Why TensorFlow for Python is dying a slow death

Religious wars have been a cornerstone in technology. Whether it’s debating the pros and cons of different operating systems, cloud providers, or deep learning frameworks – a few beers in, the facts slide aside and people start fighting for their technology like it’s the holy grail.

Just think of the endless talk about IDEs. Some people prefer VisualStudio, others use IntelliJ, still others use plain old editors like Vim. There is a never ending discussionhalf ironically, of course, about what your favorite word processor might say about your personality.

Similar wars seem to flare up around PyTorch and TensorFlow. Both camps have masses of supporters. And both camps have good arguments to suggest why their preferred deep learning framework is may be the best.

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That said, the data speaks a pretty simple truth. TensorFlow is the most widespread deep learning framework as of now. It gets almost twice as many questions about StackOverflow every month as PyTorch.

On the other hand, TensorFlow has not been growing since about 2018. PyTorch has been steadily gaining traction until the day this post was published.

For the sake of completeness I have also included Keras in the figure below. It was released around the same time as TensorFlow. But as you can see, it has tanked in recent years. The short explanation for this is that Keras is a bit simplistic and too slow for the requirements most deep learning practitioners have.