Tensorflow creates static graphs, PyTorch creates dynamic graphs.

In Tensorflow, you have to define the entire computational graph of the model and then run your ML model. In PyTorch, you can define/manipulate/adapt your graph as you work. This is particularly helpful while using variable length inputs in RNNs.

Tensorflow has a steep learning curve. Building ML models in PyTorch feels more intuitive. PyTorch is a relatively new framework as compared to Tensorflow. So, in terms of resources, you will find much more content about Tensorflow than PyTorch. This I think will change soon.

Tensorflow is currently better for production models and scalability. It was built to be production ready. PyTorch is easier to learn and work with and, is better for some projects and building rapid prototypes.

A majority of papers were presented at every "important conference" last year with PyTorch over Tensorflow. PyTorch takes over in academia.

It was used in 50 to 75% of the papers, depending on the conference and was mostly used in the minority in 2018. PyTorch. Although some believe that PyTorch is still a trigger framework that tries to build a niche in a world dominated by TensorFlow, the data tells a different story. No meeting, excluding ICML, also kept the overall paper growth of TensorFlow on track. TensorFlow has fewer papers this year than last year in NAACL, ICLR, and ACL.

In part because of its simplicity and APIs available, researchers are switched to PyTorch. This common bump may be temporary, but it's impossible, even if it were: It wouldn't reverse too rapidly.

Although TensorFlow has paralleled PyTorch in functionality, the majority

in the community has already been reached by PyTorch. This will make it easier to find PyTorh implementations, inspire writers to publish code more in PyTorch (so that people are going to do that), and PyTorch will certainly be chosen by your collaborators. Thus, any migration back to TensorFlow 2.0 is likely to be slow, if it occurs at all. TensorFlow will still have a captive audience inside Google / DeepMind, but I wonder if Google will finally relax this.

Even now, many of the researchers that Google needs to hire would still prefer PyTorch at varying levels, and I've heard grumblings that many researchers within Google would like to use a system other than TensorFlow.

Moreover, the supremacy of PyTorch could start cutting Google researchers off from the rest of the research community. They will not only have a harder time building on external research, but also less likely to draw on Google's code from outside researchers. Whether TensorFlow 2.0 would allow TensorFlow to recover

some of its research public remains to be seen. Eagle mode is sure to be attractive, but it's not the same thing about the Keras API.

Have a look at https://realpython.com/pytorch-vs-tensorflow/ and https://www.machinelearningplus.com/deep-learning/tensorflow1-vs-tensorflow2-vs-pytorch/

Tensorflow creates static graphs, PyTorch creates dynamic graphs.

In Tensorflow, you have to define the entire computational graph of the model and then run your ML model. In PyTorch, you can define/manipulate/adapt your graph as you work. This is particularly helpful while using variable length inputs in RNNs.

Tensorflow has a steep learning curve. Building ML models in PyTorch feels more intuitive. PyTorch is a relatively new framework as compared to Tensorflow. So, in terms of resources, you will find much more content about Tensorflow than PyTorch. This I think will change soon.

Tensorflow is currently better for production models and scalability. It was built to be production ready. PyTorch is easier to learn and work with and, is better for some projects and building rapid prototypes.

Thank you Ahmed A. Mawgoud , Muhammad Ali and Collette Curry for the valuable knowledge

Dr. Collette Curry , do you mean by "variable length inputs" that Pytorch is more suitable for NLP?

Valuable information thanks for the contribution.

