Pytorch vs Tensorflow: Which Deep Learning Framework to Choose

Deep learning is based on artificial neural networks (ANN) and in order to program them, a reliable framework is needed. PyTorch and TensorFlow are considered the most popular choices among deep learning engineers, and in this article, we compare PyTorch vs TensorFlow head-to-head and explain what makes each framework stand out. 

Deep learning explained

But before jumping to frameworks directly, let’s talk a bit about deep learning and ANNs. Deep learning is a subset of machine learning and is based on artificial neural networks and representation learning. Deep learning is aimed at simulating the behavior of a human brain – in other words, to train a machine to think in a way people do. 

Prime examples of deep learning include facial recognition, autonomous vehicles, virtual assistants, computer vision, and fraud detection. In all these examples, a machine first learns on a set of data and then gradually learns to make choices based on the new data (just like we do, basically). And since the machine training process and the process of programming artificial neural networks are quite complex and cumbersome, data engineers use frameworks for faster development. This is where the frameworks come into play – now let’s talk compare PyTorch versus TensorFlow in detail. 

PyTorch: an overview 

PyTorch is an open-source machine learning framework, originally developed by Meta AI (later renamed as Facebook AI Research). Its first public release was in 2017 and since then, the framework has gained immense popularity among developers and researchers.

PyTorch is based on Python (making it very intuitive and clear) and Torch – an open-source machine learning library. The framework supports more than 200 mathematical operations and has a variety of use cases (more on that below).

As for the key PyTorch features, they are:

• Dynamic graph computation: allows changing the network behavior “on the go”, instead of waiting for all code to be executed;
• Tensor computation: tensors are generic n-dimensional arrays, accelerated by GPUs and are used for arbitrary numeric computation;
• TorchScript: the production environment of PyTorch for transitioning between modes;
• Automatic differentiation: the feature is used for creation and training of neural networks;
• Support of popular libraries and packages: due to the fact that PyTorch is based on Python, it is 100% compatible with such libraries as Numba, NumPy, or SciPy;
• Gradients: the feature is used to optimize model parameters and minimize the loss function;
• Distributed training: PyTorch supports distributed training through the torch.distributed package.

As you can see, PyTorch is designed to help data engineers seamlessly build and train neural networks – but due to its pythonic nature, it also allows quick development, which is perfect for prototyping. With this said, let’s shift to the main PyTorch benefits.

The main benefits of PyTorch

Now, what are the reasons for choosing PyTorch for your deep learning project? In general, this framework is praised for its simplicity and ease of use (thanks, Python!) – but let’s dig a bit deeper:

• Easy debugging: you can debug PyTorch by using one of the most common Python debugging tools;
• Support for dynamic computational graphs: as already mentioned above, dynamic computation facilitates model optimization and speeds up the process of implementing changes;
• Scalability: since PyTorch is supported by major cloud providers, it provides great scalability;
• Easy data parallelism: helps developers distribute computational work among several CPU/GPU cores;
• Active community: due to the framework’s popularity, PyTorch has a very active community that can provide you with needed help;
• Versatility: since PyTorch can integrate various layers and architectures, it’s suitable for both production and R&D.

The main use cases of PyTorch

Though PyTorch is a versatile framework, below we list certain use cases where PyTorch truly shines. Note though that the primary use case for PyTorch is research, since it was initially promoted as a framework for researchers and is equipped with all necessary tools for it. 

• Natural Language Programming: PyTorch offers a PyTorch-NLP library specifically for creating NLP models and for their fast and effective training. 
• Reinforcement learning (RL): PyTorch is equipped with the Pyqlearning library for reinforcement learning projects. Paired with Python Deep Q learning architecture, this library allows developers to effectively design RL models. 
• Image classification: one more use case of PyTorch is image classification, and with the help of this framework, developers successfully create accurate computer vision models. 
• Prototyping: since PyTorch allows fast development, it’s most often used to create prototypes and quickly test them, which is especially useful if you need a short time-to-market.

To sum up, PyTorch is light, swift, and highly versatile. Now it’s time to talk about TensorFlow and see what benefits it can offer against PyTorch.

TensorFlow: an overview

TensorFlow is probably the most popular and well-known framework for deep learning (and not only) projects. It is an open-source framework developed by Google (first public release in 2015), and it was first based on DistBelief – a cloud-source framework used as a pilot product for carrying out unsupervised feature learning and deep learning projects.

But despite being created on the base of DistBelief, TensorFlow has a set of features that set these two frameworks apart. First, TensorFlow has a more portable code because it was designed to work outside Google’s computing infrastructure. Second, TensorFlow is not centered around deep learning in a way DistBelief did and can be efficiently used for other projects. Finally, TensorFlow supports faster configuration and can run against hgh-level APIs.

