PyTorch vs TensorFlow in 2023.

PyTorch vs TensorFlow are currently the two most prominent frameworks used in the field of Deep Learning. The debate about which one is better has been ongoing for a while, with strong supporters on both sides.

The rapid advancements of PyTorch and TensorFlow have made this debate more complex, often muddled by outdated or incomplete information. This makes it challenging to definitively say which framework is better suited for specific tasks.

Although TensorFlow was traditionally favored for industrial applications and PyTorch for research purposes, these distinctions are no longer as clear as in 2023. The discussion about which framework is superior has become more nuanced. Let’s take a closer look at these nuances and differences.

Practical consideration 

Practical Matters to Consider

PyTorch and TensorFlow have their distinct paths of development and complex decisions behind their designs. In the past, comparing these two has involved diving into technical details and predicting their future features. However, both frameworks have evolved significantly over time, rendering many of these technical differences less relevant.

Fortunately, for those who prefer a clear picture, the current debate between PyTorch and TensorFlow boils down to three practical factors.

Model Availability: 

As the realm of Deep Learning undergoes annual advancements and gives rise to ever-larger models, the feasibility of training state-of-the-art (SOTA) models from scratch diminishes. However, there is a silver lining in the form of publicly available SOTA models. It’s of paramount importance to make the most of these pre-existing models in situations where they align with the task at hand. This approach not only saves time and resources but also capitalizes on the progress achieved by the research community.

Deployment Infrastructure:

Producing well-functioning models becomes insignificant if they cannot be effectively applied. Speeding up the deployment process is crucial, especially given the rising popularity of microservice-oriented business models. The efficiency of deployment can greatly influence the success or failure of businesses centered around Machine Learning. In essence, it’s not just about building great models, but also about getting them into action quickly.

Ecosystems:

Gone are the days when Deep Learning was confined to specific applications in controlled settings. Artificial Intelligence is now revolutionizing various industries, necessitating a framework that seamlessly fits into a broader ecosystem, enabling development across mobile, local, and server-based applications. Furthermore, the emergence of dedicated Machine Learning hardware, like Google’s Edge TPU, underscores the need for practitioners to utilize a framework that harmonizes effectively with such advanced hardware solutions.

We will sequentially examine these three practical factors and subsequently offer our suggestions on the most suitable framework for various domains.

PyTorch vs. TensorFlow – Model Availability:

Developing a successful Deep Learning model from scratch can prove to be quite challenging, particularly in domains like Natural Language Processing (NLP) where fine-tuning and optimization pose significant hurdles. The increasing complexity of state-of-the-art models renders the tasks of training and refining them nearly impracticable, especially for small-scale enterprises. Both startups and researchers are often constrained by limited computational resources, hindering their ability to explore and harness such intricate models independently. Consequently, the accessibility to pre-trained models becomes an invaluable asset, whether for transfer learning, fine-tuning, or immediate inference purposes.

In the realm of model availability, there exists a notable contrast between PyTorch and TensorFlow. While both PyTorch and TensorFlow provide their official model repositories, as elaborated upon in the subsequent sections, practitioners might desire to leverage models from alternative sources. It is pertinent to undertake a quantitative assessment of model accessibility within each framework.

Hugging Face:

HuggingFace serves as a platform that simplifies the process of incorporating advanced machine-learning models into various applications using just a few lines of code.

When we compare the availability of models on HuggingFace for two major machine learning frameworks, PyTorch and TensorFlow, we uncover noteworthy observations. The data is presented visually, showing the total number of models in three categories: those exclusively designed for PyTorch, those exclusively for TensorFlow, and those compatible with both frameworks. The statistics demonstrate a significant prevalence of models exclusively designed for PyTorch, constituting nearly 92% of the entire collection. This marks a notable increase from the previous year’s 85%. In contrast, TensorFlow-exclusive models make up only about 8%, and roughly 14% of the entire model assortment is tailored for TensorFlow, a reduction from the prior year’s 16%. It’s important to highlight that during the year 2022, over 45 thousand models exclusively for PyTorch were introduced, while the count of TensorFlow-exclusive models increased by approximately 4 thousand models.

