Tensorflow vs Pytorch

Since you are here on this article, I assume you have started your deep learning journey or just starting and you are in a dilemma like many others.

Since there are so many options available in Machine Learning that we all get confused at from time to time.One the very common confusion we have is to choose between deep learning libraries: Tensorflow and PyTorch.

So let us clear this doubt here. We will not tell you which one to use but give you few points of comparison that will help you decide which one is the best for your case. So let’s begin.


Looking at the trend below what we see is, both Tensorflow and Pytorch are neck to neck when it comes to google search. After the initial growth of Tensorflow, we seen negative trend for Tensorflow. While Pytorch came after Tensorflow, but its long trend has been positive.


Both Tensorflow and PyTorch are opensource and backed by two tech gaints. On one hand we have Tensorflow that has been developed by Google, whereas on the other hand we have PyTorch that has been developed by Facebook.


One of the major difference between Tensorflow and Pytorch is in their computational graph implementation. While Tensorflow builds a static graph, PyTorch builds in a dynamic graph. So it means in Tensorflow, the entire computation graph for a model is defined first and then we run the model. But in PyTorch, we can dynamically manipulate the graph on-the-go. This is one of the reason a lot of researched like PyTorch where they get to play with the graph.


Another major point to consider is that Pytorch is Python First. It has deep integration with Python. Hence building Machine Learning models in Pytorch feels more intuitive. While for Tensorflow, we need to understand about it’s working (sessions, placeholders etc.). So in short the learning curve is steeper in Tensorflow.


Tensorflow with Google’s backing and with an early start has a much bigger community supporting it than PyTorch. So finding finding resources to learn Tensorflow becomes easier. We have many Tensorflow tutorials and MOOCs instead of using PyTorch. But don’t get us wrong, PyTorch has been consistently growing and has a rich supports rich ecosystem of tools and libraries.


Tensorflow provides TensorBoard. It is an excellent tool that helps us visualizing our ML models. PyTorch doesn’t have any such tool out of the box, although we can do it with Matplotlib with some effort.

Point #7:

When it comes to putting your model into production Tensorflow is better. It was built to be production ready and is scalable. While, PyTorch is easir to learn, provides scalable distributed training and is lighter to use. Hence it is preferred for research prototyping.

So which one to use?

As you understand from the above points it is difficult for us as well to suggest one of them to you. There is no right or wrong answer here. It depends on your choice and area of work.

Both are great deep learning frameworks with two big giants backing them. At the end of the day both of them get the job done. If your have not yet decided, toss a coin and pick any one and get started and decide as you learn.

That’s it for this post, thank you for reading. If you want to learn the basic of Pytorch do read our post Introduction to Pytorch.

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