PyTorch vs TensorFlow: Which is Better for Deep Learning?

Introduction

With the advent of Machine Learning (ML) and Artificial Intelligence (AI) across various sectors, the need for efficient ML models and frameworks has become paramount. Among the numerous frameworks available, PyTorch and TensorFlow have emerged as the most prominent and widely used. Despite their similarities in features, integrations, and language support, each framework has unique strengths and weaknesses. This article provides a comprehensive comparison of PyTorch and TensorFlow, focusing on their features, integrations, and usage to help you decide which is better suited for your deep learning projects.

Table of Contents

1. What’s a Machine Learning Framework?
2. Key Features of Machine Learning Frameworks
3. PyTorch
4. TensorFlow
5. Variants and Integrations
6. Language Support
7. Integrations and Ecosystem
8. Additional Considerations
9. PyTorch vs TensorFlow: Model Availability
10. Conclusion

What’s a Machine Learning Framework?

Machine learning frameworks are interfaces that contain a set of pre-built functions and structures designed to simplify many of the complexities of the machine learning lifecycle, which includes data preprocessing, model building, and optimization. Almost all businesses today use machine learning in some way, from the banking sector to health insurance providers and from marketing teams to healthcare organizations.

Key Features of Machine Learning Frameworks

• Ease of Use: High-level APIs simplify the development process.
• Pre-built Components: Ready-to-use layers, loss functions, optimizers, and other components.
• Visualization: Tools for visualizing data and model performance.
• Hardware Acceleration: GPU and TPU acceleration to speed up calculations.
• Scalability: Ability to handle massive datasets and distributed computing.

PyTorch

PyTorch is an open-source machine learning framework developed by Facebook’s AI Research lab. Its dynamic computation graph makes it flexible and easy to use during model development and debugging.

Key Features of PyTorch

• Dynamic Computation Graph: Also known as "define-by-run," it allows the graph to be built on the fly, making it easily modifiable during runtime.
• Tensors and Autograd: Supports n-dimensional arrays (tensors) with automatic differentiation (using AutoGrad) for gradient calculation.
• Extensive Library: Includes numerous pre-built layers, loss functions, and optimizers.
• Interoperability: Easily integrated with other Python libraries like NumPy, SciPy, and more.
• Community and Ecosystem: Strong community support with various extensions and tools.

TensorFlow

TensorFlow, developed by the Google Brain team, is an open-source machine learning framework that is highly adaptive and scalable. It extends support to various platforms, from mobile devices to distributed computing clusters.

Key Features of TensorFlow

• TensorFlow Computation: Initially used a static computation graph, now supports eager execution by default with TensorFlow 2.x, allowing for more intuitive debugging.
• TensorFlow Extended (TFX): A platform for deploying production ML pipelines.
• TensorFlow Lite: Optimized for mobile and embedded devices.
• TensorBoard: Provides visualization tools to track ML workflows.

Variants and Integrations

PyTorch

• LibTorch: Provides C++ API for performance-critical applications.
• TorchScript: Transforms models built using PyTorch into a format that does not depend on Python, enabling easy deployment.
• PyTorch Lightning: A high-level API for AI researchers, simplifying model training and experimentation.

TensorFlow

• TensorFlow Lite: Optimized for mobile and embedded devices.
• TensorFlow.js: Enables development and training of models in JavaScript.
• TensorFlow Extended (TFX): A production-ready ML platform for deploying models.
• TensorFlow Hub: Facilitates easy sharing and reuse of pre-trained models.

Language Support

PyTorch

• Primarily supports Python.
• Robust C++ API (LibTorch) for performance-critical applications.
• Community-driven projects and bindings for other languages such as Java, Julia, and Swift.

TensorFlow

• Extensive support for Python.
• Offers APIs for JavaScript (TensorFlow.js), Java, and C++.
• Experimental support for Swift, Go, and R.
• TensorFlow Serving for deployment using RESTful APIs.

Integrations and Ecosystem

PyTorch Integrations

• Hugging Face Transformers: Useful for using pre-trained models from Hugging Face.
• PyTorch Geometric: Extends PyTorch to geometric deep learning and graph neural networks.
• FastAI: Simplifies training neural networks using PyTorch.