Hi Mohamed Yehia Dahab

PyTorch is suitable for NLP, however there is not as much support or information about using it in this way available online. This will change as more people use it. A good place to start looking at PyTorch and NLP is available here https://github.com/fastai/course-nlp

Yes, it is an interesting link ( https://www.researchgate.net/deref/https%3A%2F%2Fgithub.com%2Ffastai%2Fcourse-nlp). Thank you, Dr. Collette Curry

In PyTorch, your on-go graph can be defined/manipulated. This is especially useful when using variable-length inputs in RNNs.

checkout this article

https://medium.com/@UdacityINDIA/tensorflow-or-pytorch-the-force-is-strong-with-which-one-68226bb7dab4

I believe there is no particular "hard" technical reason. Many internet arguments still evolve around PyTorch vs Tensorflow v1 (v1!). However tf v1 is dead and was replaced by Keras/Tensorflow2 what's basically a different framework. Some observations:

1. It's confusing for new tf users to distinguish between dead v1 tutorials/help/etc and current v2 information on the internet. Google did the same with Angular in the past what's cool from branding perspective but hell for new users (Hint: Don't google for "tensorflow", google for "keras").

2. Maybe some people are frustrated by their Tensorflow v1 experience and angry because most obtained v1 hard skills became obsolete with v2. Instead of moving on with Tensorflow2/Keras they gave PyTorch a shot.

3. The PyTorch API and Keras Model API (i.e. you wrap your model in a class) make it easy for users to switch between frameworks. For my current project, I switched from Keras to PyTorch because my collaborator only knows PyTorch and I'm too agnostic to argue about Spanish vs Italian, coffee vs tea, etc.

When you build on top of existing research, you tend to look at the already published code. If you take TensorFlow, already published code would be in tf1, which has few deprecated functionalities for which you may need to refactor your code, although scripts are available for the refactoring, it may not be always perfect detecting and refactoring the deprecated ones. Whereas PyTorch from the beginning uses a dynamic computation graph, hence things are relatively smooth.

Pytorch is more relateable to python, whereas Tensorflow is very hard to relate to, and I can't focus enough how important Python has become for researchers.

I find pytorch more dynamic in terms of editing a graph network.

Pytorch is easy to master if you know the basics of DL(or similar fields), whereas Tensorflow focueses on production, and a little good for hackers.

In the following link there are useful information

https://realpython.com/pytorch-vs-tensorflow/

Table of Contents

• What Is TensorFlow?
  • Who Uses TensorFlow?
  • Code Style and Function
  • Special Features
  • Tensorflow Ecosystem
• What Is PyTorch?
  • Who Uses PyTorch?
  • Code Style and Function
  • Special Features
  • PyTorch Ecosystem
• PyTorch vs TensorFlow Decision Guide
  • Style
  • Data and Model
  • Project Goal
• Conclusion
• Further Reading

hi there

can you please provide a better source for beginners as how to approach pytorch

Dear Maham Ghauri , I think you can follow this question https://www.researchgate.net/post/Where_can_I_find_a_detailed_tutorial_for_using_Pytorch_in_NLP

Respected Mohamed Yehia Dahab

please refer the link below

https://pureai.com/articles/2019/10/10/machine-learning-framework-popularity.aspx#:~:text=%22While%20some%20believe%20that%20PyTorch,different%20story%2C%22%20He%20writes.&text=He%20speculates%20that%20researchers%20are,its%20simplicity%20and%20available%20APIs.

Regards

Rachit S Garg

both TensorFlow and PyTorch provide useful abstractions to reduce amounts of boilerplate code and speed up model development. In term of differences, PyTorch is more “pythonic” and has an object-oriented approach whereas TensorFlow has several options from which you may choose. basically, it is a question a interest from everyone of us. In contrast , both are powerful tools for deep learning.

According to The Gradient's 2019 study of machine learning framework trends in deep learning projects, the two major frameworks continue to be TensorFlow and PyTorch, and TensorFlow is losing ground -- at least with academics.

According to Horace He, author of the study and the article presenting the findings, PyTorch is taking over in academia, with a majority of papers presented at every "major conference" in the last year using PyTorch over TensorFlow. Depending on the conference, it was used in 50 to 75 percent of the papers, whereas in 2018, PyTorch was often in the minority.

