DOCSLIB.ORG

Mmlspark: Unifying Machine Learning Ecosystems at Massive Scales

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mmlspark: Unifying Machine Learning Ecosystems at Massive Scales MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales Mark Hamilton 1 Sudarshan Raghunathan 2 Ilya Matiach 3 Andrew Schonhoffer 3 Anand Raman 2 Eli Barzilay 1 Karthik Rajendran 4 5 Dalitso Banda 4 5 Casey Jisoo Hong 4 5 Manon Knoertzer 4 5 Ben Brodsky 2 Minsoo Thigpen 4 Janhavi Suresh Mahajan 4 Courtney Cochrane 4 Abhiram Eswaran 4 Ari Green 4 Abstract have proliferated. These different frameworks often have dramatically different APIs, data models, usage patterns, We introduce Microsoft Machine Learning for and scalability considerations. This heterogeneity makes Apache Spark (MMLSpark), an open-source li- it difficult to combine systems and complicates production brary that expands the Apache Spark distributed deployments. In this work, we present Microsoft Machine computing library to tackle problems in deep Learning for Apache Spark (MMLSpark), an ecosystem that learning, micro-service orchestration, gradient aims to unify major machine learning workloads into a sin- boosting, model interpretability, and other areas gle API for execution in a variety of distributed production of modern machine learning. We also present a grade environments and languages. We describe the tech- novel machine learning deployment system called niques and principles used to unify a representative sample Spark Serving that can deploy Apache Spark pro- of machine learning technologies, each with its own soft- grams as distributed, sub-millisecond latency web ware stack, communication requirements, and paradigms. services with significantly greater flexibility and We also introduce tools for deploying these technologies lower latencies than existing frameworks. Spark as distributed real-time web services. Code and documen- Serving generalizes beyond just map-style compu- tation for MMLSpark can be found through our website, tations and allows distributed aggregations, joins, https://aka.ms/spark. and shuffles and allows users to leverage the same cluster for both training and deployment. Our contributions allow easy composition across 2. Background machine learning frameworks, compute modes Throughout this work we build upon the distributed comput- (batch, streaming, and RESTful web serving) and ing framework Apache Spark (Zaharia et al., 2016). Spark cluster types (static, elastic, and serverless). We is capable of a broad range of workloads and applications demonstrate the value of MMLSpark by creating a such as fault-tolerant and distributed map, reduce, filter, and method for deep object detection capable of learn- aggregation style programs. Spark improves on its prede- ing without human labeled data and demonstrate cessors MapReduce and Hadoop by reducing disk IO with its effectiveness for Snow Leopard conservation. in memory computing, and whole program optimization We also demonstrate its ability to create large- (Dean & Ghemawat, 2008; Shvachko et al., 2010). Spark scale image search engines. clusters can adaptively resize to compute a workload effi- ciently (elasticity) and can run on resource managers such as Yarn, Mesos, Kubernetes, or manually created clusters. arXiv:1810.08744v2 [cs.LG] 21 Jun 2019 1. Introduction Furthermore, Spark has language bindings in several popu- As the field of machine learning has advanced, frameworks lar languages like Scala, Java, Python, R, Julia, C# and F#, for using, authoring, and training machine learning systems making it usable from almost any project. In recent years, Spark has expanded its scope to support *Equal contribution 1Microsoft Applied AI, Cambridge, Mas- sachusetts, USA 2Microsoft Applied AI, Redmond, Washing- SQL, streaming, machine learning, and graph style compu- ton, USA 3Microsoft Azure Machine Learning, Cambridge, Mas- tations (Armbrust et al., 2015; Meng et al., 2016; Xin et al., sachusetts, USA 4Microsoft AI Development Acceleration Pro- 2013). This broad set of APIs allows a rich space of compu- gram, Cambridge, Massachusetts, USA 5AUTHORERR: Miss- tations that we can leverage for our work. More specifically, ing nicmlaffiliation. Correspondence to: Mark Hamilton we build upon the SparkML API, which is similar to the <[email protected]>. popular Python machine learning library, scikit-learn (Buit- Proceedings of the 36 th International Conference