The PyTorch vs. TensorFlow debate has been a hot topic among deep learning engineers. Making the right choice for your project can be the difference between success and failure. But with so much personal preference and bias involved, how can you make an informed decision?
We've researched and tested both tools, and we're ready to tackle the question: "Is PyTorch better than TensorFlow?" Join us as we delve into the key factors that separate these two powerful deep learning frameworks.
In this article, we’ll cover:
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PyTorch is an open-source deep learning framework designed by Meta AI, with the AI community’s contributions. PyTorch was designed in September 2016, currently on its second version, PyTorch 2. It natively supports CPU and GPU while also running on TPU with some extensions.
In September 2022, Meta announced moving PyTorch to the PyTorch Foundation, a part of the non-profit Linux Foundation—a technology consortium whose core mission is the collaborative development of open-source software.
PyTorch is based on Torch, a scientific computing framework written in C and CUDA (GPU programming based on C). However, its clean and simple Python interface made PyTorch one of the most popular deep learning frameworks.
The key PyTorch advantages are as follows:
PyTorch can be used to build deep learning models for various deep learning applications ranging from computer vision, natural language processing, and speech recognition to generative AI applications.
TensorFlow is an open-source library for large-scale deep learning. TensorFlow was developed by the Google Brain team in 2015. In 2019, TensorFlow 2 was released, offering a simpler and cleaner API than TF1.
Some TensorFlow advantages are as follows:
A key player in the TensorFlow ecosystem is Keras. Keras is a high-level API built on top of TensorFlow that makes building neural networks easy for people without a strong deep learning background. Out of the top winning teams in Kaggle, most use Keras—on account of its rapid experimentation capabilities.
Both Keras and TensorFlow 2 can be used to build deep learning models for various use cases ranging from computer vision and natural language processing to speech recognition and generative AI.
The PyTorch vs. TensorFlow debate has often been framed as TensorFlow being better for production and PyTorch for research.
However, both frameworks keep revolving, and in 2023 the answer is not that straightforward. Let’s take a look at this argument from different perspectives.
Designing fancy state-of-the-art deep learning models from scratch is very time-consuming, not to mention extremely difficult.
In both practice and research, you’ll need to use and modify already implemented models. Therefore, you’ll need easy access to pre-trained neural networks ready for “consumption” or to source code for a state-of-the-art deep learning model in your preferred framework. Let’s explore the options.
To start with, TensorFlow and PyTorch offer access to an extensive collection of pre-trained models, reposited in TensorFlow Hub and PyTorch Hub, respectively. These are repositories of trained models ready for inference, fine-tuning, and deployment. With just a couple of imports and a few lines of code, you can use a pre-trained Mask RCNN for image segmentation and choose your preferred backbone model.
As of now, TensorFlow Hub offers up to 1300 different models spanning four domains—computer vision (image and video), text, and audio. The vast majority are for computer vision.
In contrast, PyTorch Hub hosts a mere 49 models. In terms of pre-trained model collections, TensorFlow has the upper hand.
Open-source implementations of research papers play a crucial role in the work of researchers and R&D departments, as they need to be able to replicate or build upon previous studies.
It's evident that PyTorch is the preferred choice of the majority of researchers. Data from Paperswithcode shows that 68% of all published papers utilize the framework. Only about 30% of papers have at least one code implementation on GitHub repositories, and it's reasonable to assume that this distribution is consistent across frameworks.
Models, such as the famous ChatGPT, GPT3, and Google’s new chatbot Bard, are all built using the Transformer architecture currently dominating the deep learning space. Training Transformers from scratch is challenging due to their vast size and data requirements. However, HuggingFace enables us to easily train and fine-tune large models with just a few lines of code.
A quick look at the site’s available models reveals the below statistics.
It's apparent that PyTorch is the leading player in the Transformer arena on HuggingFace. Consider that 64% of all available TensorFlow and Keras models are already available for PyTorch.
It proves PyTorch's dominance in the field and its ability to future-proof itself with a vast selection of transformer models. This makes a significant difference when you wish to fine-tune and train a model with custom loops; otherwise, the HuggingFace API handles most of the hardships.
Although TensorFlow has been widely recognized as the industry standard, offering an easy way to transition models from the development stage to deployment, PyTorch has made some recent strides with a new tool introduction. Let’s take a look.
Created by Google, it’s one of the first serving tools to exist. It’s a flexible, high-performance serving system for machine learning models designed for production environments. It consumes a TensorFlow SavedModel and accepts inference requests over either REST or gRPC interfaces. It can serve multiple models or multiple versions of the same model simultaneously. Unlike TorchServe, it can serve models without Python handlers.
TensorFlow Lite is a widely recognized tool for those looking to use models in edge devices, such as microcomputers, microcontrollers, or cell phones. A converted module optimizes models via pruning and quantization and stores them in a special format, tflite. Then, the TensorFlow Lite Interpreter, installed in an edge device, can run the model in inference mode and provide predictions with low latency and without any connectivity requirements.
It’s a very straightforward tool with excellent documentation and tutorials. Its Analyzer API helps you analyze models in TensorFlow Lite format by listing their structure.
TorchServe provides an easy tool for packaging and versioning models. It supports both pre-made handlers and custom Python handlers. It can serve multiple models simultaneously and is highly scalable. For those who wish to communicate via REST APIs, TorchServe offers a much faster and more reliable method compared to TensorFlow Serving. This post presents a quantitative comparison of the REST interface.
