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

大数据的影响就在我们身边，并使大量的商业服务成为可能。此外，它正在建立科学调查的第四范式，发现是基于挖掘数据，而不是通过观察验证的理论。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充满活力的研究活动。这个吗？快速Python深度学习基础设施？（RaPyDLI）项目推进深度学习（DL），这是一种新的令人兴奋的人工智能方法来解决大数据问题，其中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，自动驾驶汽车是这些领域的早期目标。DL使用一种非常通用的无偏方法来分析大型数据集，这些数据集受到大脑作为一组连接神经元的启发。与大脑一样，人工神经元从经验中学习，对应于一个？训练数据集？还有培训网？可以用来做决定。在语音上训练，DL网络可以增强语音识别，在图像上训练，DL网络可以识别图像中的对象。斯坦福大学参与者最近的一项研究在1000万张图像上训练了100亿个连接，以识别图像中的物体。这项研究涉及的数据集，大约是0.1%的数据大小？学会了？一个成年人一生中所存储的数字数据，以及当今世界存储的数字数据总量的十亿分之一。请注意，每天上传到社交媒体网站的15亿张图像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并通过提供方便的DL计算环境来增强DL，从而实现快速原型设计，即使用新算法进行交互式实验。这将使DL能够应用于更大的数据集，如那些？看见了吗？一个人的一生。RaPyDLI与印第安纳州大学、田纳西大学和斯坦福大学的合作伙伴关系使其能够利用并行计算算法和运行时、大数据、云和DL本身的专业知识实现这一目标。RaPyDLI将通过研讨会与DL从业者接触，收集对其软件的需求和反馈。此外，它将主动接触到代表性不足的社区与夏季经验和DL课程模块，包括示范建成？深度学习即服务？。RaPyDLI将构建为一组开源模块，可以从Python用户界面访问，但可以在最大的超级计算机或云上的C/C++或Java环境中互操作地执行，并进行交互式分析和可视化。RaPyDLI将支持GPU加速器和Intel 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，从而有利于研究和教育。

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.