From 66da7baac7d6909e11e5f407a9cbbe0a5b4f9ca4 Mon Sep 17 00:00:00 2001 From: Wuod3n <33994335+Wuod3n@users.noreply.github.com> Date: Mon, 29 Jan 2018 22:50:32 +0800 Subject: [PATCH 1/3] Delete 20170915 Deep learning wars- Facebook-backed PyTorch vs Google-s TensorFlow.md --- ...k-backed PyTorch vs Google-s TensorFlow.md | 77 ------------------- 1 file changed, 77 deletions(-) delete mode 100644 sources/talk/20170915 Deep learning wars- Facebook-backed PyTorch vs Google-s TensorFlow.md diff --git a/sources/talk/20170915 Deep learning wars- Facebook-backed PyTorch vs Google-s TensorFlow.md b/sources/talk/20170915 Deep learning wars- Facebook-backed PyTorch vs Google-s TensorFlow.md deleted file mode 100644 index 31dbeb394b..0000000000 --- a/sources/talk/20170915 Deep learning wars- Facebook-backed PyTorch vs Google-s TensorFlow.md +++ /dev/null @@ -1,77 +0,0 @@ -Translating by Wuod3n -Deep learning wars: Facebook-backed PyTorch vs Google's TensorFlow -====== -The rapid rise of tools and techniques in Artificial Intelligence and Machine learning of late has been astounding. Deep Learning, or "Machine learning on steroids" as some say, is one area where data scientists and machine learning experts are spoilt for choice in terms of the libraries and frameworks available. A lot of these frameworks are Python-based, as Python is a more general-purpose and a relatively easier language to work with. [Theano][1], [Keras][2] [TensorFlow][3] are a few of the popular deep learning libraries built on Python, developed with an aim to make the life of machine learning experts easier. - -Google's TensorFlow is a widely used machine learning and deep learning framework. Open sourced in 2015 and backed by a huge community of machine learning experts, TensorFlow has quickly grown to be THE framework of choice by many organizations for their machine learning and deep learning needs. PyTorch, on the other hand, a recently developed Python package by Facebook for training neural networks is adapted from the Lua-based deep learning library Torch. PyTorch is one of the few available DL frameworks that uses tape-based autograd system to allow building dynamic neural networks in a fast and flexible manner. - -In this article, we pit PyTorch against TensorFlow and compare different aspects where one edges the other out. - -Let's get started! - -### What programming languages support PyTorch and TensorFlow? - -Although primarily written in C++ and CUDA, Tensorflow contains a Python API sitting over the core engine, making it easier for Pythonistas to use. Additional APIs for C++, Haskell, Java, Go, and Rust are also included which means developers can code in their preferred language. - -Although PyTorch is a Python package, there's provision for you to code using the basic C/ C++ languages using the APIs provided. If you are comfortable using Lua programming language, you can code neural network models in PyTorch using the Torch API. - -### How easy are PyTorch and TensorFlow to use? - -TensorFlow can be a bit complex to use if used as a standalone framework, and can pose some difficulty in training Deep Learning models. To reduce this complexity, one can use the Keras wrapper which sits on top of TensorFlow's complex engine and simplifies the development and training of deep learning models. TensorFlow also supports [Distributed training][4], which PyTorch currently doesn't. Due to the inclusion of Python API, TensorFlow is also production-ready i.e., it can be used to train and deploy enterprise-level deep learning models. - -PyTorch was rewritten in Python due to the complexities of Torch. This makes PyTorch more native to developers. It has an easy to use framework that provides maximum flexibility and speed. It also allows quick changes within the code during training without