Pytorch vs tensorflow reddit. PyTorch replicates the numpy api + pythonic practices.

Pytorch vs tensorflow reddit That being said, it doesn't seem like pytorch has something as quick as `tf. 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. Converting to Keras from ONNX is not possible, and converting to SavedModel from ONNX does also not work in a stable way at the moment (see this issue). There was a discussion here some time ago about TF, and I would not say that it is dead. In my field this nowadays this is pytorch almost 100%. Tensorflow + C++ + Windows was a nightmare but now I use pytorch->onnx and run onnxruntime in c++ and have no problems. 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 AMD GPUs work out of the box with PyTorch and Tensorflow (under Linux, preferably) and can offer good value. Also, Most bigger AI is create using pytorch. Import order. If you look at Tensorflow, it'd be easiest to start learning Keras first. Meaning you will find more examples for PyTorch. Should I use Tensorflow or Pytorch? Thanks in advance! The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. I prefer tensorflow only when the model needs to deployed in real-time. It abstracts away a lot things, which is not ao good for along run. 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. Bye bye tensorflow. It never felt natural. Get the Reddit app Scan this QR code to download the app now. The learning curve is probably a little steeper for Pytorch initially, but it is the default for modern deep learning research. The first point I would make is that Spark was designed first and foremost to be a data processing system. PyTorch is focusing on flexibility and performance, while TensorFlow is working on user-friendliness and responsible AI. PyTorch vs. But if you want to know if you have to use tensorflow or pytorch for a particular task, I could try to give my opinion on that. We would like to show you a description here but the site won’t allow us. In the vast majority of cases, I'd recommend using PyTorch. But PyTorch PyTorch vs TensorFlow Locked post. js needs either a TF SavedModel or Keras model (see here). The build system for Tensorflow is a hassle to make work JAX is numpy on a GPU/TPU, the saying goes. In the ongoing debate of PyTorch vs I'm getting back into machine learning after a long hiatus. This subreddit has gone Restricted and reference-only as part of a mass protest against Reddit's recent API changes, which break third-party apps and moderation tools. If you know numpy and/or python, it will make sense to you. , Quick Poll Tensorflow Vs PyTorch in 2024), I get the feeling that TensorFlow might not be the best library to use to get back up to speed. This makes it quite straightforward to flesh out your ideas into working code. Like others have said, python is To add to your point, if your work deals with SOTA, newer research, comp sci, etc. However i find there is one critical feature which is lacking in pytorch is model serialisation. However, between Keras and the features of TF v2, I've had no difficulty with TensorFlow and, aside from some frustrations with the way the API is handled and documented, I'd assume it's as good as it gets. TensorFlow 1 is a different beast. But most new work is being done in PyTorch for production, or Jax for performance/research. Let alone microcontrollers. Not sure if it's better than Pytorch but some codes that are written in PaddlePaddle seem to be able to beat Pytorch code on some tasks. PyTorch is definitely more popular for SOTA and research (statistics for both conda and pip says that we approx. For 1), what is the easiest way to speed up inference (assume only PyTorch . , TensorFlow) on platforms like Spark. Tensorflow syntax is a pain. So in theory this should work. , Quick Poll Tensorflow Vs PyTorch in 2024), I get the feeling that TensorFlow might not be the best library to use to get back up to In this article, we'll look at two popular deep learning libraries — PyTorch and TensorFlow – and see how they compare. If I had to start from scratch, I'd do pytorch probably. I made a write-up comparing the two frameworks that I thought View community ranking In the Top 5% of largest communities on Reddit. Tensorflow 1. Yet, I see time and time again people advocating for PyTorch over TensorFlow (especially on this sub). However, tensorflow implements under-the-hood computations more efficiently than pytorch. Lately people are moving away from TensorFlow toward PyTorch. However, Tensorflow. Keras is still a gentler intro. Different answers for Tensorflow 1 vs Tensorflow 2. Discussion 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 I'm new in DL, I have learned ANN and CNN. The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. We love reddit! We hope to make it a better place for everyone! Last time i checked, pytorch didn't have a light interpreter for linux boxes. , that new research is 99% of the time going to be in pytorch, and it's often difficult to port quickly to tensorflow, especially if you're using things like custom optimizers, so you may as well use pytorch to save yourself