DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Please enter at least three characters to search
Refcards Trend Reports
Events Video Library
Refcards
Trend Reports

Events

View Events Video Library

Zones

Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks

Last call! Secure your stack and shape the future! Help dev teams across the globe navigate their software supply chain security challenges.

Modernize your data layer. Learn how to design cloud-native database architectures to meet the evolving demands of AI and GenAI workloads.

Releasing software shouldn't be stressful or risky. Learn how to leverage progressive delivery techniques to ensure safer deployments.

Avoid machine learning mistakes and boost model performance! Discover key ML patterns, anti-patterns, data strategies, and more.

Related

  • Deep Learning Frameworks Comparison
  • AI Frameworks for Software Engineers: TensorFlow (Part 1)
  • Ethical AI and Responsible Data Science: What Can Developers Do?
  • Unlocking the Power of Explainable AI With 5 Popular Python Frameworks

Trending

  • AWS to Azure Migration: A Cloudy Journey of Challenges and Triumphs
  • Agile and Quality Engineering: A Holistic Perspective
  • How Trustworthy Is Big Data?
  • FIPS 140-3: The Security Standard That Protects Our Federal Data
  1. DZone
  2. Coding
  3. Frameworks
  4. The Battle: TensorFlow vs. Pytorch

The Battle: TensorFlow vs. Pytorch

Which machine learning framework should you use? An answer from 3,000 developers.

By 
Eitan Rosenzvaig user avatar
Eitan Rosenzvaig
·
Feb. 13, 19 · Opinion
Likes (4)
Comment
Save
Tweet
Share
21.1K Views

Join the DZone community and get the full member experience.

Join For Free

who hasn’t heard about the battle between facebook’s pytorch and google’s tensorflow ? a quick search will reveal the intensity of this clash of frameworks. here is one great article by kirill dubovikov .

at its core, the duel is fuelled by the similarity of the two frameworks. both frameworks:

  • are open source libraries for high-performance numerical computation
  • are supported by large tech companies
  • have strong and active supporting communities
  • are python-based
  • use graphs to represent the flow of data and operations
  • are well documented

taking all of this into account, we can say that almost anything created in one of the frameworks can be replicated in the other at a similar cost. therefore, the question stands.

which framework should you use? what is the main difference between each community?

at /data , we are constantly surveying the developer community to track the trends and predict the future of different technology sectors. for machine learning, in particular, this clash is critical. the prevailing framework, if there is one, will have a huge impact on the path that the machine learning community will take in the years to come.

with this in mind, we asked the developers who said that they are involved in data science (ds) or machine learning (ml) which of the two frameworks they are using, how they are using them, and what else they do in their professional life.

tensorflow is winning the game, but is pytorch playing on the same console?

from the 3,000 developers involved in ml or ds, we saw that 43 percent of them use pytorch or tensorflow.

this 43 percent is not equally distributed between the two frameworks. tensorflow is 3.4 times bigger than pytorch. a total of 86 percent of ml developers and data scientists said they are currently using tensorflow, while only 11 percent were using pytorch.

moreover, pytorch has more than 50 percent of its community also using tensorflow. on the other hand, only 15 percent of the tensorflow community also uses pytorch. it would seem like tensorflow is a must, but pytorch is a nice-to-have.

tensorflow pytorch

who is using pytorch and who is using tensorflow? what is each framework being used for the most?

here are the things that really stood out from the rest:

tensorflow vs pytorch

it is conclusive. in comparison to pytorch, tensorflow is being used in production and most probably deployed to the cloud as implied by the significantly higher backend experience of tensorflow users (4.8 years vs. 3.8 of pytorch users). as compared to pytorch, its community is composed more of professional machine learning developers (28 percent), software architects (26 percent), and programmers within a company (58 percent). this is most likely due to google’s focus on deployment through apis such as tensorflow serving , which has become a key motivator for the adoption of tensorflow for many developers who are trying to push data products into production environments.

on the other hand, pytorch is being used more than tensorflow for data analysis and ad-hoc models within a business context (10 percent). in the pytorch community, there are far more python-first developers (i.e developers using python as a primary language) who work on web applications (46 percent). moreover, the versatility of this pythonic framework allows researchers to test out ideas with almost zero friction and therefore, it’s the go-to framework for the most advanced cutting edge solutions.

interested in more insights about machine learning developers and data scientists? get in touch !

PyTorch TensorFlow Machine learning Data science Framework dev

Opinions expressed by DZone contributors are their own.

Related

  • Deep Learning Frameworks Comparison
  • AI Frameworks for Software Engineers: TensorFlow (Part 1)
  • Ethical AI and Responsible Data Science: What Can Developers Do?
  • Unlocking the Power of Explainable AI With 5 Popular Python Frameworks

Partner Resources

×

Comments
Oops! Something Went Wrong

The likes didn't load as expected. Please refresh the page and try again.

ABOUT US

  • About DZone
  • Support and feedback
  • Community research
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Core Program
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 3343 Perimeter Hill Drive
  • Suite 100
  • Nashville, TN 37211
  • support@dzone.com

Let's be friends:

Likes
There are no likes...yet! 👀
Be the first to like this post!
It looks like you're not logged in.
Sign in to see who liked this post!