The age-old question of ‘which deep learning framework to use’ has now changed. Developers are now asking ‘PyTorch vs TensorFlow: Which one out of the two should I use?’ While TensorFlow has been around for a long time, PyTorch is like the new kid on the block which has slowly and steadily built its base. The below graph by Gradient clearly shows how the popularity of PyTorch suddenly spiked between 2018 and 2019 as compared to TensorFlow and it is still continuing.

Source: Gradient

But does being popular mean PyTorch is the right framework for your project? When it comes to choosing between PyTorch and Tensorflow, you should first understand their basics, identify the main differences, and then align them with your project requirements to see which one fits your needs the best.

PyTorch vs TensorFlow: Understanding the Difference

Before we deep dive into understanding the differences between PyTorch and TensorFlow. Let’s first understand each framework individually.

What is PyTorch?

Developed by Facebook, PyTorch is an open-source machine learning library that has gained popularity in the last few years because of its ease of use, simplicity, efficient memory usage, and computational graphs. Since PyTorch is imperative, all computations run immediately on it. That means users don’t need to wait till they write the full code before checking if a section of code works correctly or not. You can run small pieces of code and PyTorch can inspect them in real-time.

Some of the top PyTorch examples include projects like: 

  • Horizon: An applied reinforced learning platform
  • Pyro: A probabilistic programming language which runs on a PyTorch backend and it is written in Python
  • CheXNet: A project by Stanford ML group which uses deep learning and PyTorch to detect radiologist level pneumonia in patient’s chest X-rays.

What is TensorFlow?

Developed and released by Google in 2015, TensorFlow is an open-source machine learning library. While it was originally developed for big numerical computations; it has also proved to be incredibly useful for deep learning developments. It can build and train machine learning models, which makes it a great option for easy debugging and quick model iteration. There are also numerous TensorFlow tutorials which are run on Google Colab and written in Jupyter notebooks which you can use to brush your basics (though they may not be beginner-friendly).

Some of the top TensorFlow examples include projects like: 

  • Magenta: Built on TensorFlow, Magenta is an open-source Python library which can create music and art by training machine learning models.
  • Ludwig: It is a toolbox which can directly train deep learning models without writing any code.
  • Sonnet: Powered by TensorFlow, Sonnet helps in creating complex neural networks.

PyTorch vs TensorFlow: Key Differences 

1. PyTorch vs TensorFlow: Computational Graphs

One of the biggest differences between PyTorch and TensorFlow is the way they handle computations graphs. While TensorFlow generates static graphs, PyTorch creates dynamic graphs. That is because, in the case of TensorFlow, the computational graph is generated only after the complete data processing is done. On the contrary, with PyTorch, you can manipulate your graph in real-time because it’s possible to run and inspect the code in real-time. 

2. PyTorch vs TensorFlow: Product Deployment

With TensorFlow, you can directly deploy a trained model to production using its REST Client API which makes the entire process incredibly easy. 

While with PyTorch, product deployments have definitely become easier than before with the newest 1.0 stable version, there is still no framework that you can use to deploy models directly, like TensorFlow. You would either have to use Django or Flask as the backend server for the deployment.

3. PyTorch vs TensorFlow: Visualisation 

When it comes to visualisation, TensorFlow offers TensorBoard which helps in quickly visualising machine learning models and spot any errors easily. Apart from generating computational graphs, it also helps in observing the behaviour of training parameters over a specific period of time, which in turn helps with debugging. 

PyTorch, on the other hand, doesn’t offer a native visualisation feature on its own. Instead, it has a graph visualisation package called Visdom which is limited and rather minimalist. More importantly, it doesn’t offer the same kind of versatility that TensorBoard does. There is also a PyTorch tutorial on the official website which can help you integrate PyTorch with TensorBoard, but even that isn’t as effective as using TensorBoard directly.

4. PyTorch vs TensorFlow: Device Management 

Users don’t need to define anything for different devices in the case of TensorFlow since all the default settings are pre-set. It will automatically assume that the user wants to be on the GPU if there is a GPU available. Though it also means that even if one GPU is already in use, it will consume the memory of all the available GPUs. In the case of PyTorch, you would have to move all the data onto the device to run on that particular device.

TensorFlow is a more mature machine learning library with powerful visualisation capabilities and options for high-level model development. On the other hand, PyTorch is a rather new library with a very active and strong community and it is also more Python friendly. While it is best to use PyTorch for research-related projects because of its incredibly fast and dynamic training, you should go for TensorFlow if you want to work on AI-related projects.

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