Jax vs pytorch vs tensorflow

Jax vs pytorch vs tensorflow. 94735 s. And from what I've heard, I think Jax will likely replace Tensorflow within Google. Hi guys, long post incoming. Looking at the recent paper that shows how poorly you can transfer (= getting the same results) functions from torch to jax and vice versa I think it is best to not mix them in a workflow. Tensorflow seems to be very production friendly, and as one of the earliest frameworks for Deep Learning, a lot of code has been written in Tensorflow and is still requiring maintenance. e. JAX is similar to other popular frameworks such as PyTorch and TensorFlow, but it has some unique features that make it a good choice for certain tasks. Pytorch will continue to gain traction and Tensorflow will retain its edge compute JAX vs. Pytorch is an open source machine learning framework with a focus on neural networks. The bias is also reflected in the poll, as this is (supposed to be) an academic subreddit. ネットワークを flax. Jax solves the problem, at least in part, of slow inference times on CPUs. 046s whereas TensorFlow has an average inference time of 0. DZone conducted a mini-experiment to study how JAX stacks up against other libraries. 0. And fine tuning existing models is the way to go as you'll never have the computing power to train models on the size of datasets necessary to train models as good as the best available models. That said, PyTorch offers a much further breadth of libraries and utilities, pre-trained and pre-written network definitions, a data loader, and portability to deployment destinations. Moreover, the 2018 survey reported that TensorFlow was used by 7. Both have their own style, and each has an edge in different features. However, while integration is possible, some adjustments may be needed due to differences in programming paradigms and design principles. Well, JAX and TensorFlow originated at Google, whereas PyTorch was first developed by Facebook. Thank god. JAX is another alternative used inside Google, but it’s quite low level and hard to use. Conversely, if you know nothing and learn pytorch, you will feel more at home when 100% backwards compatibility. Language support. Documentation. JAX vs TensorFlow for Machine Learning We would like to show you a description here but the site won’t allow us. So keep your fingers crossed that Keras will bridge the gap Apr 23, 2019 · For example, TensorFlow training speed is 49% faster than MXNet in VGG16 training, PyTorch is 24% faster than MXNet. compile that wraps your model and returns a compiled model. We'll talk about the main difference now. jit でデコレートする. RobbinDeBank. PyTorch offers flexibility without sacrificing the ease of use. Luckily, Keras Core has added support for both models and will be available as Keras 3. Apr 14, 2022 · Machine learning is a huge discipline, with applications ranging from natural language processing to solving partial differential equations. Train times under above mentioned conditions: TensorFlow: 7. 7 GB of RAM) was significantly lower than PyTorch’s memory usage (3. 6 percent for PyTorch. Flax is the neural network library for JAX. Mar 1, 2024 · PyTorch has made strides in deployment tools like TorchServe, but TensorFlow is still popular in production environments. Ease of use. Also according to said paper: Jax is def. ) Jun 21, 2021 · JAX's functionality with lower level function definitions makes it preferable for certain research tasks. So we have to ask ourselves: what problem does jax solve. Oct 18, 2019 · Across all models, on GPU, PyTorch has an average inference time of 0. With TF2. Derrick Mwiti. PyTorch is generally faster and provides superior debugging capabilities compared to Keras. Let’s look at it from three simple angles: the supply, the demand, and your situation and aspiration. Tensorflow and Pytorch on the other hand have precompiled GPU and TPU kernels for each operation. Its robustness and scalability make it a safe choice for businesses. It is incredibly user Sep 28, 2022 · TensorFlow Lite vs PyTorch Live. Since JAX requires less Research vs development. It is an open-source framework offered under an MIT License. It is mostly used in research than in production. 1) Jax Vs PyTorch Vs TensorFlow. Apr 28, 2023 · Whisper JAX vs. If you are a beginner, stick with it and get the tensorflow certification. Aug 6, 2019 · Being a high-level API on top of TensorFlow, we can say that Keras makes TensorFlow easy. Nov 17, 2021 · JAXのコードをそのままPyTorchにした場合の結果. 