TensorFlow vs PyTorch
Deep learning, one of the most fascinating subjects in computer science, has spawned a slew of machine learning frameworks and libraries, sparking community discussions about platforms like PyTorch vs TensorFlow.
Currently, the most prominent frameworks are PyTorch and TensorFlow, which were created by Facebook and Google, respectively.
Both of these frameworks are open-source libraries for machine learning that are widely utilised in commercial and academic research. They’re also distinct enough that you’ll want to think about the framework you’ll use before getting started.
Why the comparison?
Why is there a debate between PyTorch and TensorFlow in the machine learning community? You’ll need a framework to get started with machine learning. This framework gives you the tools you need to build machine learning models using the data you already have.
TensorFlow and PyTorch aren’t the only deep learning frameworks out there – JAX, MXNet, and PyTorch’s predecessor torch are all viable possibilities — but they are by far the most popular.
In a few ways, the two are similar. Both frameworks are suitable for machine learning beginners as well as programmers with prior experience with other frameworks. Both have large, active user bases, as well as comprehensive documentation and tutorials.
They’re also distinct enough that choosing between PyTorch and TensorFlow is crucial. The framework you choose will have a big impact on how you programme. Furthermore, the framework you select will impact how much effort particular tasks — such as deployment or data parallelism implementation — would demand.
PyTorch is an open-source Python machine learning software created by Facebook AI Research’s machine learning team. It was first released in 2016 and is based on the Torch machine learning framework, which is slightly older and uses Lua.
PyTorch is used in a number of notable deep learning applications. This contains the Tesla Autopilot feature as well as Pyro, Uber’s probabilistic programming language.
PyTorch, like most machine learning frameworks, has two major features: neural network machine learning and tensor computing.
While PyTorch was created with Python in mind, it also has a C++ interface. PyTorch stands out among machine learning frameworks because of its imperative and “Pythonic” programming style. Most machine learning frameworks are declarative.
Google Brain created TensorFlow, an earlier open-source machine learning framework. It was first made public in 2015, and it is still in use at Google for both research and production.
It’s based on DistBelief, a Google machine learning framework that’s private and closed-source.
TensorFlow is available in two major versions: the original TensorFlow and TensorFlow 2, which was launched in late 2019. TensorFlow 2 provides a few enhancements to the framework that make it easier to use and more comparable to other machine learning frameworks.
PyTorch vs TensorFlow
The following are the most significant distinctions between PyTorch and TensorFlow.
Because of the different coding styles that these frameworks encourage, PyTorch may be easier to use than TensorFlow if you’re already a Python coder.
In a 2017 essay for Towards Data Science, Kirill Dubovikov, the CTO of Cinimex DataLab, lays down some of these discrepancies. TensorFlow, according to Dubovikov, “feels more like a library than a framework” since “all operations are quite low-level and you will need to write a lot of boilerplate code even if you don’t want to.” While TensorFlow offers abstractions that can help you write less boilerplate code, PyTorch’s more Pythonic and imperative programming style may make it feel more obvious and user-friendly.
However, certain aspects of the framework may make TensorFlow more appealing in specific cases.
Dashboards and data visualisation
TensorFlow contains TensorBoard, a visualisation framework for displaying data dashboards. PyTorch has its own visualisation toolkit, Visdom, however, it isn’t as comprehensive as TensorBoard. TensorBoard is also integrated with PyTorch.
Deployment and Scalability
TensorFlow considers scalability. As a result, large-scale applications that need the usage of several servers may find the TensorFlow framework to be easier to handle.
TensorFlow models have traditionally been easier to deploy on browsers and phones using TensorFlow Extended (TFX), TensorFlow’s deployment infrastructure, than PyTorch ML models. TensorFlow also made deployment easier in general.
This changed in 2020, when TorchServe, a tool for serving PyTorch models, was released. The tool isn’t as complicated as TFX, but it does offer a flexible and simple deployment mechanism.
Parallelism implementation is also a significant distinction between the two frameworks. PyTorch improves speed by taking advantage of Python’s asynchronous execution capabilities, which allows you to distribute training across numerous GPUs with a single line of code. With TensorFlow, you’ll have to do it manually, which means more code will be written.
PyTorch is the more “user-friendly” of the two frameworks, and its design makes it ideal for quick solutions and smaller applications. TensorFlow has some capabilities that make it ideal for larger groups, particularly enterprise machine learning researchers.
The framework’s toolbox for deploying models on both mobile devices and servers is one of the reasons it was deemed the best option for machine-learning firms. Over the last few years, though, modifications to PyTorch have made it a far more attractive commercial option.
Feature TensorFlow PyTorch
Dataset. Ideal for huge datasets and high-performance models. Ideal for huge datasets and high-performance models.
The API level. Provides both high and low-level APIs Provides only low-level APIs
Performance. High Performance. High Performance.
Architecture. It’s complicated, and it might not be particularly useful for beginners. The complexity is high, and the readability is low.
Ease of Use. Because the number of lines of code is less than PyTorch, it produces a smaller model with higher accuracy. There are more lines of code to write, and it isn’t as straightforward as Tensorflow.
Debugging. Difficult to debug. Easy to debug.
PyTorch vs.TensorFlow: Who Uses Which?
You should think about the state of the machine learning community as well as the technical differences between the two frameworks when determining which to choose.
Many industry professionals believed TensorFlow to be the go-to option for a long time. As a result, learning TensowFlow was probably worth knowing if you were working with a professional data scientist or AI researcher, just to make sure you were on the same page.
This pattern has shifted in recent years. PyTorch, for example, exceeded TensorFlow in early 2019 and has only grown since then, according to data from Papers With Code. PyTorch was used in 58 per cent of articles in June 2021, whereas TensorFlow was used in only 13 per cent of publications. Researchers prefer PyTorch by a large margin, according to the data for framework mentioned at important conferences.
The decline in TensorFlow’s popularity generally corresponds to the release of TensorFlow version 2.0, although many recent implementations still utilise the older version.
There are no guarantees that the new PyTorch trend will continue. It’s possible that TensorFlow will regain its prominence in a year or two, or that a new framework may take over the machine learning environment.
Nonetheless, PyTorch has grown in popularity, and it is expected to be the most popular machine learning framework for a long time.
For a time, TensorFlow was better documented than PyTorch since it was older and more established. However, both are likely to be as well-documented as each other now. There will be no shortage of tutorials, documentation, or online discussion forums to help you learn how to use whichever framework you select.
What should you use: TensorFlow or PyTorch?
Both frameworks share a lot of similarities. They’re both approximately the same age and have well-documented histories, as well as big communities and resources. Both languages are funded by tech behemoths and are expected to remain in development for the foreseeable future.
PyTorch will most likely be easier to use for beginners. If you’re looking for quick, hacky solutions or are trying out machine learning for the first time, it’ll probably be a better fit. It’s also likely to be the superior choice if you prefer a more imperative, Pythonic coding approach to TensorFlow’s declarative style.
Parallelism is slightly easier to achieve with PyTorch than with TensorFlow if you need to distribute a workload across multiple GPUs, but both systems are capable of data parallelism.
TensorFlow, via TFX, makes deployment considerably easier and adds features to PyTorch that PyTorch lacks.
In general, PyTorch will be more convenient than TensorFlow, which offers several features PyTorch lacks. You won’t run into a situation where PyTorch can’t accomplish something that TensorFlow can, and vice versa, for the most part. The size of your project and your personal preferences for coding style will be the most important factors.