on Machine inck et al., 2013). Like scikit-learn, all SparkML models Learning, Long Beach, California, PMLR 97, 2019. Copyright have the same API, which makes it easy to create, substitute, 2019 by the author(s). MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales and compose machine learning algorithms into “pipelines”. to distributed serving seamless and instant. However, SparkML has several key advantages such as Many companies such as Microsoft, Amazon, IBM, and limitless scalability, streaming compatibility, support for Google have embraced model deployment with web ser- structured datasets, broad language support, a fluent API vices to provide pre-built intelligent algorithms for a wide for initializing complex algorithms, and a type system that range of applications (Jackson et al., 2010; Microsoft,c; differentiates computations based on whether they extract High, 2012). This standardization enables easy use of cloud state (learn) from data. In addition, Spark clusters can use a intelligence and abstracts away implementation details, en- wide variety of hardware SKUs making it possible to lever- vironment setup, and compute requirements. Furthermore, age modern advances in GPU accelerated frameworks like intelligent services allow application developers to quickly Tensorflow, CNTK, and PyTorch (Abadi et al., 2016; Seide use existing state of the art models to prototype ideas. In the & Agarwal, 2016; Paszke et al., 2017). These properties Azure ecosystem, the Cognitive Services provide intelligent make the SparkML API a natural and principled choice to services in domains such as text, vision, speech, search, unify the APIs of other machine learning frameworks. time series, and geospatial workloads. Across the broader computing literature, many have turned to intermediate languages to “unify” and integrate disparate 3. Contributions forms of computation. One of the most popular of these lan- guages is the Hypertext Transfer Protocol (HTTP) used In this section we describe our contributions in three key ar- widely throughout internet communications. To enable eas: 1) Unifying several Machine Learning ecosystems with broad adoption and integration of code, one simply needs to Spark. 2) Integrating Spark with the networking protocol create a web-hosted HTTP endpoint or “service”. Putting HTTP and several intelligent web services. 3) Deploying compute behind an intermediate language allows different Spark computations as distributed web services with Spark system components to scale independently to minimize bot- Serving. tlenecks. If services reside on the same machine, one can These contributions allow users to create scalable machine use local networking capabilities to bypass internet data learning systems that draw from a wide variety of libraries transfer costs and come closer to the latency of normal func- and expose these contributions as web services for others tion dispatch. This pattern is referred to as a “micro-service” to use. All of these contributions carry the common theme architecture, and powers many of today’s large-scale appli- of building a single distributed API that can easily and cations (Sill, 2016). elegantly create a variety of different intelligent applications. Many machine learning workflows rely on deploying In Section4 we show how to combine these contributions learned models as web endpoints for use in front-end appli- to solve problems in unsupervised object detection, wildlife cations. In the Spark ecosystem, there are several ways to ecology, and visual search engine creation. deploy applications as web services such as Azure Machine Learning Services (AML), Clipper, and MLeap. However, 3.1. Algorithms and Frameworks Unified in these frameworks all compromise on the breadth of models MMLSpark they export, or the latency of their deployed services. AML deploys PySpark code in a dockerized Flask application that 3.1.1. DEEP LEARNING uses Spark’s Batch API on a single node standalone cluster To enable GPU accelerated deep learning on Spark, we have (Microsoft,a; Grinberg, 2018). Clipper uses on an interme- previously parallelized Microsoft’s deep learning frame- diate RPC communication service to invoke a Spark batch work, the Cognitive Toolkit (CNTK) (Seide & Agarwal, job for each request (Crankshaw et al., 2017). Both method 2016; Hamilton et al., 2018). This framework powers use Spark’s Batch API which adds large overheads. Further- roughly 80% of