Tensorflow vs Pytorch – Comparison, Features & Applications

Developing Machine Learning and Deep Learning architectures is a tricky business. However, over the years, many useful libraries have emerged to make the job of developers a little easier.

These libraries play a pivotal role in Machine Learning and Deep Learning researches and projects. Today, we’re going to talk about two popular Deep Learning libraries that have become a staple for developers around the world – TensorFlow and PyTorch. While TensorFlow has been under the limelight for years, today, PyTorch is becoming increasingly popular among freshers, thanks to its simplicity.

In this article, we will gain a deeper understanding of the two library frameworks by comparing and contrasting their intrinsic differences.

  1. Origins

Although both TensorFlow and PyTorch are open-source frameworks, TensorFlow was created by Google, and PyTorch was developed by Facebook. While TensorFlow is based on the idea of data flow graphs for models, PyTorch is based on Torch.

  1. Special features

Both TensorFlow and PyTorch have their fair share of unique features. For instance, TensorFlow has TensorBoard (excellent for visualization of ML models) and TensorFlow Serving (a gRPC server used for the deployment of models in production). Likewise, PyTorch has an array of useful features including dynamic computation graphs, naive support for Python, and support for CUDA – together these reduce the running time of the code and boost the code performance. 

  1. Computational graphs

The process of defining computational graphs in both frameworks differ. While TensorFlow is inclined towards creating static graphs, PyTorch defines computational graphs dynamically. So, in TensorFlow, you will first need to define the entire computation graph of the model, and only then can you run your ML model. On the contrary, PyTorch allows you to define your graph on-the-go – a graph is created at each execution point – and also manipulate the graph at run-time.

  1. Visualization

When it comes to visualization of ML and Deep Learning models, TensorFlow takes the crown. It is equipped with TensorBoard that not only makes it super easy to visualize models but is also quick in spotting errors in real-time. The end result is accurate graphs in real-time. This feature is missing in PyTorch, although it does have support for visualization tools like Matplotlib.

  1. Debugging

For debugging in TensorFlow, there’s a separate debugging tool – TFDBG – that allows you to see the internal structure of the running TensorFlow graphs during training and inference sessions. PyTorch too has its own debugging tools such as PDB or IPDB that make debugging a smooth and hassle-free process (mainly due to the dynamic computational aspect).

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  1. Serialization 

In serialization too, TensorFlow remains ahead of PyTorch. It allows you to save your entire graph as a protocol buffer and later loaded it in other supported languages. There’s no such provision for serialization in PyTorch.

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  1. Learning curve 

Since PyTorch is largely written in Python and comes with a C++ and CUDA backend, it features a simple and more intuitive structure for model building, unlike TensorFlow. Also, PyTorch is available for Linux, macOS, and Windows. For TensorFlow, you must possess extensive knowledge of its different parameters like placeholders, sessions, variable scoping, etc. Naturally, TensorFlow has a more steep learning curve than PyTorch. 

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  1. Community

The fact that TensorFlow has been around for much longer than PyTorch speaks volumes about its community backup – it has a bigger community than PyTorch. Hence, it is easier to find resources for TensorFlow. Also, if you ever get stuck in TensorFlow, there’s an active community out there to help you. However, since PyTorch is a relatively new framework, its community is still at a nascent stage.

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Wrapping up

Now, coming to the question, which framework is better,  there would be no right answer. Both the library frameworks are exceptional in their unique ways. However, TensorFlow does seem to have the upper hand over PyTorch when it comes to community support and visualization. On the contrary, for a beginner, who has just started experimenting with ML and Deep Learning models, PyTorch would be a great first choice. So, ultimately, it all depends on what you are looking for from these frameworks and how you intend to use them. 

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What are open source frameworks?

Open source is based on the idea of openly exchanging technology knowledge in order to enhance it through diverse perspectives and views. Because the technique is open source, the amount of effort required is minimized because many contributions are made by a large number of people. More than merely building a programming environment is what frameworks are all about. They aid in the development of a set of standards for certain activities. Open-source frameworks are development environments that anybody may access for free, and anyone can contribute improvements to them. Individuals who wish to make updates to the framework will face no obstacles.

What is serialization?

Serialization is the method of transforming an object instance's state information into a binary or textual format for storage or transmission across a network. It refers to transforming data into portable structures such as byte streams in Big Data. When vast volumes of data must be stored in flat files and accessed later, this method is utilized. Because the data structure is complex, achieving this without serialization becomes too laborious, error-prone, and difficult. It entails converting an object's public and private members, as well as the class and assembly names, into a sequence of bytes, which is then sent to the data stream.

What is visualization?

The use of more modern visualization tools to highlight the links inside data is referred to as visualization in Big Data. Beyond pie, bar, and other charts, visualization techniques include programs that can display significant changes and more illustrative visuals. The usage of hundreds of rows, columns, and characteristics is avoided in favor of a more aesthetic visual depiction of the data in these drawings. To exhibit statistics and develop relationships among bits of information, data visualization employs increasingly dynamic, graphical images that include personalization and animation.

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