Pytorch vs TensorFlow Overview
If you are here, you’re likely to have begun your journey of Deep Learning, because when I did start mine, I asked the same question, Pytorch or TensorFlow? Well, as you might already know that these two are Popular Deep Learning frameworks by two tech giants Google and Facebook.
TensorFlow and Pytorch frameworks were built to help researchers and technology geeks to learn, build, train and use powerful AI solutions to change the course of current world affairs.
In this post, I want to explain some of the key similarities, differences and compare the two popular deep learning frameworks Pytorch and TensorFlow based on some significant considerations to help you pick one whenever you are not certain which one to go with.
Pytorch is Object-Oriented and more Pythonic than TensorFlow. It seems to be more clear, developer-friendly and due to its modular structure, it gives you the ability to define reusable modules in an OOP manner which is very flexible and powerful. On the other hand, it is hard to make quick changes to TensorFlow.
This one is a bit tricky. Both Pytorch and TensorFlow support building models from scratch, using pre-trained models and customizing pre-trained models. Pytorch due to its modular approach makes model building from scratch very smooth and easy for projects with bigger scope and functionalities while TensorFlow using Keras is suitable for beginners only.
When it comes to using pre-trained models and customizing them, both frameworks have a huge range of models available trained on trillions of data to serve users with limited resources, but Pytorch stays a step ahead of TensorFlow in terms of wide variety and support. Although TensorFlow easily lets you customize what’s available in their model garden.
When it comes to Deploying Apps or Trained Models to production, TensorFlow is the clear winner. It possesses production-ready deployment options and great support for mobile platforms. PyTorch, on the other hand, is still a young framework and does not provide support on production platforms. It is more popular among the research community.
Although PyTorch 1.0 will be released soon and it will come up with lots of amazing features of integrations, it will be then set for providing production-ready services and become useful for both research and production purposes just like TensorFlow or better, who knows.
Both PyTorch and TensorFlow are capable frameworks from the modeling perspective to the technical differences, but Deep Learning no longer depends on specific use cases in small environments, rather on the larger ecosystems surrounding it which facilitates development for mobile, local, and server applications. From this aspect, whichever framework provides tools for easy deployment, management, and distributed training stands out. Let’s take a look at each framework’s ecosystem now.
Both the frameworks provide a wide range of facilities to enlarge their ecosystem and provide overall better services. But TensorFlow is definitely ahead of PyTorch in this regard. Here’s why:
PyTorch provides a huge variety of services ranging from model Hub, Libraries like TorchVision, TorchText, TorchAudio, SpeechBrain, TorchElastic, to TorchX to enable activities like deployment, management, and support.
TensorFlow, on the other hand, offers a wider range of products and services like TensorFlow Hub for complex pre-trained ML Models, Model Garden for customizing those pre-trained models as per needs, Google’s Datasets, Cloud, Colab, Playgroups for complex NLP problems and other countless advanced yet useful things like AutoML, etc. Indeed, Google has invested heavily in ensuring that there is an available product in each relevant area of an end-to-end Deep Learning workflow to make impossible things possible.
As it is clear, the PyTorch vs TensorFlow debate isn’t simple as it constantly changes with respect to the landscape and out-of-date information would make it even more complicated. In 2022, both PyTorch and TensorFlow frameworks are very advanced , and their core Deep Learning features overlap each other notably but their extraordinary services like their model availability, time to deploy, and associated ecosystems, overrule their technical differences.
So, in the end, You’re not wrong to choose either framework, as both have good documentation, many learning resources, and active communities. While PyTorch is more popular and adopted by the research community and TensorFlow remains the dominant industry framework, there are certainly use cases for each in both domains.
Hopefully, this article provided some clarity about the complex PyTorch vs TensorFlow landscape! Anyway, at the end of the day, it never hurts to come out of your comfort zone and try out newer things.