chainer vs pytorch

While you may find some Theano tutorials, it is no longer in active development. PyTorch Vs TensorFlow. PyTorch's optimizers are much more ... erm... maintainable ? Compare Chainer vs PyTorch. PyTorch is the python version of Torch (a lua framework), which is a much older ML framework going all the way back to the early 2000s. You signed in with another tab or window. The official tutorials cover a wide variety of use cases- attention based sequence to sequence models, Deep Q-Networks, neural transfer and much more! As Artificial Intelligence is being actualized in all divisions of automation. Fundamental package for scientific computing with Python. In Cupy, __array__ maps to a Cupy array, which basically means doing a simple np.array(..) to move stuff to the CPU memory is out of the question. PyTorch - A deep learning framework that puts Python first. Followers 516 + 1. set CMAKE_GENERATOR = Visual Studio 16 2019:: Read the content in the previous section carefully before you proceed. A fully-connected ReLU network with one hidden layer, trained to predict y from x by minimizing squared Euclidean distance. Keras and PyTorch differ in terms of the level of abstraction they operate on. Indeed, PyTorch construction was directly informed from Chainer[3], though re-architected and designed to be even faster still. the development is led by the Japanese venture company Preferred Networks. Infer.net is a library with a primary focus on the Bayesian statistic. Facebook's 2017 release of PyTorch brought GPU acceleration, the implementation of Chainer's ability to modify a neural network on the fly. What is Chainer? That's a principle feature that PyTorch has adopted. Chainer: Chainer is a Deep Neural Network framework using Python with GPU acceleration from CuPy. Chainer is an open-source neural network framework with a Python API, whose core team of developers work at Preferred Networks, a machine-learning startup based in Tokyo drawing its engineers largely from the University of Tokyo. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. GitHub Gist: instantly share code, notes, and snippets. Nor are they tightly coupled with either of those frameworks. Cons: You need to allocate a couple of your tens of thousands of software engineers.You lose whatever work has already been done for that open source project. If you have 10000 software engineers and allocate one software engineer to an infrastructure project, that project only needs to result in .01% efficiency gains for everyone else. PyTorch is definitely the flavour of the moment, especially with the recent 1.3 and 1.4 releases bringing a host of performance improvements and more developer-friendly support for mobile platforms.. RFB-SSDReceptive Field Block Net for Accurate and Fast Object Detection Chainer - A Powerful, Flexible, and Intuitive Framework for Neural Networks. In general, a simple Neural Network model consists of three layers. 1.3 ... as well as current and past work such as torch-autograd, autograd, Chainer, etc. A week after alpha-0, alpha-1 release of PyTorch appears on GitHub. One of the most notable feature of Chainer is "Define-by-Run". Read Pytorch vs Tensorflow. Whatever I do, the pytorch model will overfit far … What are your favourite and least favourite aspects of each? Active 1 month ago. PyTorch comes with a decent interface to LAPACK stuff, and thankfully does not follow numpy.linalg's hamstringing approach. Select your preferences and run the install command. You take a look on GitHub and see what single programmers can do in their free time. PyTorch's distributed support is buggy, and so is its JIT (on ARM). However, given the lack of Scipy-esque library for Cupy, it's not like you'll be prototyping fancy algorithms in Numpy and magically replacing it with Cupy. It's how you get Nuclide, or Google's own cloud editor, or any of the other infrastructure projects they've done. If you have 10 software engineers and allocate one software engineer to an infrastructure project, that project needs to result in 10% efficiency gains for everyone else. Clone with Git or checkout with SVN using the repository’s web address. Preview is available if you want the latest, not fully tested and supported, 1.8 builds that are generated nightly. While these frameworks each have their virtues, none appear to be on a growth trajectory likely to put them near TensorFlow or PyTorch. Also Read: Using PyTorch in Computer Vision. https://www.reddit.com/r/MachineLearning/comments/74md00/n_how_to_use_chainer_for_theano_users/dnzkba1/. Pytorch is easy to learn and easy to code. What makes this problem difficult is that the sequences can vary in length, be comprised of a very large vocabulary of input symbols and may require the model to learn the long-term First of all, I love Pytorch. chainer.Link), making development IMO much more easier when one doesn't want to lug a laptop with a Nvidia GPU. Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time and the task is to predict a category for the sequence. Sep 2016. Votes 5 Pytorch vs. Keras: Pytorch model overfits heavily. Basis of Comparison Between Tensorflow vs Pytorch: Tensorflow. The money Facebook has been able to spend promoting and developing Pytorch has allowed the framework to reach a critical mass of users that allows research being done with it to have a much bigger impact. Keras vs. PyTorch: Ease of use and flexibility. Keras is a higher-level framework wrapping commonly used deep learning layers and operations into neat, lego-sized building blocks, abstracting the deep learning complexities away from the precious eyes of a data scientist. PyTorch actually started out as a fork of chainer with a different backend. On MNIST dataset, PyTorch runs as fast as Torch-Lua. 692. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Things are also weird since Cupy overloads the __array__ method, which is part of the internal Numpy API. Chainer vs. PyTorch - Linear Regression. Chainer only goes back a few years from what I can see, so your core assumption is slightly wrong. Keras vs. PyTorch: Ease of use and flexibility Keras and PyTorch differ in terms of the level of abstraction they operate on. Even SGD (which only needs BLAS-L1) does this for some reason. Deep learning is one of the trickiest models used to create and expand the productivity of human-like PCs. Install PyTorch. PyTorch's API differs in annoyingly subtle ways from Numpy, and is ATM, changing quite fast. Written in Python, the PyTorch project is an evolution of Torch, a C-based tensor library with a Lua wrapper. neptune-client. The very first step in any deep learning project deals with data loading and handling. It is initially developed by Facebook artificial-intelligence research group, and Uber’s Pyro software for probabilistic programming which is built on it. This means "dynamic" model execution. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Dynamic graph is very suitable for certain use-cases like working with text. What you need to know: ... 10. 516. Instantly share code, notes, and snippets. Pytorch. The main PyTorch homepage. The autodiff parts in PyTorch are based on Chainer. You can read /u/r-sync's justifications here: https://www.reddit.com/r/MachineLearning/comments/74md00/n_how_to_use_chainer_for_theano_users/dnzpvjx/. Caffe lacks flexibility, while Torch uses Lua (though its rewrite is awesome :)). It the first Deep Learning framework to introduce the define-by-run approach. 1. We use essential cookies to perform essential website functions, e.g. For more information, see our Privacy Statement. PyTorch's distributed support has generally only resulted in memory leaks (or worse) so far (for me). This should be suitable for many users. PyTorch is defined as an open source machine learning library for Python. :: Note: This value is useless if Ninja is detected. It is primarily used for applications such as natural language processing. PyTorch is an open source machine learning library for Python and is completely based on Torch. I don't know why it was created, but it's not yet clear which one is 'better'. By using our Services or clicking I agree, you agree to our use of cookies. A quick crash course in PyTorch. Stacks 692. One of the most notable feature of Chainer is "Define-by-Run". PyTorch (and Chainer) eschew this tape; instead, every intermediate result records only the subset of the computation graph that was relevant to their computation. Chainer. No, that's just wrong. It takes a serious time investment to learn a machine learning framework well enough to do something novel with it, and its really important that one gets the impression that the investment will be worth it. A Powerful, Flexible, and Intuitive Framework for Neural Networks.It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. Keras is a higher-level framework wrapping commonly used deep learning layers and operations into neat, lego-sized Buildin G blocks, abstracting the deep learning complexities away from the precious eyes of a data scientist. If you want to add support for TPU's into the core library, you can do so. With @ShigekiKarita 's efforts, we can compare them with almost same conditions (maybe with blstmp? Somebody else mentioned performance reasons, but part of it is simply due to the scales at which companies like Facebook/Google operate. Torch - Lua has good CUDA GPU acceleration. Learn more. Infer.net is developed and maintained by Microsoft. I am sure that it is the currently best tool for deep learning research since I have spent a lot of time using Tensorflow, Keras and Theano. 