PyTorch vs TensorFlow: Choosing the Best Framework for Deep Learning

The rise of e-commerce, digitization of business processes, and social media have led to a sharp increase in data generation in recent years. The availability of data has accelerated the implementation and adoption of deep-learning applications. Frameworks such as TensorFlow and PyTorch have been at the forefront of this change, enabling developers to implement deep-learning applications with ease. However, choosing the right framework for your needs can be challenging, especially for beginners. To help you with the same, let’s discuss the features, advantages, and disadvantages of PyTorch and TensorFlow. We will also explore use cases to help you decide whether to use TensorFlow or PyTorch for your projects.

What are PyTorch and TensorFlow?

PyTorch and TensorFlow are two of the most widely used deep learning frameworks in data science. These frameworks provide tools to build, train, and deploy neural network models for tasks like image recognition and natural language processing.

• PyTorch is a popular deep-learning framework based on the torch framework. Facebook launched it in 2016, and it is now maintained by the PyTorch Foundation. Due to its focus on usability and performance considerations, PyTorch has become a popular tool in the deep-learning research community.
• TensorFlow is also a popular, open-source, end-to-end deep learning framework. Google launched it in 2015, and it is widely used in production environments. It is known for its static computation graph, efficient deployment capabilities, and production-grade features.

To choose between PyTorch and TensorFlow, we need to know how these frameworks compare in terms of different features. Let’s first compare PyTorch and TensorFlow based on their ease of use, flexibility, popularity, and community support.

PyTorch vs TensorFlow: Ease of Use, Flexibility, Popularity, and Community Support

Ease of use, flexibility, popularity among the developer community, and community support are deciding factors when choosing frameworks to develop applications. No one wants to develop software applications using a framework that is hard to learn and has no documentation or community support. Hence, let’s first compare PyTorch and TensorFlow by comparing their ease of use, flexibility, popularity, and community support.

Ease of Use

PyTorch is designed for user-friendliness. Due to its Pythonic syntax, it is easier to learn for a beginner who already knows Python. It also allows us to make changes to the model, experiment, and debug deep learning models quickly, as it uses dynamic computation graphs.

On the other hand, TensorFlow has a steeper learning curve. Due to the verbose syntax, you need to write a lot of code even to build a simple neural network. Instead of TensorFlow, if you use Keras, a high-level API for TensorFlow, you might find it easier to write the code. However, experimenting and debugging is still hard with TensorFlow as it uses static computation graphs.

Flexibility

PyTorch allows us to change the neural network behavior on the go as it uses dynamic computation graphs. Suppose you are working in an environment where you want to start with a simple model and keep adding functionalities to create complex models. In that case, PyTorch is the go-to framework, allowing iterative model architectures.

TensorFlow takes a more structured approach to building neural networks using a static computation graph. With TensorFlow, we must define the entire model architecture before running the code. If we want to make any changes to the neural network, we have to run the whole code from the start. Hence, TensorFlow lacks the flexibility offered by PyTorch.

Popularity

Due to its Pythonic syntax, researchers and academics find it easy to use PyTorch to experiment with and demonstrate deep learning concepts. Because of this, PyTorch has gained much popularity among researchers and academics alike. For instance, if you look at the Papers with Code, a website that keeps track of the creation date of repositories related to research papers on machine learning and deep learning topics, PyTorch’s use in implementing research papers has increased from 51 percent in September 2020 to 59 percent in September 2024. In the same period, the use of TensorFlow for implementing ML and DL research papers decreased from 10 percent to 2 percent.

Alternatively, TensorFlow is more popular among the developer community. TensorFlow’s popularity among developers has also been fueled historically by the unavailability of robust deployment tools for ML models in PyTorch. However, PyTorch has tried to fill this gap with tools like TorchServe, PyTorch Mobile, and PyTorch Live, which might lead to its popularity among developers, too.

Overall, PyTorch has gained popularity recently and is more popular than TensorFlow, which is also reflected in the data on Google search trends.

Until 2021, TensorFlow was searched more than PyTorch on Google. However, PyTorch has taken the lead now and has almost 1.8 times the search volume compared to TensorFlow.

Documentation and Community Support

PyTorch has a large community of researchers and academicians. Along with extensive documentation and an active developer forum, there is also an abundance of third-party learning resources, online courses, and projects that you can use to learn PyTorch concepts or clarify doubts when needed.

TensorFlow has a larger and more established developer community than PyTorch. It also has a lot of learning materials, online courses, books, and projects available that make it easy to learn TensorFlow, even though TensorFlow has a steeper learning curve than PyTorch. Libraries like TensorFlowOnSpark, SparkFlow, and elephas also exemplify the extensive participation from individual developers and organizations alike to stimulate TensorFlow adoption among the developers.

After discussing their ease-of-use, flexibility, popularity, and community support, let’s compare PyTorch and TensorFlow in terms of training time, model availability, deployment infrastructure, and accuracy to gain deeper insights into their capabilities.

PyTorch vs TensorFlow: Comparing Training Time, Model Availability, Deployment Infrastructure, and Accuracy

Training Time

Ideally, the choice of deep-learning framework shouldn’t impact the training time of the model. The training time required to build deep learning models for the same architecture and the same amount of data is important, as less training time will give us more time for debugging and retraining if required. Multiple studies have been conducted in the past to compare the training time for models with the same architecture and data using TensorFlow and PyTorch.