Microsoft’s internal deep learning work- more, if back-end containers are isolated, this precludes ser- loads and is flexible enough to create most models described vices with inter-node communication like shuffles and joins. in the deep learning literature. CNTK is similar to other MLeap achieves millisecond latencies by re-implementing automatic differentiation systems like Tensorflow, PyTorch, SparkML models in single threaded Scala, and exporting and MxNet as they all create symbolic computation graphs SparkML pipelines to this alternate implementation (Com- that automatically differentiate and compile to machine code. bust). This incurs a twofold development cost, a lag behind These tools liberate developers and researchers from the dif- the SparkML library, and a export limitation to models in ficult task of deriving training
Load more

Recommended publications
  • A Tensor Compiler for Unified Machine Learning Prediction Serving
    A Tensor Compiler for Unified Machine Learning Prediction Serving Supun Nakandala, UC San Diego; Karla Saur, Microsoft; Gyeong-In Yu, Seoul National University; Konstantinos Karanasos, Carlo Curino, Markus Weimer, and Matteo Interlandi, Microsoft https://www.usenix.org/conference/osdi20/presentation/nakandala This paper is included in the Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation November 4–6, 2020 978-1-939133-19-9 Open access to the Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation is sponsored by USENIX A Tensor Compiler for Unified Machine Learning Prediction Serving Supun Nakandalac,∗, Karla Saurm, Gyeong-In Yus,∗, Konstantinos Karanasosm, Carlo Curinom, Markus Weimerm, Matteo Interlandim mMicrosoft, cUC San Diego, sSeoul National University {<name>.<surname>}@microsoft.com,[email protected], [email protected] Abstract TensorFlow [13] combined, and growing faster than both. Machine Learning (ML) adoption in the enterprise requires Acknowledging this trend, traditional ML capabilities have simpler and more efficient software infrastructure—the be- been recently added to DNN frameworks, such as the ONNX- spoke solutions typical in large web companies are simply ML [4] flavor in ONNX [25] and TensorFlow’s TFX [39]. untenable. Model scoring, the process of obtaining predic- When it comes to owning and operating ML solutions, en- tions from a trained model over new data, is a primary con- terprises differ from early adopters in their focus on long-term tributor to infrastructure complexity and cost as models are costs of ownership and amortized return on investments [68]. trained once but used many times. In this paper we propose As such, enterprises are highly sensitive to: (1) complexity, HUMMINGBIRD, a novel approach to model scoring, which (2) performance, and (3) overall operational efficiency of their compiles featurization operators and traditional ML models software infrastructure [14].
    [Show full text]
  • Lightgbm Release 3.2.1.99
    LightGBM Release 3.2.1.99 Microsoft Corporation Sep 26, 2021 CONTENTS: 1 Installation Guide 3 2 Quick Start 21 3 Python-package Introduction 23 4 Features 29 5 Experiments 37 6 Parameters 43 7 Parameters Tuning 65 8 C API 71 9 Python API 99 10 Distributed Learning Guide 175 11 LightGBM GPU Tutorial 185 12 Advanced Topics 189 13 LightGBM FAQ 191 14 Development Guide 199 15 GPU Tuning Guide and Performance Comparison 201 16 GPU SDK Correspondence and Device Targeting Table 205 17 GPU Windows Compilation 209 18 Recommendations When Using gcc 229 19 Documentation 231 20 Indices and Tables 233 Index 235 i ii LightGBM, Release 3.2.1.99 LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: • Faster training speed and higher efficiency. • Lower memory usage. • Better accuracy. • Support of parallel, distributed, and GPU learning. • Capable of handling large-scale data. For more details, please refer to Features. CONTENTS: 1 LightGBM, Release 3.2.1.99 2 CONTENTS: CHAPTER ONE INSTALLATION GUIDE This is a guide for building the LightGBM Command Line Interface (CLI). If you want to build the Python-package or R-package please refer to Python-package and R-package folders respectively. All instructions below are aimed at compiling the 64-bit version of LightGBM. It is worth compiling the 32-bit version only in very rare special cases involving environmental limitations. The 32-bit version is slow and untested, so use it at your own risk and don’t forget to adjust some of the commands below when installing.
