PyTorch and TensorFlow are the two most popular frameworks for deep learning. The debate about which one is better does not go away, with each camp having its share of supporters.
In recent years, PyTorch and TensorFlow have developed so rapidly that the debate is constantly ongoing. Outdated or incomplete information is present in abundance, which further confuses the complex discussion; you can ask your own questions to Python programmers here.
Although TensorFlow has a reputation for being industry-oriented and PyTorch for research, we will see that these views are partly based on outdated information. Let’s look at their differences now.
PyTorch is a relatively new deep-learning framework based on Torch. Developed by Facebook’s AI research group and published on GitHub in 2017, it is used for NLP apps. The PyTorch framework has a reputation for simplicity, ease of use, flexibility, efficient memory usage, and dynamic computational graphs. In addition, it makes coding more convenient and increases processing speed.
TensorFlow is an open-source deep-learning framework developed by Google and released in 2015. It is known for documentation and training support, scalable production and deployment capabilities, multiple levels of abstraction, and support for various platforms such as Linux, Android, etc.
TensorFlow is a symbolic mathematics library used for neural networks and is best suited for programming data flow in various tasks. It offers several levels of abstraction for building and training models.
TensorFlow is a promising and rapidly developing platform for deep learning. It offers a flexible, comprehensive ecosystem of community resources, libraries, and tools that facilitate the creation and deployment of machine-learning applications.
PyTorch or TensorFlow?
Both offer useful abstractions that facilitate model development by reducing template code. PyTorch has a more “pythonic” approach and is object-oriented, while TensorFlow offers many options.
PyTorch is used in many deep learning projects today, and its popularity is growing among AI researchers, although it is the least popular of the two frameworks. Trends show that this may change soon.
When researchers need flexibility, debugging capability, and a short duration of the training, they choose PyTorch. It runs on Linux, macOS, and Windows.
Thanks to a well-documented framework, an abundance of trained models, and textbooks, TensorFlow is a favorite tool for many industry professionals and researchers. TensorFlow offers better visualization, which allows developers to debug and monitor the learning process. PyTorch, however, provides only limited visualization.
TensorFlow also outperforms PyTorch in deploying trained models in production, thanks to the TensorFlow Serving framework. PyTorch does not offer such a framework, so developers have to use Django or Flask as an internal server.
PyTorch achieves optimal performance in data parallelism by relying on Python’s built-in asynchronous execution support. However, in the case of TensorFlow, it is necessary to manually encode and optimize each operation performed on a specific device to provide distributed training. In general, you can repeat everything done in PyTorch in TensorFlow; you just need to work more on it.
If you are just starting with machine learning, you should first master PyTorch, as it is popular in the research community. However, if you are familiar with machine learning/deep learning and aim to get a job in this industry as soon as possible, study TensorFlow first.
As you can see, the difference is subtle. The landscape is constantly changing, and outdated information makes it more difficult to understand the whole picture. Today, PyTorch and TensorFlow became very mature platforms, and their core deep-learning functions overlap significantly. Today, the practical aspects of each framework, such as the availability of their models, deployment time, and associated ecosystems, displace their technical differences.
You can’t go wrong by choosing one or another because both have good documentation, lots of training resources, and active communities. Although PyTorch has become a de facto research framework after its polishing by the research community, and TensorFlow remains an inherited industrial framework, there are certainly cases of using each of them in both areas.
These frameworks have the ability to revolutionize all aspects of deep learning, from virtual assistance to driving a car around the city. It will be simple and subtle and will have an immersive impact on the industry and users!