Shifting our focus to the 30 most popular models available on HuggingFace, we discover intriguing trends. Although all these models can be accessed using PyTorch, none are exclusively designed for TensorFlow. However, the number of models compatible with both frameworks has risen from 19 to 23. This suggests a deliberate effort to expand TensorFlow’s coverage for the most highly sought-after models in the community.

Research paper.

In the world of research, researchers need to have access to the latest models and techniques described in recently published research papers. Imagine you’re a researcher and you want to try out a new model that has been introduced in a paper. Instead of starting from scratch and building the entire model on your own, you’d prefer to have a head start by using the code and resources shared by the authors of the paper. This way, you can focus your efforts on the actual research instead of spending a lot of time setting up the basic structure.

PyTorch is a popular framework that researchers often use to build and experiment with machine learning models. It’s like a toolkit that makes it easier to create and train complex models. The trend we’ve seen on platforms like HuggingFace, where researchers share their models and code, is also seen across the entire research community. Many research journals have published a lot of papers over the years, and if we look at the types of frameworks mentioned in these papers, we can see a pattern.

The graph they’ve provided shows the percentage of papers that used either PyTorch or TensorFlow (another popular framework) over time. PyTorch’s popularity has grown quickly. In just a few years, it went from being used in only around 7% of papers to being used in nearly 80% of papers that mention either PyTorch or TensorFlow.

One of the reasons behind PyTorch’s rapid growth is that TensorFlow, especially its earlier version called TensorFlow 1, had some difficulties, especially when used in research. This prompted researchers to explore alternatives, and PyTorch emerged as a newer and more attractive option. Even though TensorFlow 2 fixed many issues in 2019, PyTorch’s momentum was strong enough to maintain its position as the preferred framework for research, at least from the perspective of the research community.

If we look at researchers who switched from one framework to another, we can see a trend. Most researchers who used TensorFlow in 2018 shifted to PyTorch in 2019 (about 55% of them). Similarly, a large majority of researchers who used PyTorch in 2018 continued using PyTorch in 2019 (about 85% of them). This is shown in a Sankey diagram, which helps visualize this movement from one framework to another.

It’s important to note that the data they’ve collected is from before TensorFlow 2 was released. However, as they’ll explain in the next section, this fact does not change the situation in the research community.

Modal availability in the world.

The data presented indicates PyTorch’s current prominence within the research landscape. While TensorFlow 2 aimed to enhance its research applicability, PyTorch has solidified its position as the preferred choice for researchers, leaving little incentive to revisit TensorFlow. The challenge of backward compatibility between TensorFlow 1 and 2 further compounds this preference shift.

Presently, PyTorch stands out as the frontrunner in research due to its extensive community adoption, with a majority of publications and available models being developed using the PyTorch framework.

Google Brain’s Approach: 

Google Brain, a pioneer in the field of deep learning, prominently utilizes JAX, a powerful numerical computing library. Additionally, they harness Flax, a neural network library built atop JAX. This strategic utilization of JAX and Flax showcases Google’s commitment to efficient neural network development.

DeepMind’s Evolution: 

DeepMind, known for groundbreaking AI research, initially embraced TensorFlow in 2016. However, their shift to JAX, announced in 2020, highlighted their pursuit of accelerated research. The JAX ecosystem at DeepMind is epitomized by Haiku, a neural network library, revealing its dedication to innovation.

DeepMind’s Contributions: 

DeepMind’s contributions to the AI landscape include Sonnet, an advanced TensorFlow API tailored for research, often dubbed “the research version of Keras.” While its pace of development has slowed, it remains a valuable resource for TensorFlow enthusiasts. Additionally, DeepMind’s Acme framework holds essential significance for practitioners in Reinforcement Learning.

OpenAI’s Preference:

OpenAI, a prominent AI research institute, embraced PyTorch as its internal standard in 2020. While their older baselines repository relies on TensorFlow, it’s worth noting that TensorFlow remains a robust choice for those focused on Reinforcement Learning, particularly due to the high-quality implementation offered by the Baselines project.