Getting back to the topic, TensorFlow is supported by Google and is often used for large-scale, advanced applications. As for its key features, they are:

• Support of various platforms, such as CPU, GPU, TPU (tensor processing units), and mobile;
• End-to-end machine learning library that has a rich set of tools, suitable for every stage of the machine learning development process;
• Availability of multiple avenues for model deployment;
• Comes in multiple programming languages, including Python, C++, JavaScript, Java.
• High-level and low-level APIs that you can select, based on the needs and requirements of your project.

If compared to PyTorch, TensorFlow is more complex and more suitable for large-scale projects. But before starting a detailed comparison, let’s look at the main benefits of using TensorFlow.

The main benefits of TensorFlow

Since TensorFlow is considered the main rival of PyTorch, there are obviously valid reasons for such popularity. Developers and researchers select TensorFlow due to the following benefits:

• Support by Google: due to this fact, the framework receives frequent releases, has a very robust support, and is compatible with numerous tools and services;
• Efficient data visualization: TensorFlow has a TensorBoard data visualization tool that allows presenting the data graphically;
• Scalability: since TensorFlow works across multiple platforms, it has a very high level of scalability, which is a big advantage;
• Open-source nature: because TensorFlow is available to anyone who wants to work with it, this factor makes it a highly popular choice among data engineers;
• Compatibility: as stated above, TensorFlow is compatible with various programming languages and that means, developers can work in an environment that is the most comfortable for them;
• Great architectural support: the TensorFlow architecture deploys TPU, which speeds up computation and facilitates model deployment.

The main use cases of TensorFlow

TensorFlow is not focused on neural networks only and thus, there are many areas of its applications. The most notable ones are:

• Image processing: TensorFlow is used for processing and analyzing both images and videos and delivers valuable insights to users.
• Large-scale AI applications: the most notable example is probably the deployment of TensorFlow for the optimization of Google’s RankBrain ML system (more specifically, for improving its information retrieval abilities).
• Generative modeling: TensorFlow is often used for generative modeling and helps in further recognition of certain patterns and/or behaviors. This proves especially useful in fraud detection and prevention.
• Text recognition: in addition to image processing, TensorFlow is also capable of recognizing the text and its sentiment.

The main differences between PyTorch and TensorFlow

As you can see, both frameworks have an impressive set of features and are used in a variety of applications of different sizes. But if everything is that simple, why do data scientists and engineers have a hard time choosing between PyTorch or TensorFlow? Below, we take a look at the main differences between PyTorch and TensorFlow.

Model availability

It takes a lot of time, resources, and finances to create a SOTA (State-of-the-Art) deep learning model from scratch. The issue is especially relevant for startups and small or middle-sized companies that simply do not have the needed resources or need to implement a certain solution quickly. Hence, there is a big number of available pre-trained models that companies can use for the most various purposes. As you might guess, these models are built on both PyTorch and TensorFlow and here is what the numbers tell.

Note: below, we compare the main types of pre-built models and deep learning platforms that use both PyTorch and TensorFlow frameworks.

Readymade models

First, let’s look at the off-the-shelf models, provided by both frameworks. These models are already pre-trained and can be used by a company almost immediately. All you need to do is write several lines of code, perform a few imports, and the model is ready for the use. 

Both TensorFlow and PyTorch store the pre-trained models in their repositories: 

• PyTorch Hub: 49 models 
• TensorFlow Hub: about 1300 various models

As you can see, TensorFlow is a clear winner here, though models by both frameworks are highly efficient.

HuggingFace

HuggingFace is an ML and data science platform that enables users to easily build, train, and deploy machine learning models. It features a big variety of pre-trained models, and a great number of them are either exclusive to PyTorch or Tensorflow or are available in both frameworks. Here are the numbers showing the PyTorch vs TensorFlow popularity:

• PyTorch exclusive models: 92%
• TensorFlow exclusive models: 8%

As well, in 2022, over 45,000 PyTorch exclusive models were added to the HuggingFace in sharp contrast to only 4,000 TensorFlow exclusive models added the same year.

Research papers

Open-source models based on recently published research papers are crucial for researchers and their work. The availability of such models greatly saves time and allows researchers to duplicate the repository and immediately get down to work without the need to build their own model. 

Since PyTorch is basically a research framework, no wonder it is the number one choice when it comes to choosing a framework for research papers. According to the report by Paperswithcode, 68% of published papers use PyTorch and in this regard, TensorFlow remains the secondary option.

