PyTorch vs. TensorFlow

Ananya Avasthi
October 6, 2021

Deep learning has been growing in popularity for the past few years. Since its rise in popularity, two competitors have come on top of the deep learning spectrum. What is deep learning? Deep learning is an off-shoot of machine learning. It is inherently a neural network with three or more layers. These neural networks are responsible for simulating the behavior of the human brain. Of course, it cannot perfectly complete this task, but the more layers of neural networks there are, the better it can learn from colossal amounts of data. While a neural network with a single layer can create approximate predictions, additional hidden layers can assist in optimizing and refining the data for improved accuracy.

Deep learning assists artificial intelligence (AI) applications to become independent through providing services that improve automation, performing analytical and physical tasks without human intervention. Users utilize deep learning technology in everyday products and services like digital assistants, voice-enabled TV remotes, and credit card fraud detection, as well as emerging technologies like self-driving cars. 

 Many software has started incorporating deep learning due to its rise in popularity. PyTorch and TensorFlow have gained a significant lead in deep learning frameworks. These libraries are competing head-to-head for taking the crown for being the primary deep learning tool. TensorFlow is older, so it has always had an edge, but PyTorch has been doing a fantastic job catching up in the last six months. There are still a lot of discussions about which deep learning framework to choose for a project.

A comparison between TensorFlow & PyTorch


The AI development process often begins with data exploration, also known as exploratory data analysis (EDA). Teams start with this exploration to understand what approach is likely to work for the problem at hand. Data scientists explore data in several ways, including by making charts and other visualizations of a dataset. Visualization done manually takes a lot of time. PyTorch and TensorFlow both have elements for quick visual analysis: making the training process more manageable.


Tensorboard is the tool equipped for visualizing data. The interface is interactive and visually pleasant. Tensorboard creates a detailed synopsis of metrics and training data. The data is relatively easy to export and looks aesthetically pleasing for presentation purposes. The downside of Tensorboard is that it is cumbersome and complicated to use. Plugins make Tensorboard available for PyTorch too.


Visdom for visualization in PyTorch. The interface is considerably lighter and very simple to use. One thing Vidsow prides itself on is the flexibility it offers. Direct support for PyTorch tensors makes it very straightforward. However, Visdom lacks the interactivity Tensorboard has and has essential features missing for overviewing data.

Graph Generation

Having a static graph creates convenient operations: it stores a fixed graph data structure and ships models independent of code. It performs graph transformations but is more complex than dynamic graphs(for instance, while implementing recursive neural networks). When working with new architectures, the most flexibility possible is desirable, and these frameworks allow for that.


TensorFlow uses static graphs, which allow distribution over multiple machines. Models are used free from code. TensorFlow recently made an addition of mimicking dynamic charts called eager execution. This design makes TensorFlow more production-friendly and flexible while working with new architectures. 


PyTorchhas always incorporated dynamic graphs. This created competition between the two frameworks. A particular feature that allows diagrams to be changed anytime resonated with programmers and researchers regarding neural network generation. Structured data and size variations in data are simpler to deal with in dynamic graphs. PyTorch also uses static graphs.

Device Management

Massive fluctuations occur in performance while managing devices. The execution of both frameworks is different.


TensorFlow is designed to switch to GPU if one is available. Ona may control how the GPUs are accessed; however, the GPU acceleration is automated, so there is no control over memory usage.


PyTorch enhances the training process. PyTorch uses Compute Unified Device Architecture (CUDA is a parallel computing platform and application programming interface) to specify usage of GPU or CPU. It will not run without CUDA and give specifications about CPU or GPU, providing more control.

If one compares the two frameworks, it entirely depends on your need, as switching from one to the other is also a seamless process. PyTorch seems to be a favorite among programmers and scientific researchers. The scientific community tends to prefer PyTorch while working on citations. Meanwhile, organizations and startups prefer TensorFlow. The reputed production features give TensorFlow a good case. Visualization with Tensorboard creates an attractive presentation for clients.