PyTorch vs TensorFlow: What Are They, and Which Should You Use?

Akshat Biyani, CareerFoundry Contributor

Deep learning, artificial intelligence, machine learning. You’ve probably seen or heard these words before, and might even know a fair bit about them. But what do they really mean, and how do you start working with them? Most people choose to begin their adventures with machine learning with either PyTorch or TensorFlow. 

PyTorch and TensorFlow are leading machine learning libraries used to power thousands of highly intelligent applications. Developed by the big players in tech – Facebook’s Artificial Intelligence Research lab and Google’s Brain team, these platforms deal with deep learning.

In this article, we will tell you about the origins of PyTorch and TensorFlow and explain the use case for each of them. You can also use the following clickable menu to skip forward to a section you’re interested in.

  1. What is deep learning?
  2. What is PyTorch?
  3. What is TensorFlow?
  4. PyTorch vs TensorFlow: Which should you use?
  5. Key takeaways and next steps

With that, let’s get started!

 

What is deep learning?

If you’ve heard about PyTorch and TensorFlow, you may have also heard about deep learning, but what exactly is it? Let’s recap to find out.

Highly intelligent computer programs capable of ‘learning’ have been around for a couple of decades now. The modern ones use an ingenious technique called deep learning. Deep learning employs an artificial neural network with three or more layers. These networks attempt to learn from large sets of data—and when we say large, we mean LARGE. Now, while a single layer of neural networks can make approximate predictions on its own, it’s the multi-layer structure that makes deep learning algorithms so incredibly accurate. 

Deep learning drives many artificial intelligence (AI) applications and services that drive automation and perform analytical and physical tasks without human intervention. These applications are everywhere around you—from something as seemingly simple as a weather app on your phone, to more sophisticated state-of-the-art gadgets like self-driving cars. Google’s image-based CAPTCHA (yes, that annoying process of selecting all the boxes with cars in them to prove you’re not a bot before sending a form), these applications are used to feed their deep learning programs. These programs then process billions of bytes of data and create algorithms that power things like Google’s self-driving cars!

​​But how do you start dipping your feet in the data analytics pond? In this post, we’ll compare two of the most popular deep learning programs: PyTorch and TensorFlow. 

Learn more: What’s the Difference Between Machine Learning and Deep Learning?

What is PyTorch? 

Simply put, PyTorch is an open-source machine learning library developed by Facebook’s AI Research lab (FAIR). It was first introduced in 2016 and has since been distributed on the BSD license as free software. The name, interestingly enough, is a combination of two words you are probably familiar with: Python and Torch.

Python is the software’s user interface, while Torch is one of the first machine learning libraries released in 2002. The use of the name Torch here is more than just a subtle homage: PyTorch shares some of its C++ backend with Torch, thus allowing users to program on it using C/C++. 

Learn more: What’s the Best Language for Machine Learning?

Advantages of using PyTorch

  • One of the key advantages of PyTorch is that it uses Python as the main programming language. Python is undoubtedly the most popular language used for machine learning because of its sheer versatility and ease of use. Being a part of the Python package ecosystem, PyTorch is also fully compatible with popular Python libraries such as SciPy and NumPy.
  • The Python base also makes PyTorch relatively easier to learn, compared to other machine learning frameworks. Its syntax and application closely resemble that of many popular programming languages, like Java and Python. 
  • Adding on to the ease of usage, PyTorch now sports a new hybrid UI that allows you to work in two user modes: eager mode and graph mode. Eager mode is better for R&D projects, whereas graph mode offers great functionality in a C++ runtime environment. 

Drawbacks to using PyTorch

  • PyTorch currently lacks a coherent model serving in production. However, it is worth noting that FAIR has announced that they are working on this.
  • It does not have comprehensive monitoring and visualization interfaces like TensorFlow’s TensorBoard. This complicates handling data in PyTorch. 

Now that we know a fair bit about Facebook’s PyTorch, let’s have a look at the other big player in the game: Google’s TensorFlow. 