time and headaches. PyTorch replicates the numpy api + pythonic practices. ml. I'm biased against tensorflow though because I find it's often a pain to use. x or 2. PyTorch: Which One is Prevailing? TensorFlow has long been a dominant force in deep learning, supported by a vast community, active forums, and regular updates from Google. Classes are natural and reward mix and matching. Since both libraries use cuDNN under the hood, I would expect the individual operations to be similar in speed. PyTorch has chosen not to implement this, which makes TPUs slower than GPUs for PyTorch. If you just start with TensorFlow you might get Pytorch. Though tensorflow might have gotten better with 2. Once you code your way through a whole training process, a lot of things will make sense, and it is very flexible. Static Graphs: PyTorch vs. Documentation is the worst s#it possible. I don't think people from PyTorch consider the switch quite often, since PyTorch already tries to be numpy with autograd. So at that point, just using pure PyTorch (or JAX or TensorFlow) may feel better and less convoluted. This part of the summary is shocking to say the least: On TPU, a remarkable 44% of PyTorch benchmark functions partially orcompletely fail. This is mostly not true for tensorflow, except for massive projects like huggingface which make an effort to support pytorch, tensorflow, and jax. There is a 2d pytorch tensor containing binary values. Just to say. Its robustness and scalability make it a safe choice for businesses. Even the co-creator of PyTorch acknowledges this fact, he tweeted recently: "Debates on PyTorch vs TensorFlow were fun in 2017. Lastly, Keras may be a problem, since without proper installation, Keras throws some crashes (its a pain to install). Should I reconsider when I was making the decision was around the time 2. Some played with Julia from time to time but that also seems to have faded again. Huggingface has the best model zoo and the best API, and works as a wrapper for both frameworks. Conversely, if you know nothing and learn pytorch, you will feel more at home when Both Tensorflow and PyTorch have C++ APIs. Explore the key differences between Pytorch and Tensorflow 2 as discussed on Reddit, focusing on performance and usability. tensorflow. keras is a clean reimplementation from the ground up by the original keras developer and maintainer, and other tensorflow devs to only support tensorflow. I agree to some extent. It's library that is higher level than TensorFlow and is actually part of it now. 9M subscribers in the MachineLearning community. I have never understood why there is this strong divide between tf and pytorch, specially the tf 2. The former are frameworks for making efficient computations that require gradients (e. A similar trend is seen in 8 top AI journals. However, in PyTorch, the training doesn't even seem to pass a single epoch and takes too long. To answer your question: Tensorflow/Keras is the easiest one to master. I've made models using Tensorflow from both C++ and Python, and encountered a variety of annoyances using the C++ API. It also boasts TensorFlow Lite for mobile deployment, with hardware acceleration through ASIC chips and TPUs on Google Cloud. So if you're doing a task that could be 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. Pytorch feels pythonic. This code Either. 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. oh just in general with nvidia documentation there are many ways to install the driver stack and under linux /ubuntu you can have the display drivers installed but they need to be compatible with certain versions of cuda depending on what card your running. Tensorflow has had so many changes that right now it is impossible to find a program that runs. 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. Matlab was great for doing some signal analysis, preprocessing tasks, and even in some cases whipping up simple baseline ML models. There's some evidence for PyTorch being the "researcher's" library - only 8% of papers-with-code papers use TensorFlow, while 60% use PyTorch. io is the original project that supports both tensorflow and theano backends. 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. In PyTorch, you are in Python a lot due to the dynamic graph, so I would expect that to add some overhead. PyTorch Memory Usage . Comparing Dynamic vs. Haven't tried wsl. PyTorch to ONNX works fine, and ONNX to Tensorflow works fine. PyTorch is known for its intuitive design, making it a preferred choice for research and prototyping, thanks to its dynamic computation graph. I have it setup and I use it to test builds because we are switching to linux at least on the production 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. 0 or Pytorch are fine. Tensorflow has 19 million. I’d export that data and use tensorflow for any deep learning tasks. 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. 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 I would suggest Pytorch. 