Why TensorFlow for Python is dying a slow death

Why TensorFlow for Python is dying a slow death

Religious wars have been a cornerstone in tech. Whether it’s debating about 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 about the endless talk about IDEs. Some people prefer VisualStudio, others use IntelliJ, again others use plain old editors like Vim. There’s a never-ending debate, half-ironic of course, about what your favorite text editor might say about your personality.

Similar wars seem to be flaring up around PyTorch and TensorFlow. Both camps have troves of supporters. And both camps have good arguments to suggest why their favorite deep learning framework might be the best.

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

On the other hand, TensorFlow hasn’t been growing since around 2018. PyTorch has been steadily gaining traction until the day this post got published.

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

Thirteen

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