Michio JP

Michio JP


The Battle: TensorFlow vs. Pytorch

Originally published by Eitan Rosenzvaig at dzone.com

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.

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:

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!

Originally published by Eitan Rosenzvaig at dzone.com


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Learn More

☞ Complete Guide to TensorFlow for Deep Learning with Python

☞ Data Science: Deep Learning in Python

☞ Python for Data Science and Machine Learning Bootcamp

☞ Deep Learning with TensorFlow 2.0 [2019]

☞ TensorFlow 2.0: A Complete Guide on the Brand New TensorFlow

☞ Tensorflow and Keras For Neural Networks and Deep Learning

☞ Tensorflow Bootcamp For Data Science in Python

☞ Complete 2019 Data Science & Machine Learning Bootcamp

#tensorflow #python

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The Battle: TensorFlow vs. Pytorch
Rohan Paul

Rohan Paul


Deep Learning with TensorFlow

Playlist in my Channel - Deep Learning with TensorFlow


#ComputerVision #OpenCV #MachineLearning #imageprocessing #DataScience #TensorFlow #DeepLearning #Python #DataScientist #Statistics  #ArtificialIntelligence #100DaysOfMLCode #Pytorch


Playlist of 12 Videos - Deep Learning / Computer Vision Algorithm Implementations

👉 https://www.youtube.com/playlist?list=PLxqBkZuBynVRyOJs4RWmB_fKlOVe5S8CR

#ComputerVision #Pytorch #MachineLearning #imageprocessing #DataScience #TensorFlow #DeepLearning #Python #DataScientist #Statistics  #ArtificialIntelligence #100DaysOfMLCode

👉 Github Repo (Numbered) - https://github.com/rohan-paul/MachineLearning-DeepLearning-Code-for-my-YouTube-Channel

👉 Blog - https://rohan-paul-ai.netlify.app/blog

You can find me here:


🐦 TWITTER: https://twitter.com/paulr_rohan
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How PyTorch Is Challenging TensorFlow Lately

  • PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments.

Google’s TensorFlow and Facebook’s PyTorch are the most popular machine learning frameworks. The former has a two-year head start over PyTorch (released in 2016). TensorFlow’s popularity reportedly declined after PyTorch bursted into the scene. However, Google released a more user-friendly TensorFlow 2.0 in January 2019 to recover lost ground.

Register for Hands-on Workshop (17th Jun) - oneAPI AI Analytics Toolkit

Interest over time for TensorFlow (top) and PyTorch (bottom) in India (Credit: Google Trends)

PyTorch–a framework for deep learning that integrates with important Python add-ons like NumPy and data-science tasks that require faster GPU processing–made some recent additions:

  • Enterprise support**: **After taking over the Windows 10 PyTorch library from Facebook to boost GPU-accelerated machine learning training on Windows 10’s Subsystem for Linux(WSL), Microsoft recently added enterprise support for PyTorch AI on Azure to give PyTorch users a more reliable production experience. “This new enterprise-level offering by Microsoft closes an important gap. PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments,” Jeremy Jancsary, a senior principal research scientist at Nuance, said.
  • PyTorchVideois a deep learning library for video understanding unveiled by Facebook AI recently. The source code is available on GitHub. With this, Facebook aims to support researchers develop cutting-edge machine learning models and tools. These models can enhance video understanding capabilities along with providing a unified repository of reproducible and efficient video understanding components for research and production applications.
  • PyTorch Profiler: In April this year, PyTorch announced its new performance debug profiler, PyTorch Profiler, along with its 1.8.1 version release. The new tool enables accurate and efficient performance analysis in large scale deep learning models.

#opinions #deep learning frameworks #machine learning pytorch #open-source frameworks #pytorch #tensorflow #tensorflow 2.0

Justyn  Ortiz

Justyn Ortiz


Guide to Conda for TensorFlow and PyTorch

Learn how to set up anaconda environments for different versions of CUDA, TensorFlow, and PyTorch

It’s a real shame that the first experience that most people have with deep learning is having to spend days trying to figure out why the model they downloaded off of GitHub just… won’t… run….

Dependency issues are incredibly common when trying to run an off-the-shelf model. The most problematic of which is needing to have the correct version of CUDA for TensorFlow. TensorFlow has been prominent for a number of years meaning that even new models that are released could use an old version of TensorFlow. This wouldn’t be an issue except that it feels like every version of TensorFlow needs a specific version of CUDA where anything else is incompatible. Sadly, installing multiple versions of CUDA on the same machine can be a real pain!

#machine-learning #pytorch #tensorflow #pytorch

Pytorch vs Tensorflow vs Keras | Deep Learning Tutorial (Tensorflow, Keras & Python)

We will go over what is the difference between pytorch, tensorflow and keras in this video. Pytorch and Tensorflow are two most popular deep learning frameworks. Pytorch is by facebook and Tensorflow is by Google. Keras is not a full fledge deep learning framework, it is just a wrapper around Tensorflow that provides some convenient APIs.

#pytorch #tensorflow #keras #python #deep-learning

Ikram Mihan

Ikram Mihan


Keras vs Tensorflow vs Pytorch

Keras vs Tensorflow vs Pytorch

Deep learning is a subset of Artificial Intelligence (AI), a field growing popularly over the last several decades. Deep learning and machine learning are part of the artificial intelligence family, though deep learning is also a subset of machine learning.

It imitates the human brain’s neural pathways in processing data, using it for decision-making, detecting objects, recognizing speech, and translating languages. It learns without human supervision or intervention, pulling from unstructured and unlabeled data.

Deep learning processes machine learning by using a hierarchical level of artificial neural networks, built like the human brain, with neuron nodes connecting in a web. While traditional machine learning programs work with data analysis linearly, deep learning’s hierarchical function lets machines process data using a nonlinear approach.

Keras,  TensorFlow and Pytorch are the three most popular deep learning frameworks. Let’s learn in detail each of these three.

#keras #tensorflow #pytorch #python