Pytorch vs tensorflow reddit Tensorflow isn't really seriously considered by many players in the field today, it's generally PyTorch or Jax for the last year if you've wanted to be spicy. Is pytorch or tensorflow better for NLP? Strictly speaking, you shouldn't use the pure versions of either. Different answers for Tensorflow 1 vs Tensorflow 2. Maybe Microsoft can explain why their data scientists choose Pytorch instead of Tensorflow There are benefits of both. Huggingface has the best model zoo and the best API, and works as a wrapper for both frameworks. I made a write-up comparing the two frameworks that I thought might be helpful to those on this sub who are getting started with ML ! Being a new Pytorch user, I was curious to train the same model with Pytorch that I trained with Tensorflow a few months ago. If you learn Pytorch first and fully understand it, then Tensorflow/Keras will be easy to reproduce. However, if you find code in Pytorch that could help into solving your problem and you only have tensorflow experience, then it will be hard to follow the code. I would suggest Pytorch. Sort of. Just to say. Tensorflow is a much higher level API. TensorFlow isn't easy to work with but it has some great tools for scalability and deployment. Background: I started with Theano+Lasagne almost exactly a year ago and used it for two of my papers. This subreddit is temporarily closed in protest of Reddit killing third party apps, see /r/ModCoord and /r/Save3rdPartyApps for more information. Might be worth mentioning Eager Execution, since the main reasons given for not using TensorFlow is the related to the static vs dynamic computational graphs. TensorFlow: Hard to start, static graph is much different than Torch PlaceHolders and really nice think, when you want multiple output from Network or merge multiple stuff. But for me, it's actual value is in the cleverly combined models and the additional tools, like the learning rate finder and the training methods. The Metal backends for Tensorflow and PyTorch are problematic, as far as I can tell. That being said, it doesn't seem like pytorch has something as quick as `tf. Members Online [N] [P] Google Deepmind released an album with "visualizations of AI" to combat stereotypical depictions of glowing brains, blue screens, etc. neural networks), while the latter is a toolbox with mainly functions for image processing and geometry. --- If you have questions or are new to Python use r/LearnPython Tensorflow ships with keras a higher level wrapper. Also for PyTorch only, the official pytorch tutorials (web-based) is one of the best and most up-to-date ones. io is the original project that supports both tensorflow and theano backends. The tutorials on the PyTorch website were really concise and informative and to me the overall workflow is much more initiative. I run a 3900X cpu and with stable diffusion on cpu it takes around 2 to 3 minutes to generate single image whereas using “cuda” in pytorch (pytorch uses cuda interface even though it is rocm) it takes 10-20 seconds. PaddlePaddle github page has 15k stars, Pytorch has 48k, Keras has 51k. most of the newer codes/projects are written in pytorch. I started off with tensorflow as well, learned tf extended, tf hub and all the works, but eventually ported over to torch when I decided to learn it. There is an abundance of materials/example projects in PyTorch. So in theory this should work. Also, TensorFlow makes deployment much, much easier and TFLite + Coral is really the only choice for some industries. So here, TensorFlow does not spend extra time in Python AND it has an optimized implementation in C++. As for why people say that researchers use pytorch and that tensorflow is used in industry and deployment, the reason is quite straightforward, if you are after being able to implement, prototype easily like in research you'd prefer pytorch because of the familiar numpy like functionally but if you're after saving some milliseconds at inference PyTorch gives you just as much control as TensorFlow, and it's easier to use overall. So if you're doing a task that could be io bound, tensorflow might be the way to go. Why is it that when I go to create a CNN with 4 layers (output channels: 64, 32, 16, 16), I can do this in PyTorch, but in Tensorflow I get resource… Lately people are moving away from TensorFlow toward PyTorch. For 1), what is the easiest way to speed up inference (assume only PyTorch and primarily GPU but also some CPU)? AMD GPUs work out of the box with PyTorch and Tensorflow (under Linux, preferably) and can offer good value. Industry Adoption. Pytorch just feels more pythonic. Pytorch feels pythonic. So at that point, just using pure PyTorch (or JAX or TensorFlow) may feel better and less convoluted. It never felt natural. However, in the long run, I do not recommend spending too much time on TensorFlow 1. Jan 10, 2024 · Where rapid prototyping and experimentation are key, PyTorch is your best option. Eager Execution is officially part of core since 1. ; TensorFlow is a mature deep learning framework with strong visualization capabilities and several options for high-level model development. PyTorch is known for its ease of use and flexibility. Community and Support: PyTorch also has a strong and growing community, excellent documentation, and a wealth of tutorials. Either tensorflow 2. 0 was released and it looked like tensorflow had just caught up with some of the features of pytorch. But it's a difficult battle to win since PyTorch is built for simplicity from the ground up. Since TF usage is dwindling in research, and possibly showing signs of similar in industry, Keras is now multi-backend again, supporting TensorFlow, PyTorch, and JAX. Should I reconsider when I was making the decision was around the time 2. But personally, I think the industry is moving to PyTorch. While pytorch and tensorflow works perfectly, for an example pytorch3d rapids deepspeed does not work. Bye bye tensorflow. Reply reply Lastly, Keras may be a problem, since without proper installation, Keras throws some crashes (its a pain to install). x - a redesigned that tried to be more pytorch-like - but pytorch was already there. Things look even worse for TF when you consider whether the people using Tensorflow are using Tensorflow 1. 