Yogesh-R | March 16th, 2022
Tensorflow vs PyTorch

The never ending confusion

Are you a deep learning enthusiast forever confused between TensorFlow and Pytorch?

Or are you just starting out your data science journey and don’t know which one to commit to?

Well we have got you covered! So down to the basics, TensorFlow is an open sourced library based on Theano created by Google while PyTorch is an open sourced library based on Torch created by Facebook.

While TensorFlow has been around, tried, tested and benchmarked by numerous data scientist, PyTorch is still swimming is shallow waters. But this doesn’t mean that TensorFlow is the unprecedented winner! PyTorch offers support for building dynamic graphs avoiding head scratching problems building complex computational graphs. Whereas TensorFlow requires you to work on dimensions of each tensor and assign placeholders well in advance. While TensorFlow does require a little heavy work while building graphs, the visualization offered by TensorFlow beats all.

The real time representation of graphs and models offered by Tensor board surpasses the almost nonexistent visualization of models offered by PyTorch.

There are innumerable resources, tutorials, documentation and loyal communities backing up and contributing to both these libraries but TensorFlow here too has an additional plus point owed to the fact that it has been around longer and used far and wide around the land.

While TensorFlow does offer a strong base of tried and tested projects, PyTorch is often used more in research and academia due to its dynamic nature. While PyTorch and TensorFlow both offer GPU extension, TensorFlow consumes the whole memory for all the available GPU.

As TensorFlow follows static computational graph approach, it is easier to optimize the code with this framework. PyTorch on the other hand, comes with already enabled GPU usage with CUDA installation. It tries to find a GPU to compute even when running over a CPU. PyTorch is gaining popularity amongst researchers and beginners due to its reliability on dynamic computational graphs. It’s easier to focus on the model rather than the graph with PyTorch whereas TensorFlow focuses on building the graph. TensorFlow is the more reliable library which can be used for high grade projects and real world applications with the reliable backing of Google, whereas PyTorch is great to begin with and use in research and personal projects.