    [Show full text]
  • 2020 Sut20 CASC-J10.Pdf
    CASC-J10 CASC-J10 CASC-J10 CASC-J10 Proceedings of the 10th IJCAR ATP System Competition (CASC-J10) Geo↵Sutcli↵e University of Miami, USA Abstract The CADE ATP System Competition (CASC) evaluates the performance of sound, fully automatic, classical logic, ATP systems. The evaluation is in terms of the number of problems solved, the number of acceptable proofs and models produced, and the average runtime for problems solved, in the context of a bounded number of eligible problems chosen from the TPTP problem library and other useful sources of test problems, and specified time limits on solution attempts. The 10th IJCAR ATP System Competition (CASC-J10) was held on 2th July 2020. The design of the competition and its rules, and information regarding the competing systems, are provided in this report. 1 Introduction The CADE and IJCAR conferences are the major forum for the presentation of new research in all aspects of automated deduction. In order to stimulate ATP research and system de- velopment, and to expose ATP systems within and beyond the ATP community, the CADE ATP System Competition (CASC) is held at each CADE and IJCAR conference. CASC-J10 was held on 2nd July 2020, as part of the 10th International Joint Conference on Automated Reasoning (IJCAR 2020)1, Online, Earth. It was the twenty-fifth competition in the CASC series [139, 145, 142, 95, 97, 138, 136, 137, 102, 104, 106, 108, 111, 113, 115, 117, 119, 121, 123, 144, 125, 127, 130, 132]. CASC evaluates the performance of sound, fully automatic, classical logic, ATP systems.
    [Show full text]
  • Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters
    Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters Qinghao Hu1,2 Peng Sun3 Shengen Yan3 Yonggang Wen1 Tianwei Zhang1∗ 1School of Computer Science and Engineering, Nanyang Technological University 2S-Lab, Nanyang Technological University 3SenseTime {qinghao.hu, ygwen, tianwei.zhang}@ntu.edu.sg {sunpeng1, yanshengen}@sensetime.com ABSTRACT 1 INTRODUCTION Modern GPU datacenters are critical for delivering Deep Learn- Over the years, we have witnessed the remarkable impact of Deep ing (DL) models and services in both the research community and Learning (DL) technology and applications on every aspect of our industry. When operating a datacenter, optimization of resource daily life, e.g., face recognition [65], language translation [64], adver- scheduling and management can bring significant financial bene- tisement recommendation [21], etc. The outstanding performance fits. Achieving this goal requires a deep understanding of thejob of DL models comes from the complex neural network structures features and user behaviors. We present a comprehensive study and may contain trillions of parameters [25]. Training a production about the characteristics of DL jobs and resource management. model may require large amounts of GPU resources to support First, we perform a large-scale analysis of real-world job traces thousands of petaflops operations [15]. Hence, it is a common prac- from SenseTime. We uncover some interesting conclusions from tice for research institutes, AI companies and cloud providers to the perspectives of clusters, jobs and users, which can facilitate the build large-scale GPU clusters to facilitate DL model development. cluster system designs. Second, we introduce a general-purpose These clusters are managed in a multi-tenancy fashion, offering framework, which manages resources based on historical data.
