PyTorch vs TensorFlow – Which deep learning framework should you use?

PyTorch and TensorFlow are two popular machine learning libraries used for advanced data analytics and prediction. Facebook’s Artificial Intelligence Research (FAIR) developed PyTorch lab. Google’s Deep Brain team developed TensorFlow. We use both frameworks for deep learning.

In this blog, we will educate you about the origins of PyTorch and TensorFlow and discuss the use cases for each of them.

Deep Learning Concepts:

Highly intelligent computer programs capable of ‘learning’ from data have been around for a couple of decades now. The latest ones use an ingenious technique called deep learning. Deep learning employs an artificial neural network, replicating human brain function, with three or more layers. These neural networks try to learn from enormous sets of data. Now, while a single layer of neural networks can make approximate predictions on its own, the multi-layer structure of the neural network that makes deep learning algorithms very accurate. 

Deep learning is behind a good deal of artificial intelligence (AI) applications and services that do automation and perform analytical tasks without human intervention. These applications are everywhere around you. From something as simple as a weather app on your smartphone to more sophisticated state-of-the-art technologies like self-driving cars.

What is PyTorch?

PyTorch is an open-source machine learning library developed by FAIR labs. It was first introduced in 2016 and is available as free software with BSD license. It derived the name from a combination of two words you are probably familiar with: Python and Torch. Python is PyTorch’s user interface. Torch is one of the first machine learning libraries released back in 2002. The name “Torch” here has a significant reference: PyTorch shares some of its C++ backend with Torch. This helps programmers to program on it using C/C++. 

Advantages of using PyTorch.

One of the key advantages of PyTorch is that it uses Python as the primary programming language. Python is one of the most popular language used for machine learning because of its versatility and ease of use. As part of the Python package ecosystem, PyTorch is also fully compatible with other popular Python libraries as NumPy and SciPy.

As the base is Python, PyTorch is relatively easier to learn compared to other machine learning frameworks. Its programming syntax closely mimics that of many popular programming languages, like Java and Python. 

Besides the ease of usage, PyTorch now sports a new hybrid user interface 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. 

Disadvantages of using PyTorch

PyTorch currently lacked a coherent model serving in production. Torch Serve, the model serving component is in currently under experimentation.

It does not have an extensive monitoring and visualization interfaces like TensorFlow’s TensorBoard. Therefore, data handling is more complex in PyTorch.

What is TensorFlow?

TensorFlow originates from Google’s own machine learning software, which was later refactored and optimized for production. As a result, they released TensorFlow to the world as an open-source machine learning library in 2015. TensorFlow’s name is again a conjunction of two keywords: Tensor and flow. The most basic data structure in TensorFlow is called a “Tensor”. You can perform operations on these tensors by building stateful data ‘flow’ charts (like a flowchart). 

Data science teams see TensorFlow as the go-to production-grade library. Being one of the earliest modern machine learning software available, TensorFlow has garnered a huge user base for itself. Its popularity declined a little after PyTorch came out in 2016. However, Google’s 2019 release of TensorFlow 2.0 significantly changed the play. TensorFlow 2.0 update made 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 that are already built in. All TensorFlow developers have full access to this data in Google Collab Notebooks. Collab Notebook is a collaboration workspace provided by Google and third parties. 

It massively helps production-related machine learning. It has available APIs for JavaScript and Swift that are good for mobile application development. TensorFlow Lite lets you compress and optimize models for the Internet of Things(IoT). 

TensorFlow has had a history of being easily deployable on most machines via its model serving component. This makes the software extremely scalable. PyTorch has started open-source serving libraries experimentally only in 2020.

Disadvantages of using TensorFlow.

Google’s product update from TensorFlow 1.x to TensorFlow 2.0 changed a lot of notable features. Developers familiar with the previous version may difficulty. This isn’t a concern for one’s starting new.

TensorFlow is slower than its competitors. While we can use it for dealing with industrial data sets, it takes more time.

PyTorch vs TensorFlow: Which one to use?

It depends on what you’re wanting to do. Both PyTorch and TensorFlow have their merits and demerits. It is better to compare the two using certain parameters to understand their strengths and weaknesses. Here are some aspects you could use to make a further judgment:

Deployment 

TensorFlow is the clear winner for deployment. PyTorch launched its serving-library Torchserve recently, whereas TensorFlow has been offering services like TensorLite and TensorFlow.js for years.

Domain 

PyTorch’s overall functionality and ease of use make it ideal for researchers and students. TensorFlow is extremely scalable and easily deployable and is, therefore, a favorite for production. 

Parallelism 

PyTorch allows developers to enable training across multiple GPUs with just a single line of code in parallel mode. To implement this in TensorFlow, you need to write a lengthier program. 

Debugging 

Easy debugging is another major factor that makes PyTorch the perfect platform for new deep neural networks developers. We can debug it using Python’s regular debuggers that most users are already familiar, like PyCharm Debugger and PDB. For TensorFlow, however, developers must learn the library’s debugger. 

Footnotes:

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