3rd point in Key Concepts: "Not all JAX code can be JIT compiled, as it requires array shapes to be static & known at compile time. The XLA compiler takes models from popular frameworks such as PyTorch, TensorFlow, and JAX, and optimizes the models for high-performance execution across different hardware platforms including GPUs, CPUs, and ML accelerators. It is from this landscape that major frameworks such as PyTorch, TensorFlow, and Flux. Aug 23, 2022 · Flax vs. Session anymore and TF2. The first thing we need to do to create an app is to import Flask and create a new instance of it. While some of these frameworks have the backing of Aug 25, 2023 · 1. JAX. ) instead of np. Figure 5: Run-time benchmark results: JAX is faster than PyTorch. 4 percent of professional developers use TensorFlow, while only 4. It is easy to use, fast, elegant and debugging is pretty intuitive. データローダを tensorflow-datasets で書き直す. Tensorflow Serving is a powerful tool capable of handling millions of requests per Second . These results compare the inference time across all models by If you want to be able to fine tune existing models PyTorch is the way to go as there are so many more available models for it than tensorflow. Jan 2, 2024 · This article compares Jax and PyTorch to decide which is better and worth learning. you can get from the nightly builds. XLA (Accelerated Linear Algebra) is an open-source compiler for machine learning. PyTorch has more debugging and testing options than TensorFlow. 学習の1ステップをJAXで書き直して jax. Jan 18, 2021 · PyTorch is a product of Facebook and it was released in 2016. Oct 13, 2022 · In both industries and academia, two deep learning libraries reign supreme: PyTorch and TensorFlow. Went from matlab cuda lib to theano to tensorflow and now mainly pytorch. In summary, the choice between TensorFlow and PyTorch depends on personal preference, the nature of the project, and whether the focus is on production deployment or research and experimentation. more advanced autodifferentiation is a breeze compared to PyTorch. Mar 26, 2020 · PyTorch Adam vs Tensorflow Adam. 0 this fall. x with its static graph approach to TensorFlow 2. When you lean into its advanced features a bit more, JAX makes you feel like you have superpowers. Aug 31, 2020 · These implementations provide a baseline for comparing the performance efficiency of each library, although our main comparison is between JAX and Autograd, as the utility of JAX/Autograd is not directly comparable to the purpose of PyTorch/TensorFlow. Pytorch/Tensorflow are mostly for deeplearning. It uses computational graphs and tensors to model computations and data flow Feb 28, 2024 · We also need to evaluate the learning curve of each framework based on the project. This can be convenient for usage in ML Ease of Use: PyTorch and scikit-learn are known for their simplicity and ease of use. At least, this is the theory. 見ての通り、JAXが圧倒的。PyTorchもnumpyに比べて速くなってはいるのでGPUを使っている効果が出ていると考えられますが、それ以上にJAXが速い。PyTorchと比較してJAXのほうが556倍も速いという結果でした。 Jan 24, 2024 · Key Takeaways. 5 GB for PyTorch. 0, and integrated the high level programming API Keras in the main API itself. jl arise and strive to be packages for "all of machine learning". " Jan 8, 2024 · TensorFlow vs. Sep 14, 2023 · PyTorch vs. But personally, I think the industry is moving to PyTorch. PyTorch is often preferred by researchers due to its flexibility and control, while Keras is favored by developers for its simplicity and plug-and-play qualities. •. 4. Speed and debugging. The researchers that developed Whisper JAX claim that the difference is more significant when transcribing long audio files. We note that the PyTorch implementation has quadratic run-time complexity (in the number of examples), while the JAX implementation has linear run-time complexity. Tensorflow did a major cleanup of its API with Tensorflow 2. 0 release in early March 2023. AI and ML. For instance, both Flax and TensorFlow can run on XLA. g. We expect to ship the first stable 2. Aug 26, 2019 · TensorFlow Lite is an open source deep learning framework for on-device inference. A framework quantizes, traces, optimizes, and saves models for both Android and iOS. PyTorch, TensorFlow, and JAX are three popular deep learning frameworks that are widely used for developing and training machine learning and deep learning models. Either way, thanks for your input! Totally agree that it's worth checking out different frameworks, and JAX is really exciting! May 14, 2022 · Let’s present a more thorough comparison of run-time performance. Dec 15, 2021 · Without a doubt TensorFlow (2015) and Pytorch (2016) have had a big impact in the ML community, the “arms race” between them has made them converge to a similar set of features (check out State of ML Frameworks 2019 for a good summary of their struggle). 