    [Show full text]
  • A Universal Neural Network Solution for Tabular Data
    Under review as a conference paper at ICLR 2019 TABNN: A UNIVERSAL NEURAL NETWORK SOLUTION FOR TABULAR DATA Anonymous authors Paper under double-blind review ABSTRACT Neural Networks (NN) have achieved state-of-the-art performance in many tasks within image, speech, and text domains. Such great success is mainly due to special structure design to fit the particular data patterns, such as CNN captur- ing spatial locality and RNN modeling sequential dependency. Essentially, these specific NNs achieve good performance by leveraging the prior knowledge over corresponding domain data. Nevertheless, there are many applications with all kinds of tabular data in other domains. Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all. Without careful architecture design based on domain knowledge, it is quite chal- lenging for NN to reach satisfactory performance in these tabular data domains. To fill the gap of NN in tabular data learning, we propose a universal neural net- work solution, called TabNN, to derive effective NN architectures for tabular data in all kinds of tasks automatically. Specifically, the design of TabNN follows two principles: to explicitly leverage expressive feature combinations and to reduce model complexity. Since GBDT has empirically proven its strength in modeling tabular data, we use GBDT to power the implementation of TabNN. Comprehen- sive experimental analysis on a variety of tabular datasets demonstrate that TabNN can achieve much better performance than many baseline solutions. 1 INTRODUCTION Recent years have witnessed the extraordinary success of Neural Networks (NN), especially Deep Neural Networks, in achieving state-of-the-art performances in many domains, such as image clas- sification (He et al., 2016), speech recognition (Graves et al., 2013), and text mining (Goodfellow et al., 2016).
    [Show full text]
  • ISBN # 1-60132-514-2; American Council on Science & Education / CSCE 2021
    ISBN # 1-60132-514-2; American Council on Science & Education / CSCE 2021 CSCI 2021 BOOK of ABSTRACTS The 2021 World Congress in Computer Science, Computer Engineering, and Applied Computing CSCE 2021 https://www.american-cse.org/csce2021/ July 26-29, 2021 Luxor Hotel (MGM Property), 3900 Las Vegas Blvd. South, Las Vegas, 89109, USA Table of Contents Keynote Addresses .................................................................................................................... 2 Int'l Conf. on Applied Cognitive Computing (ACC) ...................................................................... 3 Int'l Conf. on Bioinformatics & Computational Biology (BIOCOMP) ............................................ 6 Int'l Conf. on Biomedical Engineering & Sciences (BIOENG) ................................................... 12 Int'l Conf. on Scientific Computing (CSC) .................................................................................. 14 SESSION: Military & Defense Modeling and Simulation ............................................................ 27 Int'l Conf. on e-Learning, e-Business, EIS & e-Government (EEE) ............................................ 28 SESSION: Agile IT Service Practices for the cloud ................................................................... 34 Int'l Conf. on Embedded Systems, CPS & Applications (ESCS) ................................................ 37 Int'l Conf. on Foundations of Computer Science (FCS) ............................................................. 39 Int'l Conf. on Frontiers
    [Show full text]
  • Download Vol 13, No 1&2, Year 2020
    The International Journal on Advances in Internet Technology is published by IARIA. ISSN: 1942-2652 journals site: http://www.iariajournals.org contact: [email protected] Responsibility for the contents rests upon the authors and not upon IARIA, nor on IARIA volunteers, staff, or contractors. IARIA is the owner of the publication and of editorial aspects. IARIA reserves the right to update the content for quality improvements. Abstracting is permitted with credit to the source. Libraries are permitted to photocopy or print, providing the reference is mentioned and that the resulting material is made available at no cost. Reference should mention: International Journal on Advances in Internet Technology, issn 1942-2652 vol. 13, no. 1 & 2, year 2020, http://www.iariajournals.org/internet_technology/ The copyright for each included paper belongs to the authors. Republishing of same material, by authors or persons or organizations, is not allowed. Reprint rights can be granted by IARIA or by the authors, and must include proper reference. Reference to an article in the journal is as follows: <Author list>, “<Article title>” International Journal on Advances in Internet Technology, issn 1942-2652 vol. 13, no. 1 & 2, year 2020, <start page>:<end page> , http://www.iariajournals.org/internet_technology/ IARIA journals are made available for free, proving the appropriate references are made when their content is used. Sponsored by IARIA www.iaria.org Copyright © 2020 IARIA International Journal on Advances in Internet Technology Volume 13, Number 1 & 2, 2020 Editors-in-Chief Mariusz Głąbowski, Poznan University of Technology, Poland Editorial Advisory Board Eugen Borcoci, University "Politehnica"of Bucharest, Romania Lasse Berntzen, University College of Southeast, Norway Michael D.