Person uses Python, which powers PyTorch, a machine learning library

What is TensorFlow? 

TensorFlow originates from Google’s own machine learning software, which was later refactored and optimized for use in production. As a result, TensorFlow was released to the world as an open-source machine learning library in 2015. TensorFlow’s name is also a conjunction of two keywords: Tensor and flow. A ‘tensor’ is the most basic data structure in TensorFlow. You can perform operations on these tensors by building stateful data ‘flow’ charts (similar to a flowchart) that remind the program of past events. 

TensorFlow has historically been seen as the go-to production-grade library. Being one of the earliest modern machine learning software available, TensorFlow has garnered a huge, diversified user base for itself. While its popularity did decline a little after PyTorch came out in 2016, Google’s 2019 release of TensorFlow 2.0 improved things. The 2.0 update is mainly aimed at making the software more accessible and user-friendly. 

Advantages of using TensorFlow

  • You can get started on your project quicker with TensorFlow because of the heaps of data and pre-trained models it already has: all TensorFlow users have access to this data in Google Collab Notebooks, which is provided by both Google and third parties. 
  • It massively helps production-related machine learning. It has available APIs for JavaScript and Swift that can be used for mobile development and TensorFlow Lite, which lets you compress and optimize models for the Internet of Things devices. 
  • TensorFlow has had a history of being easily deployable on most machines across the spectrum. This makes the software incredibly scalable. Other machine learning platforms like PyTorch have only started open-source serving libraries as recently as 2020. 

Drawbacks to using TensorFlow

  • The update from TensorFlow 1.x to TensorFlow 2.0 changed a lot of features. Users familiar with the previous version might find the current version difficult or confusing.
  • TensorFlow is slower than its competitors. While it can be used for dealing with industrial data sets, it takes more time.
  • It provides less support for Windows users. TensorFlow is primarily designed for Linux systems, and can only be downloaded on Windows using the Anaconda prompt or the pip package. 

 

PyTorch vs TensorFlow: Which should you use?

Short answer? It depends on what you’re trying to do. Both PyTorch and TensorFlow have their pros and cons. It is better to compare the two using certain metrics to understand their strengths and weaknesses. Here are some of the metrics you could use to make a further judgment:

Deployment 

Impressed with your project? You can make the neural network available for other people’s use through deployment! TensorFlow is the clear winner when it comes to this. PyTorch launched its serving-library Torchserve last year, whereas TensorFlow has been offering services like TensorLite and TensorFlow.js for years.

Domain 

PyTorch’s overall functionality, ease of use, and features make it ideal for researchers and students. On the other hand, TensorFlow is incredibly scalable and easily deployable and is, therefore, a favorite for production. 

Parallelism 

PyTorch allows you to enable training across multiple GPUs with just a single line of code. While this can also be implemented in TensorFlow, you will have to write a lengthier program. 

Debugging 

Easy debugging is another factor that makes PyTorch the perfect platform for new deep neural networks users. It can be debugged using Python’s regular debuggers that most users are already familiar with—PyCharm Debugger and pdb. For TensorFlow, however, the user must learn the library’s debugger. 

Key takeaways and next steps

When it comes to determining who wins in the battle of PyTorch vs TensorFlow, well, we’re sorry to be the bearer of bad news: they’re both great. PyTorch and TensorFlow are both excellent tools for working with deep neural networks. Developed during the last decade, both tools are significant improvements on the initial machine learning programs launched in the early 2000s.

PyTorch’s functionality and features make it more suitable for research, academic or personal projects. TensorFlow, on the other hand, while a little harder to learn, offers excellent scalability and can be easily deployed on most machines. While TensorFlow does hold the distinction of being an older, more widely used platform, PyTorch is in no way less useful.

For those who want to take their love of data and deep learning to the next level, we recommend taking this free, 5-day introductory course in data analytics.  You could also read more about related topics by reading any of the following articles:

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