1 percent use PyTorch. While we are on the subject, let’s dive deeper into a comparative study based on the ease of use for each framework. In Pytorch, an LSTM layer can be created using torch. For instance, you can easily load datasets in NumPy format for usage in Jax and PyTorch. app = Flask(__name__) To start the application, we can use the “ run” method on a form like: if __name__ =='__main__': app. Disclosure: I work on PyTorch. If you know numpy and/or python, it will make sense to you. PyTorch replicates the numpy api + pythonic practices. In this article, I will introduce you to some other deep learning libraries that have considerable usage, either because they achieve speedup in some ways, or because they are used by very specific groups. Jan 18, 2022 · Keras. JAX's underlying framework, XLA , is also For context on PyTorch’s growth, the 2020 Stack Overflow Developer Survey indicated that 10. In terms of ready-to-use layers and optimizers, Flax doesn’t need to be jealous of Tensorflow and Pytorch. It comes as an alternative to TensorFlow and PyTorch when it comes to implementing off-the-shelf state of the art deep learning models, for example Transformers, Bert etc. PyTorch has . 0 is it allows ‘Model Subclassing’, which is a commonly followed practice to build neural network models in PyTorch. PyTorch models can be converted to TorchScript for interoperability. x. Aug 12, 2022 · PyTorch is less efficient than Jax. It’s evolved from TensorFlow 1. TensorFlow. Recently, Meta AI released PyTorch 2. Ease of Use. OpenCV is an open-source computer vision library widely used for image and video processing, while PyTorch is a deep learning framework known for its flexibility and dynamic computation capabilities. 0 also supports dynamic graphs. From nvidia-smi utility it is visible that Pytorch uses only about 800MB of GPU memory, while Tensorflow essentially uses whole memory. 5 days ago · TensorFlow vs. Jax’s development stage is Developing (v0. Jan 19, 2024 · JAX allows integration with other frameworks, including PyTorch, through its interoperability features. Tensorflow is also less efficient than Jax. e. 8 GB for TensorFlow vs. Reply. TensorFlow is currently more widely used than PyTorch. Despite being widely used by many organizations in the tech industry, MxNet is not as popular as Tensorflow. In most cases, I’d recommend: start with XGBoost, then PyTorch. TensorFlow, on the other hand, has a steeper learning curve and can be more complex due to its computational graph concept. Developed during the last decade, both tools are significant improvements on the initial machine learning programs launched in the early 2000s. Compared to TensorFlow, MXNet has a smaller open source community. Best. TensorFlow is optimized for performance with its static graph definition. And jax suffers from the same issue. 8: Similar to Tensorflow Lite, Pytorch has also improved their existing Pytorch Mobile. Model Subclassing. PyTorch is a deep learning framework with a pythonic and object oriented approach. Probably similar to this and this. In comparison, JAX is a more functionally-minded library for arbitrary differentiable programming. May 28, 2020 · TensorFlow (TF) is an end-to-end machine learning framework from Google that allows you to perform an extremely wide range of downstream tasks. Flax doesn’t have data loading and processing capabilities yet. Dec 8, 2020 · When it comes to Deep Learning I consider PyTorch as my default framework. 5. Then your code can run on CPU, GPU, or TPU with no changes. , in principle with respect to the Natural Language Processing field. For sure it lacks the giant library of its competitors but it’s gradually getting there. PyTorch: PyTorch is known for its dynamic computation graph, which allows for more flexible and Sep 23, 2023 · #pytorch #tensorflow #ai #llm #huggingface In this video, I compare TensorFlow and PyTorch on model availability; model deployment; and the ecosystems that Learning tensorflow is never a bad idea. What is Jax? Jax is a machine-learning framework, much like PyTorch and TensorFlow. 」で繋げればTensorFlowは「tensorflow」から、Pytorchは「torch」から大体の機能を参照できるので上記のimport数の差に意味はありません。 Tensorflowはkerasが組み込まれていて、kerasからのインポートが多いですね。 The difference between jax and, say, pytorch's jit might feel small, but jax's functional approach allows for some truly powerful features that could not be easily added to pytorch, tf or cafe. PyTorch was developed with the goal of providing production optimizations similar to TensorFlow and make models easier to write. Mar 19, 2020 · Trax is a deep learning framework created by Google and extensively used by the Google Brain team. a large chunk of old TensorFlow supporters become furious due to massive deprecation of code no longer working in the latest version. Let’s begin! JAX Apr 25, 2021 · LSTM layer in Pytorch. Jun 22, 2020 · Pytorch (python) API on the other hand is very Pythonic from the start and felt just like writing native Python code and very easy to debug. TensorFlow has a steeper learning curve compared to Keras, which is known for its user-friendly interface. data. All of these technologies are now in the open source and maintained by open source communities Jan 10, 2024 · If you are getting started with deep learning, the available tools and frameworks will be overwhelming. PyTorch is based on Torch, a C framework for doing fast computation. JAX emphasises simplicity without sacrificing speed and scalability. Jun 17, 2022 · JAX doesn't offer a way to load data and pre-process data easily, developers and experts say, requiring TensorFlow or PyTorch to handle much of the setup. 