    [Show full text]
  • Information-11-00193-V2.Pdf
    information Review Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence Sebastian Raschka 1,*,† , Joshua Patterson 2 and Corey Nolet 2,3 1 Department of Statistics, University of Wisconsin-Madison, Madison, WI 53575, USA 2 NVIDIA, Santa Clara, CA 95051, USA; [email protected] (J.P.); [email protected] (C.N.) 3 Department of Comp Sci & Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA * Correspondence: [email protected] † Current address: 1300 University Ave, Medical Sciences Building, Madison, WI 53706, USA. Received: 6 February 2020; Accepted: 31 March 2020; Published: 4 April 2020 Abstract: Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it.
    [Show full text]
  • Proceedings of CASC-27 – the CADE-27 ATP System Competition
    CASC-27 CASC-27 CASC-27 CASC-27 Proceedings of CASC-27 – the CADE-27 ATP System Competition Geo↵Sutcli↵e University of Miami, USA Abstract The CADE ATP System Competition (CASC) evaluates the performance of sound, fully automatic, classical logic, ATP systems. The evaluation is in terms of the number of problems solved, the number of acceptable proofs and models produced, and the average runtime for problems solved, in the context of a bounded number of eligible problems chosen from the TPTP problem library and other useful sources of test problems, and specified time limits on solution attempts. The CADE-27 ATP System Competition (CASC-27) was held on 29th August 2019. The design of the competition and its rules, and information regarding the competing systems, are provided in this report. 1 Introduction The CADE and IJCAR conferences are the major forum for the presentation of new research in all aspects of automated deduction. In order to stimulate ATP research and system de- velopment, and to expose ATP systems within and beyond the ATP community, the CADE ATP System Competition (CASC) is held at each CADE and IJCAR conference. CASC-27 was held on 29th August 2019, as part of the 27th International Conference on Automated Deduction (CADE-27), in Natal, Brazil. It was the twenty-fourth competition in the CASC series [137, 143, 140, 95, 97, 136, 134, 135, 102, 104, 106, 108, 111, 113, 115, 117, 119, 121, 123, 142, 125, 127, 130]. CASC evaluates the performance of sound, fully automatic, classical logic, ATP systems. The evaluation is in terms of: the number of problems solved, • the number of acceptable proofs and models produced, and • the average runtime for problems solved; • in the context of: a bounded number of eligible problems, chosen from the TPTP problem library [128] and • other useful sources of test problems, and specified time limits on solution attempts.
    [Show full text]
  • Late Payment Prediction of Invoices Through Graph Features
    Late PAYMENT PREDICTION OF INVOICES THROUGH GRAPH FEATURES Master Thesis By: Arthur HoVANESYAN Late-payment prediction of invoices through graph features Arthur Hovanesyan to obtain the degree of Master of Science, Computer Science, Data Science Track at the Delft University of Technology, to be defended publicly on Wednesday December 18, 2019 at 12:30 PM. Student number: 4322711 Thesis committee: Associate Prof. dr. Pablo Cesar, TU Delft, (Chair) Assistant Prof. dr. Huijuan Wang, TU Delft (Supervisor) Assistant Prof. dr. Christoph Lofi, TU Delft Head Data Science. dr. Judith Redi, Exact (Supervisor) Preface This report concludes the thesis project and with it the two-year journey towards receiving my master’s de- gree in Computer Science. Thank you to Exact and especially to everyone in the Customer Intelligence/Data Science team for giving me the opportunity to work on such an awesome project. This was was not possible without the supervision of Dr. Judith Redi, Dr. Huijuan Wang, Dr. Bikash Joshi, Ir. Xiuxiu Zhan and of course everyone else from the team that chipped in and kept the morale high. Thank you for your guidance during the process. There are too many of you now to list. Thank you to my family and friends for your support throughout the journey. Without you all, this would not have been possible. Arthur Hovanesyan Delft, December 2019 iii Disclaimer The information made available by Exact for this research is provided for use of this research only and under strict confidentiality. v Abstract Keeping a steady cash flow is one of the biggest if not the biggest problem that Small to Medium Enterprises (SMEs) deal with daily.
