PyTorch vs TensorFlow

Many machine learning frameworks have competed to become the next industry standard among researchers and practitioners. From Caffe and Theano's early academic output to PyTorch and TensorFlow's significant commercial support and leadership. There would be 6–7 distinct deep learning frameworks available.

Except for TensorFlow and PyTorch, most deep learning (subfield of Machine Learning) frameworks failed to make the cut.

What better place to start than GitHub repositories? TensorFlow has 165K stars, whereas PyTorch has roughly 55.8K stars, which is OK because it was launched later.

A data-driven comparison will not be useful because times and requirements will change, but a comparison based on application differentiation can give us a decent indication of what we should explore and pursue.

But first, we need to answer a question.

If you work in a deep learning-based business, you're presumably already using TensorFlow and should remain with it. For those who require models that can go into production quickly, TensorFlow's powerful deployment mechanism and extensible platform are vital. Model monitoring and artefact tracking (a consequence of software development that aids in documenting the architecture, design, and operation of the software) are key tools for industrial usage, as are easy deployment on a gRPC (Google Remote Procedure Call) server.

With the recent release of Torch Serve, you might be contemplating utilising PyTorch to access SOTA (State of the Art) models accessible in PyTorch (but only if you have a good cause).

Given the recent release of PyTorch Live, you can explore utilising PyTorch to develop mobile applications, but you'll need audio or video input at some point, which is where TensorFlow comes in.

Research

If you're a researcher or interested in research, PyTorch is a good choice because working in a pythonic environment reduces complexity.

There are a few exceptions to this rule, the most famous of which being the use of TensorFlow in Reinforcement Learning. TensorFlow's Native Agent Library, along with DeepMind's Acme framework, provides the ideal setting for Reinforcement Learning.

The implementation of OpenAI's Baseline model repository is also visible in TensorFlow, although it is not platform-specific because it can be utilised in both TensorFlow and PyTorch.

If you are conducting TPU (Tensor Processing Unit) training and don't want to utilise TensorFlow, you should look into Google's JAX. It is not a neural network framework in and of itself, but it is closer to NumPy implementation and will provide you with a Python experience. You may use Google's Flax as an alternative.

If TPU trainings are not in your plans, sticking with PyTorch is the best option.

Professor/Student

If you're a professor or a student, the framework you employ for deep learning is entirely dependent on your objectives and desired outcomes.

Students should utilise TensorFlow if their goal is to become an industry-ready Deep Learning engineer who can grasp the full Deep Learning process, not just Deep Learning theory. Exposure to the TensorFlow ecosystem and tools, as well as practise projects, will be highly educational and beneficial in this scenario.

Professors, if the focus of your course is on Deep Learning theory and understanding the Deep Learning models, Py Torch is your thing. This is especially true if you're teaching a high-level UG (Undergraduate) or early PG-level course with the goal of preparing students to conduct Deep Learning research.

Students should learn how to operate with each framework and devote some time to comprehending the distinctions between them. Despite the time restrictions of tests, this is really helpful in the long term.

Conclusion

This dispute will never die out, especially as technology advances, but both frameworks are well-developed and have overlapping functionality in deep learning applications.

While TensorFlow remains the platform of choice, thanks to Industry's legacy, PyTorch has emerged as the de facto framework for doing research.