The Rise of JAX: 

JAX, a project by Google, has been steadily gaining traction in the research community. With less overhead compared to PyTorch and TensorFlow, JAX offers a unique approach. However, migrating to JAX might not suit everyone due to its distinct underlying philosophy. Nonetheless, its rapid development pace and increasing adoption in various models and papers underscore its enduring presence.

TensorFlow’s Trajectory:

TensorFlow, while facing challenges, continues its journey as a research framework. Its evolution ahead remains a subject of curiosity, with the path to regaining dominance in the field demanding perseverance and innovation.

Round 1 Verdict: 

In the ongoing PyTorch vs. TensorFlow debate, PyTorch emerged victorious in the first round. Its user-friendly nature, coupled with its broad research appeal, positions it favorably. However, the narrative continues to unfold, and the dynamics between these frameworks remain captivating in the realm of AI researchrch

PyTorch vs TensorFlow – Deployment

In the realm of Deep Learning, the ultimate goal is often to employ the most advanced models for achieving exceptional results. However, this aspiration isn’t always practical or feasible within real-world industrial contexts. The mere availability of state-of-the-art (SOTA) models becomes insignificant if the process of translating their capabilities into useful outcomes is cumbersome and error-prone. Therefore, it’s important to look beyond the attractiveness of frameworks based on their access to cutting-edge models and consider the entire end-to-end process of Deep Learning within each framework.

TensorFlow has historically been the preferred framework for applications focused on deploying models in real-world scenarios, and this preference is well-founded. TensorFlow provides a suite of integrated tools that streamline the complete Deep Learning process, ensuring efficiency and user-friendliness. When it comes to deploying models, TensorFlow offers options like TensorFlow Serving and TensorFlow Lite, making it effortless to deploy models across diverse platforms such as cloud environments, servers, mobile devices, and Internet of Things (IoT) devices.

On the other hand, PyTorch, while initially lagging behind in terms of deployment capabilities, has made significant progress in closing this gap in recent years. The introduction of tools like TorchServe and more recently, PyTorch Live, brings native deployment capabilities into the PyTorch ecosystem. These tools offer a more streamlined approach to deploying models. However, the question remains whether these advancements are substantial enough to position PyTorch as a valuable choice for industrial applications. It’s worth exploring and evaluating whether PyTorch’s improvements in deployment make it a feasible and attractive option within the industry landscape.

TensorFlow

TensorFlow provides the advantage of scalable production through the utilization of optimized static graphs designed for enhancing inference performance. When it comes to deploying your model using TensorFlow, your choice between TensorFlow Serving and TensorFlow Lite depends on the specific application requirements at hand.

TensorFlow Serving

TensorFlow Serving is a powerful solution designed for efficiently deploying TensorFlow models on both in-house servers and cloud environments. This tool is an integral part of the TensorFlow Extended (TFX) platform, which covers the complete machine learning pipeline.

By utilizing TensorFlow Serving, you can seamlessly package models into organized directories, each with distinct tags. This enables you to easily select the appropriate model for making inference requests, all while maintaining a consistent server architecture and unchanging APIs.

The platform facilitates the deployment of models on specialized gRPC servers, leveraging Google’s high-performance RPC framework. This framework, known as gRPC, was specifically crafted to connect a wide range of microservices, making it a perfect fit for deploying machine learning models effectively.

TensorFlow Serving’s integration with Google Cloud, especially through Vertex AI, is seamless and streamlined. Additionally, it smoothly aligns with Kubernetes and Docker, enhancing its adaptability and compatibility within various deployment environments.

TensorFlow Lite

TensorFlow Lite (TFLite) serves as the ideal solution for deploying TensorFlow models on mobile, IoT, and embedded devices. TFLite stands out by compressing and enhancing models for these platforms, effectively addressing five pivotal factors for on-device AI: latency, connectivity, privacy, size, and power efficiency. Notably, it offers a unified pipeline that effortlessly exports both traditional Keras-based SavedModels (employed with Serving) and TFLite models, allowing for easy model quality comparison.

TFLite boasts compatibility across Android and iOS platforms, extending its capabilities to microcontrollers (using ARM with Bazel or CMake) and embedded Linux systems like Coral devices. With TensorFlow’s versatile APIs spanning Python, Java, C++, JavaScript, and Swift (up until this year), developers enjoy a rich array of programming languages at their disposal.