本文对比分析了PyTorch和TensorFlow两大深度学习框架的优劣，介绍了大模型训练和推理的技巧，适合AI初学者和从业者阅读。 Aug 26, 2019 · In contrast, TensorFlow by default creates a single data flow graph, optimizes the graph code for performance, and then trains the model. While PyTorch provides a similar level of flexibility as TensorFlow, it has a much cleaner interface. PyTorch has a lower barrier to entry, because it feels more like normal Python. TensorFlow and its data loading solution ( tf. While eager execution mode is a fairly new option in Dec 25, 2021 · First, accepting that your AD will have to deal with the full dynamic nature of an entire programming language means accepting a much more difficult job. Pytorch continues to get a foothold in the industry, since the academics mostly use it over Tensorflow. It also differs from those other two libraries in some very important ways. Jan 9, 2024 · Pytorch (blue) vs Tensorflow (red) TensorFlow had the upper hand, particularly in large companies and production environments. Aug 15, 2021 · Yes, jitted functions in JAX will be re-traced when faced with inputs of a new shape: this is true regardless of the content of the function. PyTorch: A Comprehensive Comparison. 0) The TensorFlow development stage is Mature (v2. Installation and Setup: OpenCV is relatively easier to install compared to PyTorch. Dec 14, 2021 · TensorFlow has been hard to pick up. However, both models had a little variance in memory usage during training and higher memory usage during the initial loading of the data: 4. 4. Torch comes with a wrapper written May 22, 2021 · Pytorch 1. While TensorFlow is used in Google search and by Uber, Pytorch powers OpenAI’s ChatGPT and Jun 19, 2022 · JAX is a framework for machine learning that allows you to use Python and NumPy to create and train neural networks. (jax. Ease of Use: PyTorch offers a more intuitive, Pythonic approach, ideal for beginners and rapid prototyping. Feb 15, 2022 · Especially if you are considering moving from PyTorch or TensorFlow to JAX, you should understand that JAX’s underlying philosophy is quite different from the two Deep Learning frameworks. 44318 s PyTorch: 27. PyTorch and Tensorflow are dedicated deep learning libraries with a lot of high-level APIs Whether you look at mentions in top conferences or code repos, PyTorch now outnumbers TensorFlow by a 3-5:1 ratio. TensorFlow is a low-level deep learning library that provides workflows to high-level APIs such as Keras - albeit with less computational power. tons of examples, manuals are outdated and unusable. PyTorch’s functionality and features make it more suitable for research, academic or personal projects. They have also released a prototype of the Pytorch Lite Interpreter which reduces the binary runtime size on mobile devices. x or 2. There's some evidence for PyTorch being the "researcher's" library - only 8% of papers-with-code papers use TensorFlow, while 60% use PyTorch. At the time of writing, Pytorch version was 1. 2. PyTorch Performance Comparison Experimental Setup. So, with this, we understood Jax Vs PyTorch Vs TensorFlow. Jul 16, 2021 · PyTorch and Tensorflow are deep learning libraries consisting of high-level APIs for modern methods in deep learning. However, PyTorch's longer tenure and larger community translate to more available resources for optimizing performance. Each of these frameworks has its own strengths, features, and use cases. PyTorch vs TensorFlow: Both are powerful frameworks with unique strengths; PyTorch is favored for research and dynamic projects, while TensorFlow excels in large-scale and production environments. Both these frameworks are powerful deep-learning tools. Not only is it also based in Python like PyTorch, but it also has a high-level neural net API that has been adopted by the likes of TensorFlow to create new architectures. Feb 12, 2024 · JAX also provides automatic differentiation using its `grad` function, which makes it easy to compute gradients of any function written in JAX. a large chunk of those that used both TensorFlow and Pytorch focused on this one rather than relearn the new TensorFlow. When choosing between TensorFlow and PyTorch, it’s essential to consider various factors. TensorFlow: looking ahead to Keras 3. Here, we compare both frameworks based on several criteria. 