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. I prefer PyTorch especially to deal with RNNs, seq2seq and weights sharing. Now, PyTorch is research-only, to put PyTorch model in production you have to learn Caffe2, not sure how well that works at If they run on Pytorch and Tensorflow, they both now natively support ROCm. Assuming you have experience with Python, PyTorch is extremely intuitive. Reply reply PyTorch (blue) vs TensorFlow (red) TensorFlow has tpyically had the upper hand, particularly in large companies and production environments. Keras is a sub-library within Tensorflow that let's you build Tensorflow models with higher-level (easier) code. 0 i left it and didn't look back. If you happen to remain in the python eco-system, you will be very easily lured to PyTorch or PyTorch based Also as for TensorFlow vs PyTorch it really shouldn't matter too much but I found PyTorch much easier to get started with. 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. View community ranking In the Top 1% of largest communities on Reddit. TF2 was pretty DOA, even Nvidia stopped really supporting it a couple of years ago haha. Is there something I'm doing wrong? Tensorflow and related librairies suffer from the problem that the API is poorly documented imo, some TFP notebooks didn't work out of the box last time I tried. Windows support is still incomplete, and tooling hasn't quite caught up (like CMAKE integration for Windows ROCm) and small things here and there. 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. Like when I think data science on that kind of expression data it feels almost the same as using a simple regression model rather than something that should be using epochs. Now, my question for this post is: If TensorFlow has fallen so far out of favor and people are advising against using it, why does a I've been meaning to do a project in tensorflow so I can make a candid, three-way comparison between Theano+Lasagne, PyTorch, and Tensorflow, but I can give some rambling thoughts here about the first two. If you are getting started with deep learning, the available tools and frameworks will be overwhelming. And ROCm now natively supports by official decree, Radeon Graphics cards, like 6800 and above for both HIP SDK and Runtime. Background: I started with Theano+Lasagne almost exactly a year ago and used it for two of my papers. Members Online • ButthurtFeminists [D] Discussion on Pytorch vs TensorFlow Discussion Hi, I've been using TensorFlow for a couple of months now, but after watching a quick Pytorch tutorial I feel We would like to show you a description here but the site won’t allow us. Be the first to comment Nobody's responded to this post yet. PyTorch, Caffe, and Tensorflow are not directly comparable to OpenCV. Instead of fighting the framework, you can focus in on tuning for performance. For people working in embedded, that's a deal breaker. Pytorch vs Tensorflow (Training Speed) Being a new Pytorch user, I was curious to train the same model with Pytorch that I trained with Tensorflow a few months ago. However, TensorFlow (in graph mode) compiles a graph so when you run the actual train loop, you have no python overhead outside of the session. With PyTorch’s dynamic computation graph, you can modify the graph on-the-fly, which is perfect for applications requiring real-time Get the Reddit app Scan this QR code to download the app now. io because of Theano support. I've done 5 years of PyTorch, hopped on it as soon as it came out because it was better than Theano (great lib, just horrible when debugging) and Tensorflow (with which my main gripe was non-uniformity: even model serialization across paper implementations varied by a lot). 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. torch's checkpoints I have been also trying TensorFlow and PyTorch (having known Caffe and Torch). The manual assumes you There are discussions elsewhere on the subject, like on reddit for example. In my opinion, PyTorch. x. 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. And that is why i would recommend PyTorch. all other resources mentioned in other answers are also among top resources for PyTorch. I don't have any problem using tensorflow but if pytorch will be more best option then I can move to pytorch. data` although I hear that nvidia dali is pretty good. TensorFlow, on the other hand, is widely used for deploying models into production because of its We would like to show you a description here but the site won’t allow us. Though there are not much tutorials or blog posts about this, I will try creating a github repo for this later (just examples with simple layers), so many more people will know This subreddit is temporarily closed in protest of Reddit killing third party apps, see /r/ModCoord and /r/Save3rdPartyApps for more information. Sort of. x approach is quite similar to pytorch in my opinion. The base Tensorflow library is lower-level (more nitty-gritty) and it would be best to approach it after you learned the basics with Keras. Both PyTorch and TensorFlow are super popular I've been using PyTorch for larger experiments, mostly because a few PyTorch implementations were easy to get working on multiple machines. But machine learning