PyTorch

PyTorch has dedicated efforts to enhance deployment convenience, an area where it once fell short. Previously, users had to resort to frameworks like Flask or Django to construct a REST API for their models. However, the scenario has evolved, as PyTorch now offers built-in deployment choices such as TorchServe and PyTorch Live, streamlining the process.

TorchServe

TorchServe stands as an open-source deployment framework that emerged from a collaborative effort between AWS and Facebook, now known as Meta. Unveiled in 2020, this framework boasts a range of capabilities including endpoint specification, model archiving, and metric monitoring. Despite its robust features, TorchServe predates its TensorFlow counterpart. This platform provides support for both REST and gRPC APIs, offering a versatile solution for deployment needs.

PyTorch Live

In 2019, PyTorch introduced PyTorch Mobile, aiming to facilitate a seamless process for deploying efficient machine learning models on Android, iOS, and Linux platforms.

Later, in late 2022, PyTorch Live emerged as an extension of the PyTorch Mobile initiative. This innovative tool leverages JavaScript and React Native to enable the creation of cross-platform AI-powered applications for iOS and Android. Notably, the core of on-device inference still relies on PyTorch Mobile. PyTorch Live offers a range of exemplary projects for a quick start, and it has future plans to integrate support for audio and video input capabilities.

Deployment – Final Words

Presently, TensorFlow continues to lead in the realm of deployment, with its Serving and TFLite offerings demonstrating greater resilience compared to their PyTorch counterparts. Particularly, the integration of TFLite with Google’s Coral devices holds significant appeal across diverse industries. In contrast, PyTorch Live focuses exclusively on mobile applications, and TorchServe is still in its early stages of development. The equilibrium is more apparent for scenarios where models are executed in the cloud rather than on edge devices. The evolution of the deployment landscape holds promise for intriguing changes in the upcoming years. However, at this juncture, TensorFlow takes the lead once again in Round 2 of the PyTorch vs. TensorFlow discourse.

A noteworthy consideration concerning model availability and deployment pertains to users aiming to leverage TensorFlow’s deployment framework while accessing models exclusive to PyTorch. Exploring the use of ONNX to seamlessly transition models from PyTorch to TensorFlow emerges as a valuable strategy.

PyTorch vs TensorFlow – Ecosystems

The differentiating factor between PyTorch and TensorFlow in 2023 lies in their respective ecosystems. While both frameworks excel in modeling, their technical distinctions have taken a backseat to the surrounding ecosystems. These ecosystems encompass vital deployment tools, management solutions, and support for distributed training. Let’s delve into the unique features of each framework’s ecosystem.

PyTorch

Hub

In addition to platforms like HuggingFace, we also have the official PyTorch Hub. This platform serves as a research-centric space for sharing repositories containing pre-trained models. PyTorch Hub boasts an extensive selection of models, spanning various domains such as Audio, Vision, and NLP. Moreover, it houses generative models, including a GAN designed to produce top-notch images of well-known personalities’ faces.

PyTorch-XLA

If you’re looking to train PyTorch models using Google’s Cloud TPUs, PyTorch-XLA is the ideal solution. PyTorch-XLA serves as a Python package that facilitates this connection by utilizing the XLA compiler. Feel free to explore the GitHub repository of PyTorch-XLA for more information.

TorchVision: 

TorchVision stands as the official Computer Vision library within the PyTorch ecosystem. This robust toolkit encompasses an all-encompassing suite of resources meticulously designed to elevate your Computer Vision endeavors. From cutting-edge model architectures to a curated collection of widely used datasets, TorchVision provides the essential building blocks for successful Computer Vision projects. For an expanded repertoire of vision models, the exploration of TIMM (pyTorch IMage Models) is highly recommended. To embark on this journey of visual discovery, the TorchVision GitHub repository is your gateway.

