XPS: FULL: DSD: Collaborative Research: Rapid Prototyping HPC Environment for Deep Learning

大数据的影响无处不在，并正在促成过多的商业服务。此外，它正在建立第四种科学调查范式，在这种范式中，发现是基于挖掘数据而不是通过观察证实的理论。大数据已经建立了一个新的学科(数据科学)，在计算机科学的几个领域开展了活跃的研究活动。这是什么？快速巨蟒深度学习基础设施？(RaPyDLI)项目提出了深度学习(DL)，这是一种解决大数据问题的新的令人兴奋的人工智能方法，它还涉及一个复杂的模型和相应的大计算？需要高端的超级计算机架构。DL已经在语音识别、药物发现和计算机视觉等领域取得了成功，在这些领域，自动驾驶汽车是早期的目标。DL使用一种非常普遍的、不偏不倚的方式来分析受大脑启发的大型数据集，作为一组连接的神经元。与大脑一样，人工神经元从与训练数据集相对应的经验中学习。那训练有素的电视网呢？可以用来做决定。在语音上进行训练，DL网络可以增强语音识别，在图像上进行训练，DL网络可以识别图像中的对象。斯坦福大学这个项目的参与者最近的一项研究训练了1000万张图像上的100亿个连接，以识别图像中的对象。这项研究涉及的数据集大约是学习到的数据大小的0.1%。由一个成年人一生和十亿分之一的数字数据存储在今天的世界上。请注意，每天上传到社交媒体网站的15亿张图片强调了大数据的惊人规模。该项目旨在通过允许其有效地使用大型超级计算机以及通过提供能够实现快速原型即新算法的交互实验的方便的数字图书馆计算环境来增强数字图书馆的能力。这将使DL能够应用于更大的数据集，如？所见？在他们有生之年被人类。由印第安纳大学、田纳西大学和斯坦福大学组成的RaPyDLI合作伙伴关系凭借在并行计算算法和运行时、大数据、云和DL本身方面的专业知识实现了这一点。RaPyDLI将通过研讨会联系DL从业者，收集对其软件的需求和反馈。此外，它将主动接触具有暑期经验的未被充分代表的社区和数字图书馆课程模块，其中包括构建为？深度学习即服务？的演示。RaPyDLI将构建为一组开源模块，可以从Python用户界面访问这些模块，但可以在C/C++或Java环境中通过交互分析和可视化在最大的超级计算机或云上互操作执行。RaPyDLI将支持GPU加速器和英特尔Phi协处理器，以及广泛的存储方法，包括文件、NoSQL、HDFS和数据库。RaPyDLI将包括基准测试和软件，并将提供一个存储库，以便用户可以为一系列神经网络贡献高级代码，从而为研究和教育带来好处。

The impact of Big Data is all around us and is enabling a plethora of commercial services. Further it is establishing the fourth paradigm of scientific investigation where discovery is based on mining data rather than from theories verified by observation. Big Data has established a new discipline (Data Science) with vibrant research activities across several areas of computer science. This ?Rapid Python Deep Learning Infrastructure? (RaPyDLI) project advances Deep Learning (DL) which is a novel exciting artificial intelligence approach to Big Data problems, which also involves a sophisticated model and a corresponding ?big compute? needing high end supercomputer architectures. DL has already seen success in areas like speech recognition, drug discovery and computer vision where self-driving cars are an early target. DL uses a very general unbiased way of analyzing large data sets inspired by the brain as a set of connected neurons. As with the brain, the artificial neurons learn from experience corresponding to a ?training dataset? and the ?trained network? can be used to make decisions. Trained on voices, the DL network can enhance voice recognition and trained on images, the DL network can recognize objects in the image. A recent study by the Stanford participants in this project trained 10 billion connections on 10 million images to recognize objects in an image. This study involved a dataset that was approximately 0.1% the size of data ?learnt? by an adult human in their lifetime and one billionth of the total digital data stored in the world today. Note the 1.5 billion images uploaded to social media sites every day emphasize the staggering size of big data. The project aims to enhance by DL by allowing it to use large supercomputers efficiently and by providing a convenient DL computing environment that enables rapid prototyping i.e. interactive experimentation with new algorithms. This will enable DL to be applied to much larger datasets such as those ?seen? by a human in their lifetime. The RaPyDLI partnership of Indiana University, University of Tennessee, and Stanford enables this with expertise in parallel computing algorithms and run times, big data, clouds, and DL itself.RaPyDLI will reach out to DL practitioners with workshops both to gather requirements for and feedback on its software. Further it will proactively reach out to under-represented communities with summer experiences and DL curriculum modules that include demonstrations built as ?Deep Learning as a Service?.RaPyDLI will be built as a set of open source modules that can be accessed from a Python user interface but executed interoperably in a C/C++ or Java environment on the largest supercomputers or clouds with interactive analysis and visualization. RaPyDLI will support GPU accelerators and Intel Phi coprocessors and a broad range of storage approaches including files, NoSQL, HDFS and databases. RaPyDLI will include benchmarks as well as software and will offer a repository so users can contribute the high level code for a range of neural networks with benefits to research and education.