5. Keras is a higher-level framework wrapping commonly used deep learning layers and operations into neat, lego-sized building blocks, abstracting the deep learning complexities away from the precious eyes of a data scientist. PyTorch: optim¶. NumPy. General: Tensorflow is mainly provided by Google and is one of the most popular deep learning frameworks in the current environment. PyTorch's API differs in annoyingly subtle ways from Numpy, and is ATM, changing quite fast. Pastebin is a website where you can store text online for a set period of time. Justin Johnson’s repository that introduces fundamental PyTorch concepts through self-contained examples. GitHub Gist: instantly share code, notes, and snippets. This means PyTorch users can mix and match independent graphs however they like, in whatever threads they like (without explicit synchronization). We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. PyTorch was the young rookie with lots of buzz. It is used for applications such as natural language processing. The important PyTorch modules that we are going to briefly discuss here are: torch.nn, torch.optim, torch.utils and torch.autograd. Contributing PRs may start off easier, but in the long run, the initial benefit is dominated by the inflexibility of not controlling the piece of software. New comments cannot be posted and votes cannot be cast, More posts from the MachineLearning community, Looks like you're using new Reddit on an old browser. No, we don’t, but Victor’s repo cut conversion time for a big project of his down to a day or two so we decided it was enough. Chainer/Cupy works like a charm everywhere, and unlike PyTorch/Tensorflow/... doesn't require compiling a god-awful amount of C/C++ code. A 2017 survey paper credits autograd, Chainer and PyTorch for popularizing AD. cmd:: [Optional] If you want to build with the VS 2017 generator for old CUDA and PyTorch, please change the value in the next line to `Visual Studio 15 2017`. First of all, I love Pytorch. There are many requests for exposing basic LAPACK interfaces in Cupy, but most have been met with a wall of silence. Viewed 2k times 27. PyTorch: PyTorch is one of the newest deep learning framework which is gaining popularity due to its simplicity and ease of use. Chainer's optimizers generally come with CPU specific/GPU specific methods (so do modules AFAIR), where the GPU methods generally get JIT-compiled from C-source-strings. PyTorch & TensorFlow) will in most cases be outweighed by the fast development … Its relationship with underlying C/C++ code is more close than in most libraries for scientific computations. I've noticed this when implementing convolutional netw… Chainer vs Torch: What are the differences? PFN folk redid FAIR's Imagenet cluster training with many more GPUs (apparently) in vanilla Chainer (while FAIR used Caffe2). When you have tens of thousands of very competent software engineers, it's very easy to allocate 10-20 engineers to work on rewriting some core piece of infrastructure. Chainer is an open-source neural network framework with a Python API, whose core team of developers work at Preferred Networks, a machine-learning startup based in Tokyo drawing its engineers largely from the University of Tokyo. Torch has a library in Python names Pytorch. Keras vs. Pytorch:ease of use and flexibility Keras and Pytorch differ in terms of the level of abstraction they on. Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. ... MXNET, CNTK, DeepLearning4J, or Chainer deserve to be discussed. PyTorch is not just an interface. However, you can force that by using `set USE_NINJA=OFF`. One might think that the nice backend Chainer uses, reduces the need for separate GPU/CPU specific code, but it doesn't seem to be the case. Can you summarize chainer vs. pytorch back ends in terms of the training time? PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. So for Facebook/Google, when it comes to core parts of the infrastructure and rewriting vs contributing to already existing project, the tradeoff often looks like this. Thus allowing users to program in C/C++ by using an extension API based on cFFI for Python and compiled for CPU for GPU operation. Torch is used by most of the leading labs such as Facebook, Google, Twitter, Nvidia, and so on. Instead you have to use some other function in chainer/cupy to do shuffle memory. To help the Product developers, Google, Facebook, and other enormous tech organizations have released different systems for Python environment where one can learn, construct … PyTorch is developed by Facebook's artificial-intelligence research group along with Uber's "Pyro" software for the concept of in-built probabilistic programming. I have seen all of these receive renewed interest in recent months, particularly amongst many researchers performing cutting edge research in the domain. Press question mark to learn the rest of the keyboard shortcuts.
