Deep Learning and AI

Choosing an ML Framework: TensorFlow vs PyTorch

May 26, 2023 • 7 min read


The 2 Powerhouse AI & ML Frameworks

There are 2 Machine Learning Frameworks that are most popular in the market: TensorFlow and PyTorch. Both are open-source Python libraries that use graphs to perform numerical computation on data used in research, commercial applications, and hobbyists. Their maturity and support make them the choice base framework for many deep learning enthusiasts. We want to compare the two and help you decide which one to use in your next deep learning project.

What is TensorFlow?

TensorFlow is an end-to-end open-source framework developed by Google released in 2015. It quickly gained popularity due to its scalability, flexibility, and extensive community support. TensorFlow's core functionality revolves around building and training deep learning models, making it a preferred choice for complex neural network architectures with multiple abstraction layers.

What is PyTorch?

PyTorch is developed by Facebook's AI Research group and released in 2016 based on the existing framework Torch. It gained traction for its intuitive and dynamic programming interface, which appealed to researchers and developers alike. PyTorch's primary focus is on providing a flexible and user-friendly framework for deep learning experimentation.

PyTorch vs TensorFlow

On the surface, TensorFlow is older, but PyTorch is just as developed. It also seems like the industry for Machine Learning is pivoting to PyTorch as the base Learning either of these frameworks should be a breeze; both are well developed and have extensive easy-to-follow documentation and plenty of tutorials and community support.

With modeling, starting from scratch is never the case anymore. Both TensorFlow and PyTorch have pre-trained models that can serve as a good starting point for your project. The community on Hugging Face also has plenty of community that posts pre-trained models both using PyTorch and TensorFlow and also includes courses on how to get started with Machine Learning.

Ease of Use and Flexibility

PyTorch is known for its:

  • simplicity and easy-to-understand syntax, making it an excellent choice for beginners
  • the syntax is direct and makes it easy for users to fully understand what you are doing inside your model
  • the dynamic computational graph allows for easy debugging and quick prototyping
  • The Pythonic nature enables intuitive coding and effortless integration with other libraries
  • The dynamic nature of PyTorch also makes it well-suited for handling irregular and varying input sizes

TensorFlow, on the other hand, is:

  • a more static computational graph approach and debugging is not as streamlined, requiring a debugging tool.
  • Requires defining the entire computation graph upfront, which can be advantageous for optimization and deployment purposes but trades for more complexity and structured and verbose API
  • TensorFlow's declarative nature and extensive documentation provide comprehensive support for building complex models
  • Graphs are then optimized and compiled for efficient execution
  • TensorFlow's static graph makes it more efficient for large-scale deployments and optimizing computational resources


PyTorch provides a more

  • intuitive and flexible training loop. Developers have fine-grained control over each step of the training process, allowing for easy customization and experimentation. The training loop in PyTorch typically involves iterating over batches of data, computing the loss, performing backpropagation, and updating the model parameters.
  • Has straightforward syntax and pythonic code that help developers fully understand what functions they are performing inside their training loop.
  • PyTorch's dynamic computational graph allows for automatic differentiation. Gradients are computed automatically as operations are executed, enabling efficient backpropagation Developers can easily access and manipulate gradients during training, making it convenient for implementing custom loss functions or advanced optimization techniques.
  • PyTorch offers built-in support for distributed training across multiple GPUs and machines. It provides tools like

TensorFlow offers a:

  • higher-level abstraction for training with its built-in training loops, such as
  • The syntax is more structured and verbose making it not as intuitive and not as pythonic as PyTorch. Many beginners might find it hard to fully grasp the functions they are performing within the training loop.
  • With TensorFlow 2.0 and above, eager execution has become the default, allowing for dynamic computation and automatic differentiation similar to PyTorch.
  • Traditional TensorFlow static computational graphs can let developers explicitly define variables and operations for differentiation if they choose to do so.
  • TensorFlow also provides distributed training capabilities through its


TensorFlow is the better framework for deployment models since the start. TensorFlow Serving and TensorFlow Lite enable easy deployment on the cloud, servers, mobile, and edge devices. Their additional year of development and release put TensorFlow on the top with aspects of developing applications and updating models on the fly.

PyTorch on the other hand has been more geared towards research. A lot of the models you might read in papers use PyTorch as their means of prototyping and development. However, PyTorch has made investments in deployment. Users before used libraries like Flask or Django but a few years back, PyTorch started offering TorchServe and PyTorch Live. While TensorFlow has a hold and edge on deployment, PyTorch is catching up with companies like OpenAI, Tesla, and Microsoft stating their choice of framework is PyTorch for deployment.

What Should You Use?

There is no correct answer except to select the framework that you are most comfortable with and match your use case. If you are unsure which one to learn, we recommend learning both! Some industries may value expertise in either framework. Here are some questions you can ask yourself:

  • Do you want everything in a single ecosystem?
    • Choose TensorFlow. It has a lot of various development frameworks with an end-to-end TensorFlow Extended platform.
  • Are you a researcher?
    • You probably already use PyTorch and stick with it. It is much more user-friendly and is the standard research framework.

  • Are you launching your model for Deployment?
    • Assing your deployment needs, TensorFlow delivers a more mature experience when delivering your model. PyTorch also has deployment options, however, only been released more recently. The adoption of PyTorch from major companies showcases its potential. Therefore the choice is up to you in which language is more valuable to you.


The discussion is nuanced as ever. Apart from these two frameworks are many others that we did not cover. And the previously popular TensorFlow is being overshadowed by PyTorch which is almost certainly becoming the new standard for deep learning and machine learning.

Many developers that used TensorFlow and have migrated to PyTorch emphasized the usability that PyTorch offers. The easy learn stack, readability, and flexibility have swayed many. Another sentiment is debugging; TensorFlow is more complex and difficult to understand but with PyTorch, its efficiency and debugging were built in instead of an afterthought later revised in TensorFlow 2.

Even if PyTorch does have weaknesses, there are plenty of resources that help alleviate them due to the dedicated community. However, new frameworks similar to TensorFlow have emerged like JAX which brings the granularity of TensorFlow with better stability, speed, and GPU-accelerated numerical calculations as opposed to Python’s NumPy. The platform is not as developed and not as mature but a good second language to learn to expand your skill set.

It's really more a matter of preference of what you want to work with since both aren't going away, and their drive to be the top dog will continue to spur innovation in the competitive deep-learning field. The gap between the two frameworks has converged and the mistakes made by one have been addressed by the other and vice versa. It's hard to recommend one over the other, so just try both of them out and see which one serves your needs the best.


deep learning and ai



machine learning

Related Content