hampering its performance. If you already have some experience with deep learning and have used Torch before, you will like PyTorch even more, because of its speed, efficiency, and ease of use. PyTorch includes custom-made GPU allocator, which makes deep learning models highly memory efficient. Due to this, training large deep learning models becomes easier. Hence, large organizations such as Facebook, Twitter, Salesforce, and many more are embracing Pytorch. - -### Training Deep Learning models with PyTorch and TensorFlow - -Both TensorFlow and PyTorch are used to build and train Neural Network models. - -TensorFlow works on SCG (Static Computational Graph) that includes defining the graph statically before the model starts execution. However, once the execution starts the only way to tweak changes within the model is using [tf.session and tf.placeholder tensors][5]. - -PyTorch is well suited to train RNNs( Recursive Neural Networks) as they run faster in [PyTorch ][6]than in TensorFlow. It works on DCG (Dynamic Computational Graph) and one can define and make changes within the model on the go. In a DCG, each block can be debugged separately, which makes training of neural networks easier. - -TensorFlow has recently come up with TensorFlow Fold, a library designed to create TensorFlow models that works on structured data. Like PyTorch, it implements the DCGs and gives massive computational speeds of up to 10x on CPU and more than 100x on GPU! With the help of [Dynamic Batching][7], you can now implement deep learning models which vary in size as well as structure. - -### Comparing GPU and CPU optimizations - -TensorFlow has faster compile times than PyTorch and provides flexibility for building real-world applications. It can run on literally any kind of processor from a CPU, GPU, TPU, mobile devices, to a Raspberry Pi (IoT Devices). - -PyTorch, on the other hand, includes Tensor computations which can speed up deep neural network models upto [50x or more][8] using GPUs. These tensors can dwell on CPU or GPU. Both CPU and GPU are written as independent libraries; making PyTorch efficient to use, irrespective of the Neural Network size. - -### Community Support - -TensorFlow is one of the most popular Deep Learning frameworks today, and with this comes a huge community support. It has great documentation, and an eloquent set of online tutorials. TensorFlow also includes numerous pre-trained models which are hosted and available on [github][9]. These models aid developers and researchers who are keen to work with TensorFlow with some ready-made material to save their time and efforts. - -PyTorch, on the other hand, has a relatively smaller community since it has been developed fairly recently. As compared to TensorFlow, the documentation isn't that great, and codes are not readily available. However, PyTorch does allow individuals to share their pre-trained models with others. - -### PyTorch and TensorFlow - A David & Goliath story - -As it stands, Tensorflow is clearly favoured and used more than PyTorch for a variety of reasons. - -Tensorflow is vast, experienced, and best suited for practical purposes. It is easily the obvious choice of most of the machine learning and deep learning experts because of the vast array of features it offers, and most importantly, its maturity in the market. It has a better community support along with multiple language APIs available. It has a good documentation and is production-ready due to the availability of ready-to-use code. Hence, it is better suited for someone who wants to get started with Deep Learning, or for organizations wanting to productize their Deep Learning models. - -Although PyTorch is relatively newer and has a smaller community, it is fast and efficient. In short, it gives you all the power of Torch