TorchText: 

For those who navigate the realm of Natural Language Processing (NLP), TorchText serves as an indispensable companion. This repository boasts an array of frequently encountered datasets in the NLP domain, coupled with versatile data processing utilities to seamlessly manipulate these datasets and others. If the realm of translation and summarization beckons, delving into fairseq opens up opportunities to excel in these text-centric tasks. To embark on this textual journey, the TorchText GitHub repository awaits your exploration.

TorchAudio: Illuminating the World of Sound

Before embarking on text-based adventures, the extraction of valuable insights from audio files often becomes a preliminary step, especially through Automatic Speech Recognition (ASR). Enter TorchAudio, PyTorch’s official audio library, equipped with an array of distinguished audio models like DeepSpeech and Wav2Vec. This invaluable resource not only offers these advanced models but also guides you through immersive walkthroughs and expertly crafted pipelines tailored to ASR and related pursuits. To dive into the auditory realm, navigate to the TorchAudio GitHub repository and unlock a world of sonic possibilities.

Discover SpeechBrain: 

Uncover the power of SpeechBrain, an innovative open-source speech toolkit designed for PyTorch enthusiasts. Whether you’re into Automatic Speech Recognition (ASR), speaker-related tasks, or diarization, SpeechBrain has you covered. For hassle-free solutions, explore AssemblyAI’s Speech-to-Text API.

 Elevate Your Speech Processing with ESPnet: 

 ESPnet, a dynamic toolkit fusing PyTorch and Kaldi’s data processing approach, empowers you to excel in end-to-end speech tasks. Seamlessly deploy speech recognition, translation, dimerization, and more using ESPnet’s versatile capabilities.

AllenNLP:

Description 3: Amplify your NLP endeavors with AllenNLP, an exceptional open-source research library supported by the Allen Institute for AI and built upon PyTorch. Access cutting-edge tools to enhance your natural language processing projects.

 Unveiling PyTorch’s Ecosystem Tools: 

Dive into PyTorch’s rich Tools page offers an array of specialized libraries for various domains like Computer Vision and Natural Language Processing. Experience the popular fast.ai library for creating modern neural networks.

TorchElastic: Revolutionize Distributed Training with Dynamic Flexibility.

Introducing TorchElastic, a groundbreaking tool born from the collaboration of AWS and Facebook. This game-changing solution facilitates distributed training by seamlessly managing worker processes and handling restart scenarios. TorchElastic integrates flawlessly with Kubernetes and has seamlessly merged into PyTorch 1.9+ to safeguard training progress during dynamic cluster changes.

TorchX: Accelerate ML Application Development with Ease

TorchX stands as a cutting-edge SDK, designed to expedite the creation and deployment of Machine Learning applications. This powerful toolkit includes the Training Session Manager API, a game-changer that effortlessly launches distributed PyTorch applications onto supported schedulers. Its prowess lies in seamlessly initiating distributed jobs and extending support to TorchElastic-managed local jobs for a truly unified experience.

Lightning: Revolutionizing PyTorch with Simplicity

Often dubbed as the Keras equivalent for PyTorch, PyTorch Lightning has evolved remarkably since its inception in 2019. Serving as a vital asset, Lightning streamlines the intricate realms of model engineering and training in PyTorch. What sets Lightning apart is its object-oriented approach, ingeniously crafting reusable and shareable components. This ingenious strategy fosters cross-project utilization, ushering in a new era of efficiency. Delve into this illuminating tutorial to unravel the nuanced workflow of Lightning and grasp its distinctions from traditional PyTorch methodologies.

TensorFlow

Hub

TensorFlow Hub emerges as a treasure trove of pre-trained Machine Learning models primed for easy fine-tuning. This facilitates the swift utilization of models like BERT through minimal code. With a versatile array of models spanning TensorFlow, TensorFlow Lite, and TensorFlow.js, TensorFlow Hub caters to diverse domains such as images, videos, audio, and text challenges. Dive into our step-by-step tutorial or explore our model repertoire for a seamless start.

Empowering Inquisitive Minds with Model Garden

For those seeking deeper involvement, the TensorFlow Model Garden stands as an invaluable repository, offering access to the source code of state-of-the-art (SOTA) models. This avenue is ideal for those desiring to unveil the mechanisms at play or customize models to meet specific requirements. Serialized pre-trained models have limitations beyond transfer learning, but the Model Garden opens doors for comprehensive understanding and modification.