This is the PyTorch equivalent to TF Lite. It is certainly a very promising tool for deploying PyTorch models at the edge. However, the library is still immature (beta stage), and not as complete as its TensorFlow counterpart, especially in the embedded devices field. Porting torch models to TensorFlow via ONNX and using TF Lite may still be a better alternative. However, with time, PyTorch Mobile may grow stronger.
TensorFlow used to be the undisputed king when it came to deployment, but PyTorch is slowly but surely catching up. However, TensorFlow still wins this round.
This issue used to be a huge part of any PyTorch vs. TensorFlow discussion. However, it must be noted that TF natively supports dynamic graphs after TensorFlow 2.0 release, just like PyTorch.
The dynamic graph approach made PyTorch popular in the earlier days. Since it became a selling point for many deep learning specialists, TensorFlow adopted this approach in its second version. Let’s quickly explain the difference between the dynamic vs. static graph approach.
Deep learning frameworks view models as directed acyclic graphs (DAGs). However, how you define them has an impact on the way you interact with them.
The difference between the two strategies is similar to that between an interpreter and a compiler. A static graph must first be defined before it runs. This offers a lot of opportunities for memory and speed optimizations. On the other hand, in a dynamic graph, you can define, change and execute nodes as you go. This provides a lot more flexibility and ease of use.
Since Python is an interpreted language, most deep learning practitioners find the dynamic graph approach more natural. Undoubtedly, it is easier to use and provides much more freedom while experimenting.
TensorFlow natively supports Tensorboard—its powerful toolkit for tracking experiments and visualizing metrics, layer weights, or other tensors as they change in time.
It’s important to note that you can use Tensorboard with PyTorch. However, Tensorboard’s integration with Keras is much smoother, as you only need to pass a callback function during fitting.
Even though the visualization argument often comes up in the PyTorch vs. TensorFlow debate, it shouldn’t be a deciding point with so many other framework-agnostic visualization tools available.
The rise of machine learning can be attributed partly to the abundance of open-source packages and libraries available. Having strong community support for a deep learning framework can be a significant advantage—even if the core framework has limitations, its ecosystem may offer robust solutions.
Let’s dive into the comparison of PyTorch and TensorFlow in terms of their ecosystems.
PyTorch has many libraries of its own, but the community has also created a largely successful ecosystem.
Here’s a non-exclusive list of links to the most important packages in the TensorFlow ecosystem. The first 4 are considered part of the main package, while the rest are GitHub repositories built by the community.
PyTorch has a very rich ecosystem built by its community. Many useful packages have been developed for most machine learning-related topics. A GitHub search might help you discover even more!
TensorFlow has expanded to encompass many machine learning solutions that revolve around the central deep learning framework. As a result, TensorFlow has a very strong native ecosystem supported by the Google team, along with the community’s support.
Here’s a list of the most important packages in the TensorFlow ecosystem.
The verdict is a tough one. I would argue that TensorFlow has a more industry-oriented ecosystem, catering to production teams. PyTorch focuses on research and modeling but may come short in production-related areas. Ultimately, the choice comes down to personal interests and project goals.
Both deep learning frameworks are equally adept at confronting any deep learning task. The most common use cases for these frameworks are computer vision and NLP, but other use cases, such as recommender systems, are also important. Some examples include:
TensorFlow object detection API offers a lot of flexibility, while Model Maker API offers rapid experimentation. Detectron 2 and TIMM are also excellent choices for this category of computer vision tasks, if you prefer PyTorch.
Segmentation Models, build with PyTorch, is a great way to build your own image segmentation models. TensorFlow’s Model Maker and Model Hub might also offer solid alternatives.
TensorFlow has a package committed for recommender systems. There is also a PyTorch alternative.
PyTorch is a better-suited choice for multimodal learning, a trending deep learning field. Torch has a special library to accelerate research in multimodal learning, TorchMultimodal, currently in beta mode.
To sum the debate up, let’s go through a list of arguments that should help you decide which framework you should use for your project.
Pros of PyTorch:
Cons of PyTorch:
Pros of TensorFlow:
Cons of TensorFlow:
Starting with Keras is the most suitable option. It can save you from dealing with many deep learning difficulties. However, if you want to dig deeper (which you’ll have to do at some point), pick PyTorch.
PyTorch is probably a better choice—unless you work in a niche field where adoption may vary!
If you deploy models in production environments, TensorFlow and its TFX-TFLite ecosystem offer a more comprehensive solution and are more widely recognized in the industry.
However, if you're open to having a second ecosystem in your tech stack, you can still use PyTorch for modeling and convert it to TensorFlow using ONNX.
Stick to the framework you know best. They are both still very robust. Complement its weaknesses with the other framework if possible.
PyTorch has won the deep learning space. However, TensorFlow is shifting its focus towards becoming a comprehensive machine learning toolkit oriented towards end-to-end deep learning solutions. TensorFlow is positioning itself in the machine learning operations space.
Adaptation is key to surviving in what is probably the fastest-moving industry at the moment. Don’t get too fixated on a single framework or tool; it's important to be proficient in multiple technologies and understand their pros and cons.
Not all problems are nails, so not every tool should be a hammer!
Understand which tool brings the fastest and most cost-effective solution and work with it. The ability to rapidly learn and utilize new tools is crucial for anyone seeking to thrive in AI.