Artificial Intelligence (AI) is becoming popular with businesses, marketers and developers. There are a number of coding frameworks to develop deep learning, including TensorFlow and PyTorch.
While PyTorch was developed by Meta (Facebook), TensorFlow is the brainchild of Google and it is used extensively for research.
In this practical guide, we take a closer look at both TensorFlow and PyTorch and how these deep learning frameworks differ.
What Is PyTorch?
PyTorch is a type of deep learning framework that is used to code and develop AI programs. It was originally developed by Meta’s AI research group.
PyTorch is suitable for applications that require natural language processing. Developers usually like this framework because of its flexibility, dynamic computational graphs and ease of use.
What Is TensorFlow?
Developed by Google in 2025, TensorFlow is an open-source deep learning framework that is well known for its scalable deployment options, extensive training support and multiple abstraction levels.
TensorFlow also supports a variety of different platforms, including Android.
As a symbolic math library, TensorFlow works well for data flow programming suited for various tasks across machine learning apps.
The Key Differences Between PyTorch And TensorFlow
While TensorFlow and PyTorch both allow you to create deep learning applications, these frameworks differ in many ways.
Here is an overview of all the differences between TensorFlow and PyTorch.
Adoption
PyTorch is a relatively new development but it is getting a lot of traction within the AI world. This popularity is thanks to the official tutorials and documentation that you can find for PyTorch.
Plus, PyTotch also integrates with the most popular computer vision architecture which makes it easy to use.
On the other hand, TensorFlow has been around for a while and it is well-known with industry professionals and researchers.
TensorFlow also comes with plenty of documentation and various tutorials across the web. You can even find trained models on GitHub.
Static And Dynamic Graph Definition
While both frameworks work with tensors and view any models as a DAG (digital acyclic graph), the definition setup of the two differs greatly.
With TensorFlow, data is simply code and the code is considered data. This means that you can define graph as a static before you run your model.
In comparison, PyTorch is much more dynamic. You can change and define different nodes as you continue your work. You don’t need to set any special placeholders or session interfaces.
Generally, PyTorch is more closely integrated with Python which makes it feel more native as a coder. With TensorFlow, the model can feel like it is lagging behind.
Debugging
With PyTorch, you can use standard debugging tools, such as PyCharm, ipdb or pdb, to debug your code because of PyTorch’s closeness to Python.
However, TensorFlow has its own special debugging tool, known as tfdbg. This browses all operations and tensors in a session to check for any errors.
This means that you won’t be able to debug any Python code with TensorFlow which can restrict your application.
Visualization
When you are working with TensorFlow, then you will automatically get Tensorboard which is their visualization tool. You can use it to compare training runs or debug your model.
You can train your model, adjust parameters and then train it again. You can even display the differences between both runs side by side in Tensorboard.
Tensorboard can visualize histograms, distributions, images and embeddings. It can also play audio and plot scalar variables.
You can even get integrations with Tensorboard or use your standard plotting tools, such as seaborn or matplotlib.
While PyTorch has a similar tool, known as visdom, it is not as feature-rich as Tensorboard. This being said, it is much easier to use.
Deployment
TensorFlow offers TensorFlow Serving which allows you to deploy your different models on a special gRPC server. It even supports mobile applications.
In comparison, PyTorch requires a REST API on top of the model for deployment. You can use Flask or any other API alternative.
You can also use the option of a REST API if TensorFlow’s gRPC server doesn’t work for your application. However, we found that TensorFlow Serving works well for use cases that require high performance.
It is also worth saying that TensorFlow supports detailed training for their deployment process.
Data Parallelism
One of the biggest differences between TensorFlow and PyTorch is declarative data parallelism.
PyTorch allows you to wrap any module and make it parallel as part of a batch. This makes it much faster to work with multiple GPUs.
In comparison, TensorFlow only allows you to adjust individual operations for specific devices. While this works with basic models, this manual process can be extremely labor intensive.
Saying this, you can make each model work in TensorFlow but the additional control you get with TensorFlow means that you will need to put more thought into your approach.
Training Time
While both PyTorch and TensorFlow are fairly accurate, it takes much longer to train TensorFlow. This being said, the Google AI framework uses a lot less memory.
On the other hand, PyTorch allows you to process your prototype much faster, although you won’t get as many inbuilt features as with TensorFlow.
Framework VS Library
Fundamentally, PyTorch and TensorFlow have different approaches to deep learning. While both of these AI coding programs are sometimes called a framework or library, there are clear differences between the two.
Both libraries and frameworks allow you to pre-code programs to develop artificial intelligence software applications.
A framework gives you the entire setup to create and develop your AI models and the rest of the application. On the other hand, a library only offers a specific set of features or functionality.
TensorFlow is considered a solid deep learning library, while PyTorch gives you the option to code your complete application without the need for additional software.
Ease Of Use
Both PyTorch and TensorFlow are relatively easy to use for AI professionals, although TensorFlow is slightly easier when it comes to debugging your code.
This is the reason why many developers use Python debugging tools when they work with PyTorch.
Is PyTorch Better Than TensorFlow?
Overall, TensorFlow is a very powerful deep learning library that has some fantastic features for model development and visualization.
You can use the different production-ready deployment options and even deploy your applications on mobile platforms.
Another great advantage of TensorFlow is that there is extensive documentation and a lot of community support, so it is great for beginners.
On the other hand, you can create and deploy artificial intelligence models with PyTorch in a similar way to TensorFlow.
Just keep in mind that PyTorch hasn’t been around for too long and there aren’t many tutorials on the individual features yet.
If you love Python or Python-related features and you need a mature debugging and development platform, then PyTorch is much better than the TensorFlow library.
Final Thoughts
Both TensorFlow and PyTorch are frameworks that you can use to create, develop and deploy AI models. They just have slightly different approaches that can work for various businesses.
It is important for AI professionals to understand the individual features of these frameworks and then choose the right one for your use case.
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