TensorFlow Integrations

• Keras: A high-level API for building and training models, integrated closely with TensorFlow.
• TensorFlow Datasets: Consists of many datasets for immediate use.
• TensorFlow Probability: Implements probabilistic reasoning and data analysis.
• TensorFlow Agents: Facilitates reinforcement learning tasks.

Additional Considerations

Community and Support

• PyTorch: Strong presence in research communities, with many academic papers and courses built around it.
• TensorFlow: Robust industrial support, extensive documentation, and numerous production use cases.

Performance

• TensorFlow: Eager execution simplifies debugging but may be slower for complex models compared to its static graph mode.
• PyTorch: Dynamic computation graphs provide flexibility and ease of debugging but may consume more memory and lack optimizations.

Ecosystem and Tools

• TensorFlow: More extensive ecosystem with tools like TFX for end-to-end ML workflows and TensorBoard for visualization.
• PyTorch: Rapidly growing ecosystem with strong community contributions and tools like PyTorch Lightning for streamlined training.

PyTorch vs TensorFlow: Model Availability

Implementing a successful Deep Learning model from scratch can be a very tricky task, especially for applications such as NLP where engineering and optimization are difficult. The growing complexity of SOTA models makes training and tuning simply impractical, approaching impossible, tasks for small-scale enterprises. Startups and researchers alike simply do not have the computational resources to utilize and explore such models on their own, so access to pre-trained models for transfer learning, fine-tuning, or out-of-the-box inference is invaluable.

In the arena of model availability, PyTorch and TensorFlow diverge sharply. Both PyTorch and TensorFlow have their own official model repositories, as we’ll explore below in the Ecosystems section, but practitioners may want to utilize models from other sources. Let’s take a quantitative look at model availability for each framework.

Hugging Face

Hugging Face makes it possible to incorporate trained and tuned SOTA models into your pipelines in just a few lines of code.

When we compare Hugging Face model availability for PyTorch vs. TensorFlow, the results are staggering. Below is a chart of the total number of models available on Hugging Face that are either PyTorch or TensorFlow exclusive, or available for both frameworks. The number of models available for use exclusively in PyTorch vastly outnumbers those for TensorFlow.

• PyTorch Exclusive Models: 180,549
• Both PyTorch and TensorFlow Models: 4,833
• TensorFlow Exclusive Models: 12,809

Almost 93% of models are PyTorch exclusive, only about 7% are TensorFlow exclusive, with about 24% of all models available for PyTorch. This highlights PyTorch's growing dominance in the deep learning community.

Most popular and well-known models like Segment Anything Model (SAM) are available in TensorFlow, PyTorch, and even Keras. It is worth checking if there are open-source resources available before starting a new project and choosing a framework.

Research Papers

For research practitioners especially, having access to models from recently-published papers is critical. Attempting to recreate new models that you want to explore in a different framework wastes valuable time, so being able to clone a repository and immediately start experimenting means that you can focus on the important work.

Given that PyTorch is the de facto research framework, we would expect the trend we observed on Hugging Face to continue into the research community as a whole; and our intuition is correct.

The adoption of PyTorch was extremely rapid and, in just a few years, grew from use in just about 7% to use in almost 85% of papers that use either PyTorch or TensorFlow.

Papers with Code

Lastly, we look at data from Papers with Code - a website whose mission it is to create a free and open resource with Machine Learning papers, code, datasets, etc. We see the steady growth of papers utilizing PyTorch - out of the 3,319 repositories created this quarter, nearly 75% of them are implemented in PyTorch, with just 3% implemented in TensorFlow.