    [Show full text]
  • Analytical and Other Software on the Secure Research Environment
    Analytical and other software on the Secure Research Environment The Research Environment runs with the operating system: Microsoft Windows Server 2016 DataCenter edition. The environment is based on the Microsoft Data Science virtual machine template and includes the following software: • R Version 4.0.2 (2020-06-22), as part of Microsoft R Open • R Studio Desktop 1.3.1093 working with R 4.0.2 • Anaconda 3, including an environment for Python 3.8.5 • Python, 3.8.5 as part of the Anaconda base environment • Jupyter Notebook, as part of the Anaconda3 environment • Microsoft Office 2016 Standard edition, including Word, Excel, PowerPoint, and OneNote (Access not included) • JuliaPro 0.5.1.1 and the Juno IDE for Julia • PyCharm Community Edition, 2020.3 • PLINK • JAGS • WinBUGS • OpenBUGS • stan and rstan • Apache Spark 2.2.0 • SparkML and pySpark • Apache Drill 1.11.0 • MAPR Drill driver • VIM 8.0.606 • TensorFlow • MXNet, MXNet Model Server • Microsoft Cognitive Toolkit (CNTK) • Weka • Vowpal Wabbit • xgboost • Team Data Science Process (TDSP) Utilities • VOTT (Visual Object Tagging Tool) 1.6.11 • Microsoft Machine Learning Server • PowerBI • Docker version 10.03.5, build 2ee0c57608 • SQL Server Developer Edition (2017), including Management Studio and SQL Server Integration Services (SSIS) • Visual Studio Code 1.17.1 • Nodejs • 7-zip • Evince PDF Viewer • Acrobat Reader • Microsoft Photo Viewer • PowerShell 6 March 2021 Version 1.2 And in the Premium research environments: • STATA 16.1 • SAS 9.4, m4 (academic license) Users also have the ability to bring in additional software if the software was specified in the data request, the software runs in the operating system described above, and the user can provide Vivli with any necessary licensing keys.
    [Show full text]
  • SONIC: Application-Aware Data Passing for Chained Serverless Applications
    SONIC: Application-aware Data Passing for Chained Serverless Applications Ashraf Mahgoub(a), Karthick Shankar(b), Subrata Mitra(c), Ana Klimovic(d), Somali Chaterji(a), Saurabh Bagchi(a) (a) Purdue University, (b) Carnegie Mellon University, (c) Adobe Research, (d) ETH Zurich Abstract and video analytics [6,27]. Accordingly, major cloud comput- ing providers recently introduced their serverless workflow Data analytics applications are increasingly leveraging services such as AWS Step Functions [5], Azure Durable serverless execution environments for their ease-of-use and Functions [48], and Google Cloud Composer [29], which pay-as-you-go billing. Increasingly, such applications are provide easier design and orchestration for serverless work- composed of multiple functions arranged in some workflow. flow applications. Serverless workflows are composed of a However, the current approach of exchanging intermediate sequence of execution stages, which can be represented as (ephemeral) data between functions through remote storage a directed acyclic graph (DAG) [21, 56]. DAG nodes corre- (such as S3) introduces significant performance overhead. spond to serverless functions (or ls1) and edges represent the We show that there are three alternative data-passing meth- flow of data between dependent ls (e.g., our video analytics ods, which we call VM-Storage, Direct-Passing, and state-of- application DAG is shown in Fig. 1). practice Remote-Storage. Crucially, we show that no single Exchanging intermediate data between serverless functions data-passing method prevails under all scenarios and the op- has been identified as a major challenge in serverless work- timal choice depends on dynamic factors such as the size flows [39, 41, 50].
    [Show full text]