7. Aug 12, 2022 · Despite increasing competition from PyTorch and JAX, TensorFlow remains the most-used deep learning framework. They provide intuitive APIs and are beginner-friendly. It integrates well with Kubernetes and Docker and provides flexibility in updating and rolling back deployed models. Inspecting graphs using its jaxprs, etc. LSTM. Sep 29, 2020 · Disadvantages of Apache MXNet. Let's explore the key differences between them. TensorFlow, developed by Google Brain, is praised for its flexible and efficient platform suitable for a wide range of machine learning models, particularly deep neural networks. run(host=HOST, port=PORT_NUMBER) The Nov 6, 2023 · TensorFlow has improved with its eager execution mode, making it more accessible for newcomers. By restricting everything to be pure functions, jax can confidently trace and transform the function as it pleases while never exiting the function's This is mostly not true for tensorflow, except for massive projects like huggingface which make an effort to support pytorch, tensorflow, and jax. TensorFlow Lite is a set of tools that enables on-device machine learning by helping developers run their models on mobile, embedded, and IoT devices. We introduce a simple function torch. numpy. Both TensorFlow and PyTorch are phenomenal in the DL community. 043s. Let's look at the differences between Flax and TensorFlow from my The memory usage during the training of TensorFlow (1. If you have experience with ml, maybe consider using PyTorch. In this section, we will compare these Sep 12, 2023 · PyTorch and TensorFlow are both excellent tools for working with deep neural networks. JAX is based on the concept of “function transformations”. We extended TFDS to support TensorFlow-less NumPy-only data loading. . A few years ago when I was trying to pick this up to create some edge applications, PyTorch wasn't so much easier that it seemed worth sacrificing EdgeTPU support. On the other hand, in my day-to-day basis I use NumPy, a well-known library Feb 28, 2024 · Let's explore Python's two major machine learning frameworks, TensorFlow and PyTorch, highlighting their unique features and differences. 1. Tensorflow vs Pytorch vs Jax TensorFlow : Developed by Google Brain and released in 2015, TensorFlow is known for its robustness, scalability, and extensive feature list. Sci-kit learn deals with classical machine learning and you can tackle problems where the amount of training data is small. The whole purpose of the AD approaches in TensorFlow/PyTorch/Jax is for these constructs to be eliminated before the AD, so they have a much smaller surface of language support required. Jax (2018) is the latest to join the party and it represents a nice synthesis of this Jun 7, 2022 · However, although at first glance TensorFlow is easier to prototype with and deploy from, PyTorch seems to have advantages when it comes to quantization and to some GPU deployments. Omer March 26, 2020, 5:06pm 1. i. Feb 3, 2024 · Key benefits. On the other hand, PyTorch provides an API that is similar to NumPy’s API as well as TensorFlow’s API, which makes it easy to transition from either of these frameworks to PyTorch. Previews of PyTorch 2. 6 percent of developers, compared to just 1. As I am aware, there is no reason for this trend to reverse. the way to go if you are using TPUs. This means It is optional in TensorFlow, but required by JAX. You can view JAX as “numpy with backprop, XLA JIT, and GPU+TPU support”. Comparing the performance and speed of JAX and PyTorch, JAX works well on hardware accelerators such as GPUs and TPUs, leading to potentially faster performance in specific scenarios. The alternative the market offers is TensorFlow, which combined with Keras provides a powerful tool to create complex models. The main differences that changed with the new version of TensorFlow is that we don’t need tf. TensorFlow vs. Dynamic vs Static Graphs: PyTorch and scikit-learn use dynamic computational Feb 6, 2024 · While both PyTorch and Tensorflow offer deployment tools, Tensorflow generally has an edge in this aspect. So, it's definitely worth learning, even if it's only for the very elegant design. During the execution of a TensorFlow program, each operation is dispatched individually. from flask import Flask, request. 8. TensorFlow offers developers comprehensive tools and APIs that make machine learning easier to start with. Improvements, bug fixes, and other features take longer due to a lack of major community support. Both TensorFlow and PyTorch are powerful deep learning frameworks with their own strengths and use cases. This variance is significant for ML practitioners, who have to consider the Oct 3, 2023 · TFDS has always been framework-agnostic. Keras is another important deep learning framework that is worth considering. Jul 17, 2020 · GPU and CPU utilisation stats as well as corresponding code for both frameworks is found below. The two main components of TensorFlow Lite are an Nov 9, 2023 · PyTorch is an open-source framework for building machine learning and deep learning models for various applications, including natural language processing and machine learning. However, tensorflow still has way better material to learn from. 0 comments. 