Papers on Pubmed: PyTorch, TensorFlow, and Keras

The plot showing the trends of publications containing keywords PyTorch, TensorFlow, and Keras on PubMed by year suggests several interesting points about the adoption and usage of these frameworks in the healthcare and biomedical research fields:

1. Healthcare Adoption Lag:
   • Slower Uptake: The healthcare sector tends to adopt new technologies more slowly compared to other fields like computer science or tech startups. This lag can be due to the rigorous validation and compliance requirements necessary in healthcare.
   • Regulatory Concerns: Regulatory bodies like the FDA and EMA require extensive validation of new technologies, which can slow down the adoption of newer frameworks like PyTorch.
2. Keras Simplicity and Accessibility:
   • Ease of Use: Keras is known for its simplicity and ease of use, making it accessible to researchers who may not have an extensive background in machine learning. This makes it an attractive option for healthcare researchers who need to implement machine learning models without deep diving into complex coding.
   • High-Level API: Keras provides a high-level API, which abstracts many of the complexities involved in building and training deep learning models. This feature can be particularly appealing for biomedical researchers who may prioritize domain knowledge over programming expertise.
3. Historical Context:
   • Early Adoption: TensorFlow was one of the earliest deep learning frameworks to gain widespread adoption, especially in academic and research settings. This early lead helped it establish a strong presence in the healthcare field.
   • Maturity: Keras, being integrated into TensorFlow 2.x, further cements its role as a user-friendly interface for building deep learning models. This integration has helped maintain its popularity among healthcare researchers.
4. PyTorch Growth:
   • Increasing Popularity: While PyTorch has seen rapid adoption in the broader machine learning community, its penetration into healthcare research might be slower. This could be due to the existing reliance on TensorFlow and Keras in the field.
   • Community and Ecosystem: PyTorch's strong community support and growing ecosystem are likely to increase its adoption in healthcare over time, but it may take longer to see this reflected in publication trends.

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Conclusion

The trend observed in PubMed reflects the unique challenges and considerations in the healthcare sector when it comes to adopting new machine learning frameworks. Keras's simplicity and TensorFlow's early lead have helped them maintain a significant presence in healthcare publications. However, as PyTorch continues to grow and its community expands, we can expect its adoption in healthcare research to increase as well.

Model Availability - Final Words

It is obvious from the above data that PyTorch currently dominates the research landscape. While TensorFlow 2 made utilizing TensorFlow for research a lot easier, PyTorch has given researchers no reason to revert to TensorFlow. Furthermore, backward compatibility issues between TensorFlow 1 and TensorFlow 2 exacerbate the challenge of using TensorFlow in research.

For now, PyTorch is the clear winner in the area of research simply because it has been widely adopted by the community, and most publications and available models use PyTorch.

There are a few notable exceptions:

• Google Brain: Google Brain makes heavy use of JAX and its neural network library, Flax, which is designed for JAX.
• DeepMind: DeepMind standardized the use of TensorFlow in 2016 but announced in 2020 that they were using JAX to accelerate their research. They also have an ecosystem built around JAX, including Haiku, their JAX-based neural network library.
• OpenAI: OpenAI standardized the usage of PyTorch internally in 2020; however, their older baselines repository is implemented in TensorFlow, which provides high-quality implementations of reinforcement learning algorithms.
• JAX: Google's JAX project is gaining popularity in the research community. While it operates with a different underlying philosophy than PyTorch or TensorFlow, its functionally-pure approach and rapid development make it a strong candidate for future adoption.

Deployment Infrastructure

While employing state-of-the-art (SOTA) models for cutting-edge results is the holy grail of deep learning applications from an inference perspective, this ideal is not always practical or even possible to achieve in an industry setting. Access to SOTA models is pointless if there is a laborious, error-prone process of making their intelligence actionable. Therefore, beyond considering which framework affords you access to the best models, it is important to consider the end-to-end deep learning process in each framework.

TensorFlow has been the go-to framework for deployment-oriented applications since its inception, and for good reason. TensorFlow has a litany of associated tools that make the end-to-end deep learning process easy and efficient. For deployment specifically, TensorFlow Serving and TensorFlow Lite allow you to painlessly deploy on clouds, servers, mobile, and IoT devices.

PyTorch used to be extremely lackluster from a deployment perspective, but it has worked on closing this gap in recent years. The introduction of TorchServe and PyTorch Live has afforded much-needed native deployment tools. Let's take a look at the deployment capabilities of each framework:

TensorFlow

• TensorFlow Serving: TensorFlow Serving is used for deploying TensorFlow models on servers, whether in-house or on the cloud, and is part of the TensorFlow Extended (TFX) end-to-end ML platform. It allows for easy model serialization and deployment using gRPC servers.
• TensorFlow Lite: TensorFlow Lite is designed for deploying TensorFlow models on mobile or IoT/embedded devices. It optimizes models for latency, connectivity, privacy, size, and power consumption. TensorFlow Lite supports Android, iOS, and microcontrollers.