Deployment

It’s not enough to build a SOTA model, but it’s also important to swiftly deploy it. Hence, when choosing a deep learning framework, you also need to consider how well it covers the end–to-end deployment process.
TensorFlow is considered to be an industry standard when it comes to transitioning models from their development stage to the deployment. The framework offers several deployment tools that allow easy deployment on various platforms, including cloud, mobile and IoT:

• TensorFlow Serving: one of the first serving tools created, it is a highly flexible and high-performing tool for ML models. Its great feature is the ability of this tool to server either multiple models or multiple versions of a model simultaneously.  
• TensorFlow Lite: a tool for working with models for edge devices, microcontrollers, and mobile devices. Has great documentation and provides an Analyzer API for analyzing models in a TensorFlow Lite format.

Though PyTorch was initially cumbersome in terms of deployment, it improved greatly in recent years and now offers the following deployment options:

• TorchServe: an efficient tool for packaging and versioning models. Same as TensorFlow Serving, can serve multiple models simultaneously and is known to be very scalable.
• PyTorch Mobile: an alternative to the TensorFlow Lite, PyTorch Mobile is used to deploy models at the edge. Note though that the library for this tool is not very mature and TF Lite might be a better option if you need to deploy a model to the mobile platform.

Expert Opinion

When talking about deployment, PyTorch has indeed caught up and even surpassed TensorFlow, in my opinion. I experience zero issues with PyTorch deployment and for me, it’s even easier than TensorFlow deployment because there are no problems with drivers.

Machine Learning Engineer at SoftTeco

Roman Kyrychenko

Dynamic vs static graph

In deep learning, the frameworks view models as directed acyclic graphs, which you can define to be either static or dynamic. With static graphs, it first has to be defined and only then it runs. This approach allows better speed optimization. But with dynamic graphs, you can define nodes on the go, and this offers greater flexibility and more opportunities for experimentation. Hence, the dynamic graph approach is more preferable by the majority of data scientists. The issue is that not all frameworks support it.

PyTorch was initially designed to support the dynamic graph approach and this feature contributed greatly to its popularity. While the first version of TensorFlow supported static graph approach only, the developers considered it and after the TensorFlow 2.0 release, the framework started supporting dynamic graph approach too.

Ecosystems

When choosing between either PyTorch or TensorFlow, it’s important to consider that the framework has a strong ecosystem and community support. In this way, the development process becomes easier and faster since data engineers have access to a variety of tools and can always resolve their issues with the help of the community. Both PyTorch and TensorFlow have strong ecosystems with valuable tools. Let’s review the most popular ones. 

Note: the lists of libraries and tools below feature just several examples. You can find a full list of PyTorch and TensorFlow libraries and tools on the official websites of each framework.

PyTorch offers the following libraries to work with: 

• TorchText: is used for natural language processing;
• TorchVision: to work with computer vision projects;
• TorchServe: for deep learning model serving;
• TorchArrow: for table data preprocessing.

As for TensorFlow, it offers the following tools:

• Keras: a high-level API;
• TensorFlow.js: a library for developing models in JavaScript;
• TF Model Garden: for implementing SOTA research models;
• GNN: a library for building graph neural networks.

Expert Opinion

In general, Keras is one of the biggest advantages of TensorFlow. It is high-level and is simply great for beginners because you don’t have to write too much code. Unlike PyTorch, where you have to write code for basically every stage of learning and every step that you take. No doubt, it’s great for customization, but can be challenging if you are a novice.

Machine Learning Engineer at SoftTeco

Roman Kyrychenko

TensorFlow vs PyTorch: a head-to-head comparison

After reviewing the biggest differences between the two frameworks, let’s put it all together and see the main features of both TensorFlow and PyTorch in the table:

What should you choose: PyTorch or TensorFlow?

When choosing between TensorFlow vs PyTorch, the main thing to consider is the list of requirements for your project and the goals that you want to achieve. But to make your choice a bit easier, let’s look at each framework one more time.

PyTorch pros:

• Easy to use, intuitive;
• Great for research-focused projects;
• Allows quick development, great for prototyping;
• Diverse and can be used in various applications.

PyTorch cons:

• Can be complex for beginners;
• May not be very suitable for large-scale projects.

TensorFlow pros:

• Strong community;
• Rapid experimentation with Keras;
• Good for large-scale projects;
• Robust ecosystem.

TensorFlow cons:

• Fewer models on HugginFace.

As you can see, it’s really hard to choose between PyTorch vs TensorFlow, so your best bet would be to consult a knowledgeable data scientist or an experienced software provider with an ML department. In this way, you will be able to discuss your current business needs, available resources and potential restraints, and will be able to select a framework that would 100% work for your individual project.