55) The PyTorch development stage is Mature (v. Add a Comment. Personally, I think Jax has a lot of really cool ideas, such as vmap and composable function transformations in general. TensorFlow is a deep learning library with a large ecosystem of tools and resources. The comparison between JAX and TenserFlow is as follows: Feature. x, which supports ‘Eager’ execution for a more PyTorch-like experience. Flax and TensorFlow are similar but different in some ways. 14K subscribers in the pytorch community. But now PyTorch seems much, much easier than it did then, while TensorFlow hasn't seemed to improve in ease-of-use. nn. Nov 5, 2020 · Exposing the Deep Learning model using Flask. Jul 5, 2023 · Google LLC, Public domain, via Wikimedia Commons. Supports Python and can be used with other languages such as Julia and Swift through its XLA compiler. It requires two parameters at initiation input_size and hidden_size. Another major change in TF2. Keras is built on top of TensorFlow, which makes it a wrapper for deep learning purposes. PyTorch: A Comparison. Apr 1, 2021 · Flax and JAX is by design quite flexible and expandable. data) are first-class citizens in our API by design. A similar trend is seen in 8 top AI journals. They are providing load and process data, training- reuse, and deploying models Jan 28, 2021 · TensorFlowでは6個、Pytorchでは7個importしました。 「. 5 GB RAM). This should be taken into consideration when kicking off a BERT-based project so that you don’t have to rebuild your codebase halfway through — like us. Performance. This is irrelevant to my work. Extending beyond the basic features, TensorFlow’s extensive community and detailed documentation offer invaluable resources to troubleshoot and enhance Sep 27, 2021 · 移行手順. - If you want to resolve vision related problems Dec 27, 2020 · MindSpor, Tensorflow, Pytorch are three frameworks that are providing machine learning capabilities to applications. 1. We used a long audio clip with more than 30 minutes to evaluate the performance of Whisper variants, with a PyTorch and JAX implementation. JAX is a new competitor of TensorFlow and PyTorch. We would like to show you a description here but the site won’t allow us. JAX Vs TensorFlow Vs PyTorch: A Comparative Analysis. It’s a Pythonic framework developed by Meta AI (than Facebook AI) in 2016, based on Torch, a package written in Lua. You write code like in numpy, but use the prefix jnp. In particular, it’s quite tightly integrated with its high-level API Keras, and its data loading library tf. linen で書き直す. You should first decide what kind of problems you want to solve and decide on classical machine learning vs deep learning. Supports Python and can be used with other languages such as C++ and JavaScript. JAX自体はNN学習に関するあれこれをサポートしていないので、それ用のライブラリを追加で利用する必要 There’s a reason PyTorch took off: tensorflow’s api and overall architecture sucks. Mar 18, 2024 · The decision between PyTorch vs TensorFlow vs Keras often comes down to personal preference and project requirements, but understanding the key differences and strengths of each is crucial. 0 and newer versions, more efficiency and convenience was brought to the game. Mar 2, 2022 · JAX is a compiler-oriented framework, which means that a compiler is responsible for transforming the Python functions into efficient machine code. 1 / 3. It currently builds models for iOS, ARM64, and Raspberry Pi. Take a look at the latest research repos and find a Tensorflow repo. Jul 27, 2020 · The answer is obviously “learn both” if you have all the time, resources, and mental energy in the world. Most of us don’t have such luxury. input_size and hidden_size correspond to the number of input features to the layer and the number of output features of that layer, respectively. Things look even worse for TF when you consider whether the people using Tensorflow are using Tensorflow 1. PyTorch - Since we say that JAX can be used for building and training deep learning models it's useful to understand how JAX stacks up against TensorFlow and PyTorch. We also need to look at the deployment considerations. tl;dr PyTorch’s Adam has consistently worse performance for the exact same setting and by worse performance I mean PyTorch’s models cannot be used for this particular application. 95%will translate to PyTorch. Industry experts may recommend TensorFlow while hardcore ML engineers may prefer PyTorch. bv dc ee hd wy qa cd jr hg tg