0 is simply that the research community has largely abandoned it. PyTorch (blue) vs TensorFlow (red) For example, if you search for CTPN, the keras implementation is updated 2 years ago (and use tensorflow 1. have 28 mil installations of Torch vs 13 mil installation of TF a month), but production figures in commercial environment is another story, and we don't know the real situation there. I've been using PyTorch for larger experiments, mostly because a few PyTorch implementations were easy to get working on multiple machines. Documentation is the worst s#it possible. You might find keras do a lot of stuff for you. io because of Theano support. However, tensorflow implements under-the-hood computations more efficiently than pytorch. In the vast majority of cases, I'd recommend using PyTorch. I can’t recall what the speedup was with the tensorflow mnist example, but it was material. torch's checkpoints Oct 27, 2024 · Comparing Dynamic vs. Specifically, I am looking to host a number of PyTorch models and want - the fastest inference speed, an easy to use and deploy model serving framework that is also fast. Please stop. ML scientists can use whatever framework they prefer (often you end up using a third party repo made in tensorflow rather than pytorch etc) ML engineers don't have to maintain anything but a single runtime, big win Bonus point: ONNXs also encapsule the model's graph, which is a big plus compared to e. Pytorch continues to get a foothold in the industry, since the academics mostly use it over Tensorflow. TensorFlow, on the other hand, is widely used for deploying models into production because of its comprehensive ecosystem and TensorFlow Serving. I've made models using Tensorflow from both C++ and Python, and encountered a variety of annoyances using the C++ API. TensorFlow has a large user base and is production-grade. . But machine learning is not as simple as tf makes it looks like. Tensorflow has had so many changes that right now it is impossible to find a program that runs. It's library that is higher level than TensorFlow and is actually part of it now. It's Learning tensorflow is never a bad idea. If you are using Tensorflow, additionally Google offers smth called TPUs which are faster than GPUs for Deep Learning and are built to integrate with Tensorflow On the long run, Pytorch API is much more pythonic and better organized than tensorflow, tensorflow have had lots of major changes so far, I’ve seen researchers battle with the different versions. Deployment: Historically seen as more challenging to deploy in production compared to TensorFlow, but with the introduction of TorchScript and the PyTorch Serve library, deployment has become more straightforward. However, in PyTorch, the training doesn't even seem to pass a single epoch and takes too long. I'm the maintainer for an open source project called Horovod that allows you to distribute deep learning training (e. TF2 was pretty DOA, even Nvidia stopped really supporting it a couple of years ago haha. I haven't deeply used either but at work everybody rooted strongly for TensorFlow save for one of our tech experts who since the early days said PyTorch was more performant, easier to use and more possible to customize. But TensorFlow is a lot harder to debug. Gradients for some JAX is numpy on a GPU/TPU, the saying goes. The learning curve is probably a little steeper for Pytorch initially, but it is the default for modern deep learning research. I’d export that data and use tensorflow for any deep learning tasks. In reverse, importing tensorflow when torch is already imported is fine — so when importing both packages, you should make sure to import torch first, and then tensorflow. Pytorch will continue to gain traction and Tensorflow will retain its edge compute I started using tensorflow, however pytorch is the new chic thing. And it seems Pytorch is being more and more adopted in research and industry with continuous development and features added. g. I wouldn't say it's worth leaving Pytorch but maybe it's worth it to know how to read a PaddlePaddle code. In my opinion, PyTorch. To add to what others have said here, TF docs and online help is a mess because their API has changed so much over the years which makes it nearly impossible to find relevant help for issues without being sidetracked by posts/articles that end up being for an older version/API. TensorFlow. x or 2. This code will usually use Theano or TensorFlow 1. Other details: PyTorch, TensorFlow, and both of their ecosystems have been developing so quickly that I thought it was time to take another look at how they stack up against one another. In my code , there is an operation in which for each row of the binary tensor, the values between a range of indices has to be set to 1 depending on some conditions ; for each row the range of indices is different due to which a for loop is there and therefore , the execution speed on GPU is slowing down. Depending on the size of your models and what you want to do, your mileage may vary. As I am aware, there is no reason for this trend to reverse. Like others have said, python is definitely way more used in industry so it’s way better to know tensorflow/PyTorch. Once you code your way through a whole training process, a lot of things will make sense, and it is very flexible. However Pytorch is generally used by researchers and it's a more pythonic way of doing Deep Learning, whereas Tensorflow is generally more widespread in the industry due to its deployment capabilities like Tensorflow lite and Tensorflow serve. Microsoft says their data scientists use Pytorch *. However i find there is one critical feature which is lacking in pytorch is model serialisation. Both Tensorflow and PyTorch have C++ APIs. okqvow mrtqi hdris amsr zfcihe cbdgx xtigd llrlkvu cewvrso unpjv ttqguy yiudis wyd gbxqmw ycuvn