PyTorch

• TorchServe: Released in 2020 through a collaboration between AWS and Facebook, TorchServe is an open-source deployment framework. It supports REST and gRPC APIs, model archiving, and endpoint specification.
• PyTorch Live: PyTorch Live builds upon PyTorch Mobile and is designed for creating cross-platform AI-powered apps for iOS and Android using JavaScript and React Native. On-device inference is performed by PyTorch Mobile.

Deployment - Final Words

Currently, TensorFlow still wins on the deployment front. TensorFlow Serving and TensorFlow Lite are more robust than their PyTorch counterparts, and the ability to use TensorFlow Lite with Google’s Coral devices is crucial for many industries. However, PyTorch Live is focused primarily on mobile, and TorchServe is still in its infancy. The playing field is more even for applications where models run in the cloud instead of on edge devices. For now, the edge in deployment goes to TensorFlow.

Ecosystems

The final important consideration that separates PyTorch and TensorFlow is the ecosystems in which they are situated. Both frameworks are capable from a modeling perspective, and their technical differences at this point are less important than the ecosystems surrounding them, which provide tools for easy deployment, management, distributed training, and more. Let’s take a look at each framework’s ecosystem:

PyTorch

• PyTorch Hub: A platform for sharing repositories with pre-trained models, including models for audio, vision, and NLP.
• PyTorch-XLA: Connects PyTorch models to Google Cloud TPUs.
• TorchVision: PyTorch's official computer vision library.
• TorchText: PyTorch's NLP library.
• TorchAudio: PyTorch's audio processing library.
• SpeechBrain: An open-source speech toolkit for PyTorch.
• ESPnet: A toolkit for end-to-end speech processing.
• AllenNLP: An NLP research library built on PyTorch.
• TorchElastic: Manages distributed training with dynamic clusters.
• TorchX: An SDK for building and deploying ML applications.
• PyTorch Lightning: Simplifies model engineering and training processes.

TensorFlow

• TensorFlow Hub: A repository of trained ML models ready for fine-tuning.
• Model Garden: Source code for SOTA models.
• TensorFlow Extended (TFX): An end-to-end platform for model deployment.
• Vertex AI: Google Cloud’s unified ML platform.
• MediaPipe: Builds multimodal, cross-platform applied ML pipelines.
• Coral: A toolkit for building products with local AI using Edge TPUs.
• TensorFlow.js: A JavaScript library for ML in the browser and server-side with Node.js.
• TensorFlow Cloud: Bridges local environment with Google Cloud.
• Colab: A cloud-based notebook environment similar to Jupyter.
• Playground: An educational tool for understanding neural networks.
• Datasets: A resource for accessing datasets released by Google Research.

Ecosystems - Final Words

This round is closely contested. Google has invested heavily in ensuring that there is an available product in each relevant area of an end-to-end deep learning workflow. TensorFlow's close integration with Google Cloud and TFX makes the development process efficient and organized, and the ease of porting models to Google Coral devices is a significant advantage for certain industries. However, PyTorch’s ecosystem is rapidly growing, and tools like PyTorch Lightning are making it easier to use in various applications. For now, PyTorch edges out TensorFlow due to its increasing adoption and strong community support.

Conclusion

Both PyTorch and TensorFlow have distinct advantages, making them suitable for various use cases. PyTorch's dynamic computation graph and user-friendly interface have made it a favorite in the research community. Additionally, PyTorch Lightning is highly regarded and often considered the secret ingredient in successful deep learning projects. On the other hand, TensorFlow's scalability and production-readiness make it ideal for industrial applications. However, the transition from TensorFlow 1 to TensorFlow 2 introduced several bugs and instabilities, leading to skepticism within the research and industry communities.

Despite these challenges, TensorFlow remains a powerful tool worth considering for upcoming projects. It's essential not to dismiss TensorFlow entirely, as it may be the right choice for your next endeavor. Currently, AICU prefers PyTorch for its projects. Understanding the key features, integrations, and community support of each framework is crucial in selecting the best tool for your deep learning needs.

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