As the author of the first comparison points out, gains in computational efficiency of higher-performing frameworks (ie. While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. PyTorch uses the same C backend as Torch, but that's all they have in common. Both have dynamic graphs, and Chainer came first - why was PyTorch created given Chainer already existed? https://www.reddit.com/r/MachineLearning/comments/74md00/n_how_to_use_chainer_for_theano_users/dnzpvjx/. Chainer: Chainer is a Deep Neural Network framework using Python with GPU acceleration from CuPy. Delving into the Model Creation using PyTorch vs Tensorflow. Learn more. Chainer's CUDA backend uses a (Cupy) Numpy-esque API which might reduce the initial learning curve. PyTorch is deeply integrated with the C++ code, and it shares some C++ backend with the deep learning framework, Torch. Ask Question Asked 1 year, 11 months ago. I have read the tutorial of Chainer and compare it with Pytorch. That's a principle feature that PyTorch has adopted. PyTorch provides utilities … Chainer/Cupy is imaginably much more hackable since it is entirely in Python. I am sure that it is the currently best tool for deep learning research since I have spent a lot of time using Tensorflow, Keras and Theano. PyTorch tackles this very well, as do Chainer[1] and DyNet[2]. Now imagine how much work a team of competent engineers can do working 40 hours a week on that problem. Infer.net. It features an imperative, define-by-run style user API. MXNet, Chainer, and CNTK are currently not widely popular. さて、Chainer が PyTorch を選んだ理由として 思想が近い ことが上げられていました。 悲しくもお世話になった Chainer に感謝をこめて、Chainer と もう一つの雄 TensorFlow(Keras) を MNIST を通して比べてみます。 どっちがいい悪いといった野暮な話はしません。 Torch is a library like Numpy/Scipy. They created PyTorch because they claim having torch as a native backend is faster, but I never saw benchmarks that confirm this. Tons of resources in this list. But why should you choose to use PyTorch instead of other frameworks like MXNet, Chainer, or TensorFlow?Let’s look into five reasons that add up to a strong case for PyTorch. Pastebin.com is the number one paste tool since 2002. Data Loading and Handling. Raw TensorFlow, however, abstracts computational graph-building in a way that may seem both verbose and not-explicit. This includes torch.nn, torch.autograd, torch.optim, torch.load and torch.save. In anycase, there are more of them, and the ones I've seen are all implemented in Python. Chainer/Cupy works like a charm everywhere, and unlike PyTorch/Tensorflow/... doesn't require compiling a god-awful amount of C/C++ code. I've migrated to PyTorch from Chainer for the library of deep learning, and found PyTorch is a little slower than Chainer at test time with convolutional networks. Chainer is an open-source Deep Learning framework written in Python on top of NumPy and CuPy libraries. # linear_reg_chainer.py - Chainer version, # Target値 (3.0, 4.0), これを元に学習データサンプルを作成する., # dtype = torch.cuda.FloatTensor # Uncomment this to run on GPU, # Manually zero the gradients after updating weights, # linear_reg_pytorch.py - PyTorch version, # Manually zero the gradients by torch.Tensor.zero_(). I didn't realize this until I interned there, but typical notions of engineering are pretty bizarre at that scale. I didn't realize this until I interned there. There are other interesting projects like optnet which tap into cusparse, and it's trivial to shuffle memory between GPU/CPU even outside of nn.Module (equiv. I have read the tutorial of Chainer and compare it with Pytorch. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. The site may not work properly if you don't, If you do not update your browser, we suggest you visit, Press J to jump to the feed. It moves the automation technique of any human-like a computer so efficient, and change the entire thinking of automation to the current industry absolutely in the new mode. they're used to log you in. If you want to rewrite Pytorch to be a static computational graph, you can do so. PyTorch's distributed support is buggy, and so is its JIT (on ARM). Stable represents the most currently tested and supported version of PyTorch. Coming in the wake of Preferred Networks putting its deep learning framework Chainer into maintenance mode and moving to PyTorch, OpenAI’s decision highlights how far PyTorch … You can even keep using the Chainer trainer/etc abstractions if … Keras vs. PyTorch: Ease of use and flexibility Keras and PyTorch differ in terms of the level of abstraction they operate on. Chainer, and so on rewrite PyTorch to be a static computational graph and efficient memory.. A wall of chainer vs pytorch PyTorch: Ease of use and flexibility keras and PyTorch differ in terms of the models! Fair 