"While some believe that PyTorch is still an upstart framework trying to carve out a niche in a TensorFlow-dominated world, the data tells a different story," He writes. "At no conference except ICML has the growth of TensorFlow even kept up with the overall paper growth. At NAACL, ICLR, and ACL, TensorFlow actually has less papers this year than last year." (See the study for charts and other detailed data.)

He speculates that researchers are switching to PyTorch in part because of its simplicity and available APIs. He notes that this bump in popularity could be temporary, but that it's not likely, nor would it be likely to reverse very quickly even if that were the case:

Even if TensorFlow has reached parity with PyTorch functionality-wise, PyTorch has already reached a majority of the community. That means that PyTorch implementations will be easier to find, that authors will be more incentivized to publish code in PyTorch (so people will use it), and that your collaborators will be most likely prefer PyTorch. Thus, any migration back to TensorFlow 2.0 is likely to be slow, if it occurs at all.

TensorFlow will always have a captive audience within Google/DeepMind, but I wonder whether Google will eventually relax this. Even now, many of the researchers that Google wants to recruit will already prefer PyTorch at varying levels, and I've heard grumblings that many researchers inside Google would like to use a framework other than TensorFlow.

In addition, PyTorch's dominance might start to cut off Google researchers from the rest of the research community. Not only will they have a harder time building on top of outside research, outside researchers will also be less likely to build on top of code published by Google.

It remains to be seen whether TensorFlow 2.0 will allow TensorFlow to recover some of its research audience. Although eager mode will certainly be appealing, the same can't be said about the Keras API.

Of course academia is one thing, but what about machine learning frameworks in production environments? Here, He compared data from job listings, Medium articles and GitHub stars over the last year to see which was more popular.

His findings? TensorFlow still rules among the enterprise and working deep learning professionals. "TensorFlow had 1541 new job listings vs. 1437 job listings for PyTorch on public job boards, 3230 new TensorFlow Medium articles vs. 1200 PyTorch, 13.7k new GitHub stars for TensorFlow vs. 7.2k for PyTorch," He wrote.

According to He, TensorFlow is often preferred in production environments in part due to pure speed advantages: "[I]ndustry considers performance to be of the utmost priority. While 10 percent faster runtime means nothing to a researcher, that could directly translate to millions of savings for a company."

"Another difference is deployment," He explained. "Researchers will run experiments on their own machines or on a server cluster somewhere that's dedicated for running research jobs. On the other hand, industry has a litany of restrictions/requirements."

"Historically, PyTorch has fallen short in catering to these considerations, and as a result most companies are currently using TensorFlow in production."

Dear Mohamed Yehia Dahab

Thanks for this valuable information. This is my first time to know about PyTorch

you are welcome Dr. Abdelhameed Ibrahim

I think because of its simplicity and available APIs.

Depends on the usage and requirement. Pytorch is relatively new framework compared to TensorFlow. So you will find loads more content about TensorFlow (this may change as Pytorch is getting widely used). For production system usage etc, TensorFlow is used in most of the places.

Pytorch is more dynamic (more ML model on the go rather than define the whole thing before you run). I found it more learner/researcher friendly (if you prefer python as your preferred language).

I also like TensorBoard which is a tool for visualising your ML models. You can use Matplotlib etc for Pytorch.

So it boils down to your preference and requirement.

I recently shifted for various reasons, the main one being a reason of easier determinism, to secure more easily the reproducibility and replicability of my results.

With Pytorch, I managed to run an experiment and get the exact same results from different computers with different GPUs. I tried but hardly achieved it with TF2 (and TF1 - and the shift from TF1 to TF2 wasn't an easy one).

On a more practical side, I also noticed that many friends in the ML industry are using Pytorch (or shifting to it) more than TF2.

The reason I shifted because TF is difficult to install on windows, mostly It does not pick my GPU. And the PyTorch provide more flexibility with less code.

In my opinion, despite its significance and powerul execution capabilities, TensorFlow demands extra resources that takes more in-place costs for such resources for the students with related limitations. Noteworthy, students are the biggest society demanding such tools, making a specific tool more/less popular!