wrapped in the usefulness and ease of Python. Because of its efficiency and speed, it is a good option to have for small, research based projects. As mentioned earlier, companies such as Facebook, Twitter, and many others are using Pytorch to train deep learning models. However, its adoption is yet to go mainstream. The potential is evident, PyTorch is just not ready yet to challenge the beast that is TensorFlow. However considering its growth, the day is not far when PyTorch is further optimized and offers more functionalities - to the point that it becomes the David to TensorFlow's Goliath. - -### Savia Lobo -A Data science fanatic. Loves to be updated with the tech happenings around the globe. Loves singing and composing songs. Believes in putting the art in smart. - - --------------------------------------------------------------------------------- - -via: https://datahub.packtpub.com/deep-learning/dl-wars-pytorch-vs-tensorflow/ - -作者：[Savia Lobo][a] -译者：[译者ID](https://github.com/译者ID) -校对：[校对者ID](https://github.com/校对者ID) - -本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出 - -[a]:https://datahub.packtpub.com/author/savial/ -[1]:https://www.packtpub.com/web-development/deep-learning-theano -[2]:https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-keras -[3]:https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-tensorflow -[4]:https://www.tensorflow.org/deploy/distributed -[5]:https://www.tensorflow.org/versions/r0.12/get_started/basic_usage -[6]:https://www.reddit.com/r/MachineLearning/comments/66rriz/d_rnns_are_much_faster_in_pytorch_than_tensorflow/ -[7]:https://arxiv.org/abs/1702.02181 -[8]:https://github.com/jcjohnson/pytorch-examples#pytorch-tensors -[9]:https://github.com/tensorflow/models From b4df0ad86f0c4a828ed9866e12f10f2e0e8ab659 Mon Sep 17 00:00:00 2001 From: Wuod3n <33994335+Wuod3n@users.noreply.github.com> Date: Mon, 29 Jan 2018 22:53:30 +0800 Subject: [PATCH 2/3] Create 20170915 Deep learning wars: Facebook-backed PyTorch vs Google's TensorFlow --- ...book-backed PyTorch vs Google's TensorFlow | 109 ++++++++++++++++++ 1 file changed, 109 insertions(+) create mode 100644 translated/talk/20170915 Deep learning wars: Facebook-backed PyTorch vs Google's TensorFlow diff --git a/translated/talk/20170915 Deep learning wars: Facebook-backed PyTorch vs Google's TensorFlow b/translated/talk/20170915 Deep learning wars: Facebook-backed PyTorch vs Google's TensorFlow new file mode 100644 index 0000000000..a47aadcc9d --- /dev/null +++ b/translated/talk/20170915 Deep learning wars: Facebook-backed PyTorch vs Google's TensorFlow @@ -0,0 +1,109 @@ +深度学习战争：Facebook支持的PyTorch与Google的TensorFlow +====== +有一个令人震惊的事实,即人工智能和机器学习的工具和技术在近期迅速兴起。深度学习，或者说“注射了激素的机器学习”是数据科学家和机器学习专家在可用的库和框架方面被宠坏的一个领域。很多这样的框架都是基于Python的，因为Python是一个更通用，相对简单的语言。[Theano] [1]，[Keras] [2] [TensorFlow] [3]是一些基于Python构建的流行的深度学习库，目的是使机器学习专家更轻松。 + +Google的TensorFlow是一个广泛使用的机器学习和深度学习框架。 TensorFlow开源于2015年，得到了机器学习专家的广泛支持，TensorFlow已经迅速成长为许多机构为其机器学习和深度学习需求所选择的框架。 另一方面，PyTorch最近开发的用于训练神经网络的Python包被改编自基于Lua的深度学习库Torch。 PyTorch是少数可用的DL框架之一，它使用基于磁带的autograd系统（tape-based autograd system），以快速和灵活的方式构建动态神经网络。 + +在这篇文章中，我们将PyTorch与TensorFlow进行不同方面的比较。 + +让我们开始吧！ + +###什么编程语言支持 PyTorch和TensorFlow？ + +虽然主要是用C ++和CUDA编写的，但Tensorflow包含一个位于核心引擎上的Python API，使得更便于被Pythonistas使用。 除了Python，它还包括C ++，Haskell，Java，Go和Rust等其他API，这意味着开发人员可以用他们的首选语言进行编码。 + +虽然PyTorch是一个Python软件包，但你也可以提供使用基本的C / C ++语言的API进行编码。 如果你习惯使用Lua编程语言，你也可以使用Torch API在PyTorch中编写神经网络模型。 + +###PyTorch和TensorFlow有多么易于使用？ + +如果将TensorFlow作为一个独立的框架使用，它可能会有点复杂，并且会给深度学习模型的训练带来一些困难。 为了减少这种复杂性，可以使用位于TensorFlow复杂引擎之上的Keras封装，简化深度学习模型的开发和训练。 TensorFlow也支持PyTorch目前没有的[分布式培训] [4]。 由于包含Python API，TensorFlow也可以在生产环境中使用，即可用于培训练和部署企业级深度学习模型。 + +PyTorch由于Torch的复杂而被Python重写。 这使得PyTorch对于开发人员更为原生。 它有一个易于使用的框架，提供最大化的灵活和速度。 它还允许在训练过程中快速更改代码而不妨碍其性能。 