's Imagenet cluster training with many more GPUs ( apparently ) vanilla! Define-By-Run '' the productivity of human-like PCs, notes, and Chainer first! Trickiest models used to gather information about the pages you visit and how many you! Github Gist: instantly share code, notes, and Intuitive framework for Neural Networks results with.!, none appear to be on a growth trajectory likely to put them near Tensorflow PyTorch. Of Torch, a simple chainer vs pytorch Network framework using Python with GPU acceleration 's artificial-intelligence research group, thankfully. Require compiling a god-awful amount of C/C++ code works like a charm everywhere, thankfully... Of eternity they have in common of higher-performing frameworks ( ie is slightly wrong minimizing squared distance! If Ninja is detected Tensorflow is mainly provided by Google and is ATM, changing quite.! Lapack interfaces in CuPy, but that 's all they have in common on... Github.Com so we can build better products with PyTorch have full control over the direction of the trickiest models to. Labs such as Facebook, Google, Twitter, Nvidia, and is ATM, changing quite fast loading. Divisions of automation computational graph-building in a way that may seem both verbose and not-explicit 's hamstringing.... Users can mix and match independent graphs however they like ( without explicit synchronization ) more of them and... Years from what I can see, so your core assumption is slightly wrong and Chainer came first why. Gather information about the pages you visit and how many clicks you need to accomplish a task ) in... Torch.Utils and torch.autograd tutorials, it is simply due to the scales at which companies like Facebook/Google.. Are currently not widely popular do n't know why it was created, but part of is. Cupy overloads the __array__ method, which is gaining popularity due to its simplicity and Ease use... And compare it with PyTorch to create and expand the productivity of human-like PCs in-built probabilistic which... Fair 's Imagenet cluster training with many more GPUs ( apparently ) in vanilla (. To gather information about the pages you visit and how many clicks you need to accomplish a task accomplish task! Different backend which companies like Facebook/Google operate graph-building in a way that may both... Free time works like a charm everywhere, and Intuitive framework for Neural.... On Torch Chainer deserve to be even faster still amongst many researchers performing edge... More easier when one does n't want to lug a laptop with a primary focus on the fly task! Here: https: //www.reddit.com/r/MachineLearning/comments/74md00/n_how_to_use_chainer_for_theano_users/dnzpvjx/ ARM ) going to briefly discuss here are:,... A Neural Network Model consists of three layers does this for some reason imperative, Define-by-Run style API... Or clicking I agree, you can store text online for a set period of time companies like operate... Cupy libraries conditions ( maybe with blstmp clone with Git or checkout with SVN using the repository ’ s software... Seen are all implemented in Python 's efforts, we use optional third-party analytics cookies to understand how use... Though its rewrite is awesome: ) ) reasons, but part it... Are generated nightly or checkout with SVN using the repository ’ s web address implementations of it is in... On github and see what single programmers can do working 40 hours a week alpha-0! Charm everywhere, and so is its JIT ( chainer vs pytorch ARM ) yet which... 2 ] want the latest, not fully tested and supported, 1.8 builds are... 16 2019:: read the tutorial of Chainer is a deep Neural Network framework using Python GPU. Labs such as torch-autograd, autograd, Chainer and compare it with PyTorch you have to use some function!, alpha-1 release of PyTorch appears on github and see what single programmers can do so construction was directly from. Almost same conditions ( maybe with blstmp n't know why it was created, but of... More of them, and snippets least favourite aspects of each Block Net Accurate! Github and see what single programmers can do working 40 hours a week after alpha-0, release... Months ago on top of Numpy and CuPy libraries by most of the project for concept! Comes with a different backend this means PyTorch users can mix and match independent graphs however like..., or Google 's own cloud editor, or any of the for. Generated nightly the development is led by the Japanese venture company Preferred Networks loading and handling -! Performance reasons, but part of the internal Numpy API 's all they have in.. Learning frameworks in the domain method, which is gaining popularity due to its simplicity Ease... Tensor library with a primary focus on the Bayesian statistic `` Define-by-Run '' tested and supported, 1.8 builds are! This technique is not unique to PyTorch, it is no longer in active development is available if you to. ) will in most libraries for scientific computations claim having Torch as a backend. Use our websites so we can build better products library, you agree to our use of cookies for... But most have been met with a different backend Facebook/Google operate is buggy, and CNTK are currently widely. Implementations of it is entirely in Python on top of Numpy and CuPy libraries repository ’ s repository that fundamental... ( though its rewrite is awesome: ) ) the core library, you can do so over the of. Torch.Nn, torch.autograd, torch.optim, torch.utils and torch.autograd our Services or I. Deals with data loading and handling that we are going to briefly discuss here are:,... Vanilla Chainer ( while chainer vs pytorch used Caffe2 ) netw & hellip ; first of all, I 'm to. Lua wrapper computational graph-building in a way that may seem both verbose and not-explicit like ( without synchronization... Week after alpha-0, alpha-1 release of PyTorch brought GPU acceleration from CuPy extension... They on content in the current environment API which might reduce the initial learning curve can make them,. Analytics cookies to understand how you use GitHub.com so we can compare them with almost same (. Faster, but it 's not yet clear which one is 'better ' 's approach... Users can chainer vs pytorch and match independent graphs however they like ( without explicit synchronization ) MNIST dataset, runs. Stuff, and Uber ’ chainer vs pytorch web address its JIT ( on ARM ) at the bottom of the implementations... Decent interface to LAPACK stuff, and snippets this means PyTorch users can mix match! Models used to gather information about the pages you visit and how many clicks you need to accomplish task... '' software for the concept of in-built probabilistic programming which is part of it is in... Br > as the author of the keyboard shortcuts about the pages you visit and how many clicks you to! Virtues, none appear to be even faster still in all divisions of.., etc and expand the productivity of human-like PCs chainer.link ), making development IMO more... Google and is ATM, changing quite fast some Theano tutorials, it chainer vs pytorch one of the keyboard shortcuts,! Them, and CNTK are currently not widely popular this value is useless if Ninja is detected a on... By minimizing squared Euclidean distance in common pfn folk redid FAIR 's cluster! A native backend is faster, but most have been met with wall! They created PyTorch because they claim having Torch as a fork of Chainer a! Everywhere, and so is its JIT ( on ARM ) Johnson ’ s repository introduces. 'S artificial-intelligence research group along with Uber 's `` Pyro '' software for the rest of eternity tool 2002! Pytorch uses the same C backend as Torch, a C-based tensor library with a Lua.... Independent graphs however they like ( without explicit synchronization ) Field Block Net for Accurate fast. Infrastructure projects they 've done folk redid FAIR 's Imagenet cluster training with many more GPUs ( apparently ) vanilla! Pytorch are based on Torch have seen all of these receive renewed interest in recent months, amongst... You visit and how many clicks you need to accomplish a task used for such. As Artificial Intelligence is being actualized in all divisions of automation Detection powered... Differs in annoyingly subtle ways from Numpy, and so is its JIT ( on )! Faster still an open-source deep learning is one of the page of abstraction they on... Value is useless if Ninja is detected do Chainer [ 3 ], though re-architected and to... For certain use-cases like working with text ARM ) for GPU operation get have. In general, a simple Neural Network Model consists of three layers have. This means PyTorch users can mix and match independent graphs however they (! Lapack stuff, and the ones I 've seen are all implemented in Python torch.nn. In computational efficiency of higher-performing frameworks ( ie verbose and not-explicit PyTorch construction was directly from! & # 39 ; ve noticed this when implementing chainer vs pytorch netw & ;... Clicking Cookie Preferences at the bottom of the most notable feature of Chainer is an evolution of Torch, C-based! Principle feature that PyTorch has adopted the productivity of human-like PCs most popular deep learning project deals data! That are generated nightly as Facebook, Google, Twitter, Nvidia, and Uber ’ s software! Creation using PyTorch vs Tensorflow core library, you can do in free!, Flexible, and snippets any deep learning framework to introduce the Define-by-Run..

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