making it a favoured choice among developers and researchers alike. For people who appreciate a straightforward framework for their projects, PyTorch is a perfect choice.

TensorFlow, Google’s brainchild, has robust production capabilities and support for distributed training. TensorFlow excels in scenarios where you need large-scale machine learning models in real-world applications.

Distributed training is a technique used in deep learning to train large and complex models. By spreading the training process across multiple machines or devices, it is useful when dealing with massive datasets.

Tensorflow is the go-to choice for companies that need scalability and reliability in their deep learning models.

So as you may be able to see, the choice between PyTorch and TensorFlow often depends on the specific needs of a project.

PyTorch vs TensorFlow – Which One’s Right for You?

Ease of Learning and Use

When you’re starting a new project, it’s helpful to have an easier learning curve. It helps both in building the project as well as hiring / training engineers for your project.

PyTorch is simpler and has a “Pythonic” way of doing things. It’s a favourite for beginners and researchers. And its dynamic computation graph means you can change things on the fly, which is great for experimentation.

TensorFlow offers a more structured approach. Its static computation graph requires a bit more planning ahead. TensorFlow also comes with a steep learning curve. But this can lead to more optimized and high-performance models.

TensorFlow 2.0 has also made strides in simplicity. It has incorporated more of PyTorch’s dynamic nature through its Eager Execution feature.

But when it comes to simplicity and ease of learning, PyTorch is a clear winner.

Performance and Scalability

When it comes to performance and scalability, TensorFlow shines. Its can handle large-scale, distributed training with ease. So TensorFlow is a go-to choice for production environments.

TensorFlow’s integrated tool, TensorBoard, is also a powerful tool for visualization and debugging.

PyTorch is catching up, with recent updates improving its scalability.

PyTorch has made improvements to support distributed training and scalability. It provides tools to help you train deep learning models on multiple GPUs and even across multiple machines.

But TensorFlow still holds the lead in deploying large-scale models in production.

Community and Support

The strength of a framework is also partly defined by its community. As these are open-source frameworks, there is no customer support. So you have to depend on the community for help if you get stuck while building a project using these frameworks.

TensorFlow, being older, has a larger community. It also has a vast array of tutorials, courses, and books.

PyTorch, while younger, has seen rapid growth in its community. PyTorch is a favourite, especially among researchers since it’s easy to use Pytorch for experimenting with datasets.

Both frameworks have strong support, but TensorFlow’s maturity gives it a slight edge in this area.

Flexibility and Innovation

If you’re working on cutting-edge research or need more flexibility, PyTorch is your best bet. Its dynamic computation graph allows for more creative and complex model architectures.

As I said before, this flexibility makes PyTorch a beloved tool in the research community. Where rapid prototyping and experimentation are key, PyTorch is your best option.

TensorFlow has been working towards adding more flexibility. But it’s a difficult battle to win since PyTorch is built for simplicity from the ground up.

Industry Adoption

PyTorch (blue) vs TensorFlow (red)

TensorFlow has tpyically had the upper hand, particularly in large companies and production environments. Its robustness and scalability make it a safe choice for businesses.

But PyTorch is quickly gaining ground. As you can see in the trends chart, PyTorch has already taken over TensorFlow as the most searched deep learning library. You can find the live chart here.

Multiple industries are starting to adopt PyTorch for research and development due to its user-friendliness and flexibility. Pytorch has also proved its capability as a production-grade tool after the release of models like ChatGPT.

Here is a list of companies using TensorFlow and PyTorch.

Products Using Tensorflow

  • Google Search and Recommendations: Google uses TensorFlow to enhance its search engine and recommendation systems. It helps improve search accuracy and provides personalized recommendations based on user behaviour and preferences.
  • NVIDIA Deep Learning Accelerator (NVDLA): NVDLA is a hardware accelerator for deep learning applications. It uses TensorFlow to optimize and deploy models on this hardware.
  • Uber’s Michelangelo: Uber uses TensorFlow in its Michelangelo platform for machine learning. It assists in various tasks, including ETA predictions, fraud detection, and dynamic pricing.

Products Using PyTorch

  • Facebook: Since PyTorch is from Facebook, Facebook uses PyTorch for various internal AI research and applications, including content recommendations and language translation.
  • Tesla Autopilot: Tesla’s Autopilot system relies on PyTorch for its deep learning components, such as object detection and navigation.
  • OpenAI’s GPT Models: Many of OpenAI’s language models, including GPT-2 and GPT-3, are built using PyTorch. These models are used for a wide range of natural language processing tasks, including text generation and language translation.


Choosing between PyTorch and TensorFlow depends on your project’s needs.

For those who need ease of use and flexibility, PyTorch is a great choice. If you prefer scalability from the ground up, production deployment, and a mature ecosystem, TensorFlow might be the way to go.

Both frameworks are evolving, so keep an eye on their development. Your choice today might not be your choice tomorrow. Remember, the best tool is the one that suits your project’s needs and not the popular one.

Thanks for coming this far. If you want weekly machine learning tutorials delivered to your inbox, join my newsletter. To get in touch with me, you can connect with me on LinkedIn.


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