PyTorch and TensorFlow — the promise of AI
To fully understand each deep learning library, we will do a deep dive into its origins and operation mechanisms. The older and more established of the two, TensorFlow, was developed by a team at Google and released as an open-source framework in 2015. Since then, it has skyrocketed in popularity largely due to its well-documented framework and large, active community with many resources. Today, TensorFlow is used by many leading tech companies including Google, Uber, and Microsoft. Like TensorFlow, PyTorch also originated from a FAANG development team — at Facebook, now Meta. Released in 2017, PyTorch was based on a combination of Python and machine learning library Torch. PyTorch has continued to grow in popularity among AI researchers since its release and has also been used in many machine learning applications.
As two of the most popular AI libraries today, both PyTorch and TensorFlow naturally have loyal user bases, for different reasons. Some of PyTorch’s advantages include its smaller learning curve due to the syntax’s similarity to Python, and the fact that it is much easier to debug. In addition, PyTorch also has more adaptable use cases. On the other hand, TensorFlow has a more extensive library of tools and much better support for visualization, in addition to its more comprehensive documentation and large user community. TensorFlow also has many other products in its ecosystem that support deployment, such as TensorFlow Serving and TensorFlow Lite.
Another significant difference between the two libraries lies in how they define their computational graphs. In machine learning, a computational graph represents how a model takes in input, performs operations on the input, and eventually returns an output. The two libraries each take a different approach to defining and executing computation graphs: TensorFlow uses a static representation, while PyTorch takes a dynamic approach. To put it simply, TensorFlow’s static representation means that the structure of the computation is predefined, and only the input values change during execution. On the other hand, PyTorch’s dynamic graph representation allows the graph to be defined as you execute operations, meaning that the graph’s structure can change depending on the input data and conditions. This key difference means that PyTorch can offer more flexibility and make it easier to adapt models to different scenarios.
Overall, both PyTorch and TensorFlow are well-loved machine learning libraries that can be used to build powerful and exciting AI tools. In its current state, TensorFlow is a better option for developers who desire more support and better deployment capabilities, but those in the experimental or research phase looking for more flexibility may prefer PyTorch. In either case, exploring each library’s capabilities and understanding its internal mechanisms is undeniably a worthwhile endeavor for any prospective AI developer.
