PyTorch vs. TensorFlow: Which Deep Learning Model Will Work Best for You?

The rise of deep learning, one of the most interesting computer science topics, has also meant the rise of many machine learning frameworks and libraries — leading to debates in the community around platforms, like PyTorch vs TensorFlow.

Right now, the two most popular frameworks are PyTorch and TensorFlow — projects developed by big tech giants Facebook and Google, respectively.

These frameworks are similar — both are open-source libraries widely used in commercial and academic research for machine learning. They’re also different enough that you’ll want to carefully consider which framework you’ll use before you begin.

These are the most important differences between PyTorch and TensorFlow.

Why Is the Machine Learning Community Debating PyTorch vs TensorFlow?

To get started with machine learning, you need a framework. This framework supplies the tools needed to create machine learning models with the data you have on hand.

PyTorch and TensorFlow aren’t the only deep learning frameworks available — JAX, MXNet and PyTorch predecessor torch are all options — but they are far and away the most popular frameworks out there.

The two are similar in a few ways. Both frameworks are good for beginners to machine learning, as well as programmers who have some experience in using other frameworks. Both have robust and active userbases, as well as in-depth documentation and tutorials.

They’re also different enough to make the decision between PyTorch and TensorFlow important. Your choice of framework will have a significant impact on how you will program. Additionally, the framework you choose will determine how much work different tasks will require — like deployment or implementation of data parallelism.

What Is PyTorch?

PyTorch is an open-source Python machine learning package, developed by the Facebook machine learning team, Facebook AI Research. First released in 2016, it is based on the slightly older and Lua-based Torch machine learning framework.

Many significant deep learning projects are built on top of PyTorch. This includes the Autopilot feature used in Teslas and Pyro, the probabilistic programming language developed by Uber.

Like most machine learning frameworks, PyTorch offers two main features: machine learning with neural networks and tensor computing.

While PyTorch is primarily designed to use in Python, it also supports a C++ interface. PyTorch’s imperative and more “Pythonic” programming style make it somewhat unique among machine learning frameworks, which are typically more declarative.

What Is TensorFlow?

TensorFlow is an older, open-source machine learning framework, developed by Google Brain. They publicly released it in 2015 and it’s actively used at Google for both research and production.

It is based on a proprietary, closed-source Google machine learning framework, DistBelief.

The framework has interfaces for many programming languages, including Python, Java, C++, and JavaScript.

Two major versions of TensorFlow are available: TensorFlow 1, the original, and Tensorflow 2, which was released in late 2019. TensorFlow 2 makes a few changes to the framework that simplifies it and brings it closer in line with other current machine learning frameworks.

Key Differences: PyTorch vs TensorFlow

If you’re already a Python programmer, you may find PyTorch a little easier to use than TensorFlow due to the different coding styles these frameworks encourage.

Kirill Dubovikov, the CTO at Cinimex DataLab, breaks down some of these differences in a 2017 article for Towards Data Science. Dubovikov describes how TensorFlow often feels more like a library than a framework — “all operations are pretty low-level and you will need to write lots of boilerplate code even when you might not want to.”

While TensorFlow does provide abstractions that can help reduce the amount of boilerplate code you will have to write, the more Pythonic and imperative coding style enabled by PyTorch may help it feel a little more clear and user-friendly.

Particular features of the framework, however, may make TensorFlow more desirable in certain situations.

Data Visualization and Dashboards

TensorFlow includes a visualization toolkit, TensorBoard, that enables the display of data dashboards. PyTorch supports a visualization toolkit of its own, Visdom, but it is not as fully featured as TensorBoard. PyTorch also integrates with TensorBoard.

Scalability and Deployment

TensorFlow keeps scalability in mind. Therefore, large-scale projects that require the use of multiple servers may be easier to manage with the TensorFlow framework.

Traditionally, deploying TensorFlow models on browsers and phones through TensorFlow Extended (TFX), TensorFlow’s deployment platform, was slightly easier than deploying PyTorch ML models. Deployment, in general, was also more straightforward with TensorFlow.

This changed recently, with the 2020 launch of TorchServe, a tool for serving PyTorch models. The tool isn’t as complex as TFX but does provide a flexible and easy-to-use method for deployment.

Data Parallelism

Implementation of parallelism is also a key difference between the two frameworks. PyTorch optimizes performance by utilizing support for asynchronous execution built into Python — meaning you can distribute training across multiple GPUs with a single line of code. With TensorFlow, you need to do this manually, which means writing more code more often.

In general, PyTorch is the more “user-friendly” of the two frameworks and its design makes it a good fit for fast solutions and smaller-scale projects. TensorFlow has certain features that make it a good fit for larger teams, especially enterprise machine learning researchers.

The framework’s toolkit for deploying models on both mobile devices and servers is part of why the framework was considered the primary option for companies using machine learning. However, changes to PyTorch have made it a much more viable enterprise alternative over the past few years.

OtOther Considerations: Who Uses PyTorch vs TensorFlow?

When deciding which framework to use, you should consider both the state of the machine learning community as well as the technical differences between the two frameworks.

For a long time, TensorFlow was considered the go-to option by many industry professionals. As a result, if you were working with a professional data scientist or AI researcher, it was likely worth knowing TensowFlow, just to ensure you were both on the same page.

This trend hasn’t held in the past few years. According to data from Papers With Code, for example, the use of PyTorch outpaced TensorFlow in early 2019 and has only accelerated since. In June 2021, 58% of papers implemented PyTorch, while just 13% of papers implemented TensorFlow. The numbers for framework mentions at major conferences look similar, with researchers preferring PyTorch by a significant margin.

The drop in TensorFlow’s popularity roughly coincides with the release of TensorFlow version 2.0, and many modern implementations continue to use the older version of the framework.

There are no guarantees that this new trend towards PyTorch holds. Within a year or two, it’s possible TensorFlow will have regained its popularity or that a new framework will dominate the machine learning landscape.

Still, PyTorch has only become more popular at this point, and it’s likely it will remain the most popular machine learning framework well into the future.

Being older and more established, TensorFlow was better documented than PyTorch for a time. Now, however, it’s likely that both are as well-documented as each other. No matter which framework you choose, there won’t be a shortage of tutorials, documentation, or online message boards to help you learn how they work.

PyTorch vs Tensor Flow: Which Should You Use?

Both frameworks have a lot in common. They’re about as old as each other and both are well-documented, with large communities and resources available. Both languages are also backed by tech giants and are likely to stay in development well into the future.

PyTorch will likely be more accessible to beginners. It will probably be a better fit if you’re interested in quick, hackier solutions or if you’re experimenting with machine learning for the first time. It’s also likely to be the better option if you prefer a more imperative, Pythonic coding style, rather than the declarative style that TensorFlow will require.

If you need to distribute a workload over multiple GPUs, parallelism is also slightly easier to implement with PyTorch than with TensorFlow — but both platforms are capable of data parallelism.

TensorFlow, by way of TFX, makes deployment much easier and provides some functionality that PyTorch does not.

In general, PyTorch is likely going to be more convenient than TensorFlow, which provides some functionality that PyTorch doesn’t. For the most part, you won’t run into a situation where PyTorch can’t do something TensorFlow can or vice versa. Your personal preferences about coding style and the scale of your project will matter most.