A Glimpse into Model Garden’s Diversity

The Model Garden encompasses three distinct categories: official models, nurtured by Google; research models, meticulously curated by scholars; and community models, fostered collaboratively. TensorFlow’s grand vision harmonizes pre-trained versions from the Model Garden onto TensorFlow Hub, accompanied by accessible source code within the Model Garden.

Seamless Model Deployment through TensorFlow Extended (TFX)

TensorFlow Extended (TFX) unfolds as an end-to-end ecosystem tailored for the deployment of models. It encompasses a spectrum of tasks, from data loading, validation, and analysis to model training, evaluation, and deployment using versatile options like Serving or Lite. TFX pairs harmoniously with Jupyter, Colab, Apache Airflow/Beam, or Kubernetes for orchestration. Seamless integration with Google Cloud, coupled with compatibility with Vertex AI Pipelines, enhances the TFX experience.

 Vertex AI: Google Cloud’s Unified Machine Learning Platform

Discover Vertex AI, Google Cloud’s groundbreaking unified Machine Learning platform. Launched this year, Vertex AI brings together the power of GCP, AI Platform, and AutoML into a seamless and integrated experience. Revolutionizing the landscape, Vertex AI empowers you to streamline, automate, and govern Machine Learning operations effortlessly by orchestrating workflows in a serverless fashion. With the ability to store workflow artifacts, it becomes effortless to track dependencies, model training data, hyperparameters, and source code.

Unlocking Possibilities with MediaPipe: Your Multimodal Machine Learning Framework

MediaPipe emerges as a game-changing framework for constructing versatile, cross-platform applied Machine Learning pipelines. Seamlessly tackle challenges like face detection, multi-hand tracking, and object detection with ease. This open-source project comes with bindings in Python, C++, and JavaScript, catering to diverse programming preferences. Dive into MediaPipe’s readily available solutions and quick-start guides to embark on your Machine Learning journey.

Empowering Local AI with Coral: Your Comprehensive AI Toolkit

Amidst the rise of cloud-based AI solutions, the demand for localized AI is growing across industries. Enter Google Coral, a comprehensive toolkit designed to meet this demand head-on. Unveiled in 2020, Coral is a holistic solution for crafting AI-powered products with a local focus. Overcoming challenges highlighted in the TFLite Deployment section, including privacy and efficiency concerns, Coral offers a range of hardware products tailored for prototyping, production, and sensing. These innovative offerings, akin to specialized Raspberry Pis optimized for AI applications, leverage Edge TPUs to deliver exceptional performance on resource-efficient devices.

Embarking on your local AI journey with Coral is made even more accessible through pre-compiled models encompassing image segmentation, pose estimation, speech recognition, and beyond. These models serve as a solid foundation for developers aiming to construct personalized local AI systems. From inception to implementation, Coral guides you through essential steps with an intuitive flowchart, making model creation a seamless and empowering experience.

FAQs about PyTorch vs. TensorFlow in 2023:

Conclusion.

In conclusion, the PyTorch vs. TensorFlow debate in 2023 reveals a dynamic landscape where both frameworks have evolved and adapted to the changing needs of the AI and deep learning community. PyTorch shines in terms of model availability and research adoption, owing to its user-friendly interface and rapid adoption by researchers. Its emphasis on flexible architecture has made it a favorite for experimentation and model creation.

On the other hand, TensorFlow remains a leader in deployment capabilities, offering tools like TensorFlow Serving and TensorFlow Lite that cater to various platforms and devices. Its well-established ecosystem, including TensorFlow Hub, TensorFlow Model Garden, and TensorFlow Extended (TFX), provides a comprehensive suite of resources for different stages of the machine learning pipeline.

Ultimately, the choice between PyTorch and TensorFlow depends on the specific priorities of practitioners. Researchers seeking a flexible and easy-to-use framework may lean toward PyTorch, while those focused on efficient deployment and a robust ecosystem might prefer TensorFlow. As AI technology advances, both frameworks will likely continue to evolve, making it an exciting time for the field of deep learning.