While the exact training time for a model can be different based on training data and system infrastructure, PyTorch is often faster than TensorFlow in a given set of conditions. For instance, the study by Novac et al. found that training time for TensorFlow was significantly higher than PyTorch for the same architecture and amount of data. The study by Simmons et al. also confirms the same.

Model Availability

With the advancements in deep learning, training state-of-the-art models from scratch is not a very popular choice. Instead, ML engineers and data scientists prefer fine-tuning pre-existing models using their own data for a given use case. TensorFlow and PyTorch both have their official model repository named TensorFlow Hub and PyTorch Hub. You can download any model from these repositories and fine-tune them for specific use cases.

With the popularity of model repositories like HuggingFace Models, it also becomes important that we are able to use HuggingFace models in deep learning apps. In this case, PyTorch has a huge advantage over TensorFlow. As of the writing of this article, there were 195,295 HuggingFace models that we can use with PyTorch. On the other hand, we can use only 13,565 HuggingFace models with TensorFlow.

Deployment Infrastructure

Deploying a good model is just as important as building it. A deep-learning framework must provide reliable, error-free tools for deploying models in production environments.

• Traditionally, TensorFlow has led the way with robust deployment options like TensorFlow Serving and TensorFlow Lite, which allow seamless deployment on cloud infrastructure, physical servers, mobile apps, and IoT devices.
• PyTorch, on the other hand, initially lacked built-in deployment tools, requiring developers to rely on frameworks like Flask or Django to serve models. However, the introduction of TorchServe, PyTorch Mobile, and PyTorch Live has significantly improved PyTorch’s deployment capabilities. These tools enable optimized workflows for Android, iOS, and Linux devices.

Despite PyTorch’s advancements, TensorFlow remains the go-to choice for large-scale production deployments due to its mature and versatile ecosystem.

Accuracy and Performance

Accuracy is a critical factor in deep learning models, as it directly impacts their business value. Studies consistently show that TensorFlow models outperform PyTorch models in terms of accuracy, especially in the early stages of training, with significantly lower error rates.

As models are trained on more data over multiple iterations, the accuracy of models in both frameworks improves. However, TensorFlow maintains a slight edge, consistently delivering higher accuracy and fewer errors compared to PyTorch.

Having compared different features of PyTorch and TensorFlow, let’s compare these features in the realm of generative AI.

PyTorch vs TensorFlow for Generative AI

Generative AI (Gen AI) models, such as GANs, transformers, and diffusion models, are transforming industries by enabling the creation of realistic images, text, and more. Choosing the right deep-learning framework for Gen AI depends on flexibility, ease of implementation, and deployment capabilities.

PyTorch is the preferred choice for generative AI research due to its dynamic computation graphs, which allow for real-time adjustments during training. This makes models like GANs (e.g., DCGAN, StyleGAN, CycleGAN) easier to implement and experiment with. Additionally, PyTorch’s strong integration with HuggingFace provides access to a vast library of pre-trained models, including transformers like GPT and DALL-E.

TensorFlow, while less popular for research, offers robust tools for deploying generative AI models in production. TensorFlow Lite and TensorFlow.js enable seamless deployment across mobile devices, browsers, and edge environments. However, PyTorch has recently closed the gap with tools like TorchServe and PyTorch Mobile, making it easier to deploy models at scale.

PyTorch is the clear favorite for generative AI research due to its ecosystem. However, TensorFlow excels in production and large-scale deployment.

Now that we’ve covered various aspects of both PyTorch and TensorFlow let’s discuss which framework you should choose based on your specific needs and use cases.

PyTorch vs TensorFlow: What Should You Use?

Both PyTorch and TensorFlow have matured significantly and provide robust tools for building and deploying deep learning models. Here are some use cases in which you might prefer PyTorch over TensorFlow or vice versa:

• PyTorch has a Pythonic syntax and is easier to learn than TensorFlow. Hence, if you are a beginner starting with deep learning and your focus is on learning and experimentation, choose PyTorch.

• Due to dynamic computation graphs, PyTorch allows changes to deep learning models to be made on the go. Hence, you can use PyTorch if you are looking for rapid prototyping and research. However, TensorFlow should be the go-to choice if you want to implement neural networks with custom features.

• PyTorch seems to be the clear choice for Generative AI applications due to its ease of implementation, availability of pre-trained models, and community-driven innovations.

• Although PyTorch provides deployment tools, TensorFlow is more appropriate for building production-grade deep learning applications. TensorFlow outperforms PyTorch heavily if we consider deploying deep learning models to Edge and Mobile devices. If your focus is on building large-scale deep learning applications with cross-platform compatibility, use TensorFlow.

Conclusion

In conclusion, both PyTorch and TensorFlow are excellent frameworks for implementing most deep-learning applications. The choice between the two often depends on personal preference, team expertise, and the specific requirements of the project. Researchers tend to prefer PyTorch for its adaptability, while developers use TensorFlow for its deployment infrastructure. Ultimately, both frameworks are excellent, and investing time in learning either one will prepare you to tackle complex deep-learning challenges. To get started with PyTorch, you can take this free course on building neural networks with PyTorch. You might also like this course on introduction to deep learning with TensorFlow.