is not as simple as tf makes it looks like. x - a redesigned that tried to be more pytorch-like - but pytorch was already there. Would use torch over tensorflow if otherwise. Finally, If you want to go for certified (but paid) versions of such topics, coursera has both ML and DL courses with high quality material. I'm wondering how much of a performance difference there is between AMD and Nvidia gpus, and if ml libraries like pytorch and tensorflow are sufficiently supported on the 7600xt. The official Python community for Reddit! Stay up to date with the latest news, What's the future of PyTorch and TensorFlow? Both libraries are actively developed and have exciting plans for the future. Keras?Stuff like build in fit and evaluate methods, callbacks and all of that. As an exercise, maybe you could visit MakerSuite and use their Python code snippets (for learning) to ask PaLM 2 to explain the pros and cons of PyTorch vs TensorFlow. I'm the maintainer for an open source project called Horovod that allows you to distribute deep learning training (e. That lead to projects like Keras to hide much of the trickiness of TF1. CNNs in PyTorch vs tensorflow upvote This subreddit is temporarily closed in protest of Reddit killing third party apps, see /r/ModCoord and /r/Save3rdPartyApps for more information. Eager Execution is officially part of core since 1. I can’t recall what the speedup was with the tensorflow mnist example, but it The theory and conceptual understanding of things is more important. Even worse, what used to work right now I can't make it to work. TensorFlow isn't easy to work with but it has some great tools for scalability and deployment. Due to a bug in PyTorch, importing torch when tensorflow is already imported will cause either a segfault crash of your Python runtime, or a deadlock. Laptops are not rly great for deep learning imo (even considering their powerful GPUs a dedicated PC or Server is much better). I wouldn't say it's worth leaving Pytorch but maybe it's worth it to know how to read a PaddlePaddle code. Both of them can be used to create any machine learning model, but pytorch is now far more widely used than tensorflow. Tensorflow vs. The same model, and same dataset, on Tensorflow, took 500 s on avg per epoch, but in PyTorch it is around 3600 s, and the colab memory usage is skyrocketing, thus crashing the server. Initially I started with multi-machine TensorFlow by following the High-Performance Models That's correct, keras. Is it true that tensorflow is actually dying and that google gave up tensorflow? comments sorted by Best Top New Controversial Q&A Add a Comment More posts you may like Pytorch is easier to debug, and on the other hand, tensorflow is lot more fussy IMO. For immediate help and problem solving, please join us at https Keras is a high level API for TensorFlow, while fastai is sort of a higher level API for PyTorch too. Things look even worse for TF when you consider whether the people using Tensorflow are using Tensorflow 1. Tensorflow vs Pytorch for RL . x - was OK for its time, but really inflexible if you wanted to do anything beyond their examples/tutorials. ptrblck May 15, 2021, 6:42am 4. TensorFlow. It's Pythonic to the nth degree: you can write what you need cleanly and concisely. There is an abundance of materials/example projects in PyTorch. If you know what you want to do maybe I can help further. 2. But if you decide to go with TensorFlow check out Keras. Share Add a Comment. g. So I assume JAX is very handy where TensorFlow is not pythonic, in particular for describing mid to low level mathematical operations that are less common or optimize common layers. DL, D Hi, I've done an intro RL course and I want to make AI bots that beat games. Or check it out in the app stores     TOPICS. neural networks), while the latter is a toolbox with mainly functions for While pytorch and tensorflow works perfectly, for an example pytorch3d rapids deepspeed does not work. I agree that this discussion board might not be the best place to discuss these vague questions, as you might TensorFlow vs. And anecdotally Pytorch seems much more popular than PyTorch, hands down, because it feels way more Pythonic—everything just clicks naturally with how Python works, making it super intuitive compared to TensorFlow. 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 It's basically hand-picking weights from Pytorch model's layers to TensorFlow model's layers, but it feels more reliable than relying on ONNX with a bunch of warnings. Internet Culture (Viral) Amazing; Animals & Pets Tensorflow vs. After talking with a friend and doing some research (e. Tensorflow died out completely about 2 years ago, no JAX yet. The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python 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. You Might Also Like: PyTorch vs Keras in 2025; TensorFlow vs JAX in 2025; Best Machine Learning A buddy and I used keras for transcriptome data for a data science challenge a few years ago but it just didn't feel right. However, in the long run, I do not recommend spending too much time on TensorFlow 1. Last