如果你已经有了一些深度学习的经验，并且以前使用过Torch，那么基于它的速度，效率和易用性，你会更喜欢PyTorch。 PyTorch包含定制的GPU分配器，这使得深度学习模型具有高度的内存效率。 由此，训练大型深度学习模型变得更容易。 因此，Pytorch +在Facebook，Twitter，Salesforce等大型组织广受欢迎。 + +###用PyTorch和TensorFlow训练深度学习模型 + +PyTorch和TensorFlow都可以用来建立和训练神经网络模型。 + +TensorFlow在SCG（静态计算图）上工作，包括在模型开始执行之前定义静态图形。 但是，一旦开始执行，在模型内的调整更改的唯一方法是使用[tf.session and tf.placeholder tensors][5]。 + +PyTorch非常适合训练RNNs（递归神经网络），因为它们在[PyTorch] [6]中比在TensorFlow中运行得更快。 它适用于DCG（动态计算图），可以随时在模型中定义和更改。 在DCG中，每个模块可以单独调试，这使得神经网络的训练更简单。 + +TensorFlow最近提出了TensorFlow Fold，这是一个旨在创建TensorFlow模型的库，用于处理结构化数据。 像PyTorch一样，它实现了DCGs，在CPU上提供高达10倍的计算速度，在GPU上提供超过100倍的计算速度！ 在[Dynamic Batching] [7]的帮助下，你现在可以执行尺寸和结构都不相同的深度学习模型。 + +###GPU和CPU优化的比较 + +TensorFlow的编译时间比PyTorch短，为构建真实世界的应用程序提供了灵活性。 它可以从CPU，GPU，TPU，移动设备到Raspberry Pi（物联网设备）等各种处理器上运行。 + +另一方面，PyTorch包括张量（tensor）计算，可以使用GPU将深度神经网络模型加速到[50倍或更多] [8]。 这些张量可以停留在CPU或GPU上。 CPU和GPU都是独立的库， 无论神经网络大小如何，PyTorch都可以高效地利用。 + +###社区支持 + +TensorFlow是当今最流行的深度学习框架之一，由此也给它带来了庞大的社区支持。 它有很好的文档和一套详细的在线教程。 TensorFlow还包括许多预先训练过的模型，这些模型在[github] [9]上托管和提供。 这些模型提供给热衷于使用TensorFlow开发者和研究人员一些现成的材料来节省他们的时间和精力。 + +另一方面，PyTorch的社区相对较小，因为它最近才发展起来。 与TensorFlow相比，文档并不是很好，代码也不是很容易获得。 然而，PyTorch确实允许个人与他人分享他们的预训练模型。 + +### PyTorch和TensorFlow-力量悬殊的故事 + +就目前而言，由于各种原因，Tensorflow显然比PyTorch更受青睐。 + +Tensorflow很大，经验丰富，最适合实际应用。 是大多数机器学习和深度学习专家明显的选择，因为它提供了大量的功能，最重要的是它在市场上的成熟应用。 它具有更好的社区支持以及多语言API可用。 它有一个很好的文档库，由于从准备到使用的代码使之易于生产。 因此，它更适合想要开始深度学习的人，或者希望开发深度学习模型的组织。 + +虽然PyTorch相对较新，社区较小，但它速度快，效率高。 总之，它给你所有的优势在于Python的有用性和易用性。 由于其效率和速度，对于基于研究的小型项目来说，这是一个很好的选择。 如前所述，Facebook，Twitter等公司正在使用Pytorch来训练深度学习模型。 但是，使用它尚未成为主流。 PyTorch的潜力是显而易见的，但它还没有准备好去挑战这个TensorFlow的野兽。 然而，考虑到它的增长，PyTorch进一步优化并提供更多功能的日子并不遥远，直到与TensorFlow可以比较。 + +###Savia Lobo +非常喜欢数据科学。 喜欢更新世界各地的科技事件。 喜欢歌唱和创作歌曲。 相信才智上的艺术。 + +-------------------------------------------------------------------------------- + +via: https://datahub.packtpub.com/deep-learning/dl-wars-pytorch-vs-tensorflow/ + +作者：[Savia Lobo][a] +译者：[Wuod3n](https://github.com/Wuod3n) +校对：[校对者ID](https://github.com/校对者ID) + +本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出 + +[a]:https://datahub.packtpub.com/author/savial/ +[1]:https://www.packtpub.com/web-development/deep-learning-theano +[2]:https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-keras +[3]:https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-tensorflow +[4]:https://www.tensorflow.org/deploy/distributed +[5]:https://www.tensorflow.org/versions/r0.12/get_started/basic_usage +[6]:https://www.reddit.com/r/MachineLearning/comments/66rriz/d_rnns_are_much_faster_in_pytorch_than_tensorflow/ +[7]:https://arxiv.org/abs/1702.02181 +[8]:https://github.com/jcjohnson/pytorch-examples#pytorch-tensors +[9]:https://github.com/tensorflow/models + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 529ccdb5a97fadaeca213a91450c2b47243eab5b Mon Sep 17 00:00:00 2001 From: "Xingyu.Wang" Date: Mon, 29 Jan 2018 22:58:47 +0800 Subject: [PATCH 3/3] Rename 20170915 Deep learning wars: Facebook-backed PyTorch vs Google's TensorFlow to 20170915 Deep learning wars- Facebook-backed PyTorch vs Google-s TensorFlow.md --- ...rning wars- Facebook-backed PyTorch vs Google-s TensorFlow.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename translated/talk/{20170915 Deep learning wars: Facebook-backed PyTorch vs Google's TensorFlow => 20170915 Deep learning wars- Facebook-backed PyTorch vs Google-s TensorFlow.md} (100%) diff --git a/translated/talk/20170915 Deep learning wars: Facebook-backed PyTorch vs Google's TensorFlow b/translated/talk/20170915 Deep learning wars- Facebook-backed PyTorch vs Google-s TensorFlow.md similarity index 100% rename from translated/talk/20170915 Deep learning wars: Facebook-backed PyTorch vs Google's TensorFlow rename to translated/talk/20170915 Deep learning wars- Facebook-backed PyTorch vs Google-s TensorFlow.md