I've heard ROCm support is available for AMD cards, but there are inconsistencies, software issues, and 2 - 5x slower speeds. Tensorflow will still be around for a long time, because so many projects are already using it. Very old code will import keras directly, and be referring to Keras 1. PaddlePaddle github page has 15k stars, Pytorch has 48k, Keras has 51k. However, to still give you a personal answer, I Explore the latest discussions on Pytorch vs Tensorflow in 2024, comparing features, performance, and community insights. Microsoft says their data scientists use Pytorch *. Also for PyTorch only, the official pytorch tutorials (web-based) is one of the best and most up-to-date ones. run call. taking a course on welding. There was healthy competition to innovate, and philosophical differences like Theano vs Torch, The company I work for (I'm not a data scientist) uses tensorflow, I've had a bias towards pytorch when I've done side projects. 0 is simply that the research community has largely abandoned it. You might find keras do a lot of stuff for you. New comments cannot be posted. TensorFlow specifically runs input processing on the CPU while TPU operations take place. Pytorch (training speeds) Being a new Pytorch user, I was curious to train the same model with Pytorch that I trained with Tensorflow a few months ago. After many months trying to learn tensorflow today I have decided to switch to pyTorch. The TensorFlow 2 API might need some time to stabilize. Or check it out in the app stores It's like the difference between reading the operating manual for a welding torch vs. Keras_core with Pytorch backend fixes most of this, but it is slower than Keras + tensorflow. Personally, I think TensorFlow 2 and PyTorch are pretty similar now, so it should not matter that much. I remember when Pytorch first became more popular than Tensorflow in the research community, everyone said Tensorflow would still remain the My biggest issue with Tensorflow 2. If hardware resources are an issue for you, consider looking into cloud solutions and remote computing where you can run your network. Members Online [N] [P] Google Deepmind released an album with "visualizations of AI" to combat stereotypical depictions of glowing brains, blue screens, etc. If you learn Pytorch first and fully understand it, then Tensorflow/Keras will be easy to reproduce. Even Bard is not development using tensorflow. Keras is a much higher level library that's now built into tensorflow, but I think you can still do quite a bit of customization with Keras. Whether you look at mentions in top conferences or code repos, PyTorch now outnumbers TensorFlow by a 3-5:1 ratio. I was using Tensorflow but some day ago, I saw a post where people were hating tensorflow. Depending on the size of your models and what you want to do, your mileage may vary. I tend to believe people will be using still keras. Either way, I have yet to see anything in either TensorFlow or Keras that isn't readily available in PyTorch. Creating own layer consume a 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. Tensorflow ships with keras a higher level wrapper. Tensorflow was always like a c++ dev wrote an Api for python devs. . I have to admit that Tensorflow Eager looks promising though. Maybe Microsoft can explain why their data scientists choose Pytorch instead of Tensorflow There are benefits of both. Tensorflow 2. My understanding is TensorFlow for prod, and PyTorch for research and development. 7, and seems to be the recommended way to For me I'm switching from Tensorflow to pytorch right now because Tensorflow has stopped supporting updates for personal windows machines. Tensorflow vs Pytorch . 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. And it seems Pytorch is being more and more adopted in research and industry with continuous development and features added. Hello, so I was mainly using Tensorflow/Keras for the past 2 years when I finally decided to learn PyTorch for some extra control, after a couple of months I decided to then learn Lightning to get out of rewriting the same boilerplate code for every project, but isn't it the same as just using tf. The tutorials on the PyTorch website were really concise and informative and to me the overall workflow is much more initiative. I I would highly recommend Pytorch to anyone who is either learning Reinforcement Learning or Bayesian Machine Learning (because Pyro is In terms of package downloads, Pytorch has 29 million downloads in the past month. 0 was released and it looked like tensorflow had just caught up Is pytorch or tensorflow better for NLP? Strictly speaking, you shouldn't use the pure versions of either. Without users, reddit would be little more than chunks of code on a server. PyTorch gives you just as much control as TensorFlow, and it's easier to use overall. --- If you have questions or are new to Python With TensorFlow, you get cross-platform development support and out-of-the-box support for all stages in the machine learning lifecycle. Either tensorflow 2. oght zhwkq pgwy sdvbu yinu caselr ppg mfiwn rohvv nst lxwigu dhhih dhjf qnm uubo