The Design and Implementation of Low-Latency Prediction Serving Systems
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Naiad: a Timely Dataflow System
Naiad: A Timely Dataflow System Derek G. Murray Frank McSherry Rebecca Isaacs Michael Isard Paul Barham Mart´ın Abadi Microsoft Research Silicon Valley {derekmur,mcsherry,risaacs,misard,pbar,abadi}@microsoft.com Abstract User queries Low-latency query are received responses are delivered Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers the high throughput Queries are of batch processors, the low latency of stream proces- joined with sors, and the ability to perform iterative and incremental processed data computations. Although existing systems offer some of Complex processing these features, applications that require all three have re- incrementally re- lied on multiple platforms, at the expense of efficiency, Updates to executes to reflect maintainability, and simplicity. Naiad resolves the com- data arrive changed data plexities of combining these features in one framework. A new computational model, timely dataflow, under- Figure 1: A Naiad application that supports real- lies Naiad and captures opportunities for parallelism time queries on continually updated data. The across a wide class of algorithms. This model enriches dashed rectangle represents iterative processing that dataflow computation with timestamps that represent incrementally updates as new data arrive. logical points in the computation and provide the basis for an efficient, lightweight coordination mechanism. requirements: the application performs iterative process- We show that many powerful high-level programming ing on a real-time data stream, and supports interac- models can be built on Naiad’s low-level primitives, en- tive queries on a fresh, consistent view of the results. abling such diverse tasks as streaming data analysis, it- However, no existing system satisfies all three require- erative machine learning, and interactive graph mining. -
ML Cheatsheet Documentation
ML Cheatsheet Documentation Team Sep 02, 2021 Basics 1 Linear Regression 3 2 Gradient Descent 21 3 Logistic Regression 25 4 Glossary 39 5 Calculus 45 6 Linear Algebra 57 7 Probability 67 8 Statistics 69 9 Notation 71 10 Concepts 75 11 Forwardpropagation 81 12 Backpropagation 91 13 Activation Functions 97 14 Layers 105 15 Loss Functions 117 16 Optimizers 121 17 Regularization 127 18 Architectures 137 19 Classification Algorithms 151 20 Clustering Algorithms 157 i 21 Regression Algorithms 159 22 Reinforcement Learning 161 23 Datasets 165 24 Libraries 181 25 Papers 211 26 Other Content 217 27 Contribute 223 ii ML Cheatsheet Documentation Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more. Warning: This document is under early stage development. If you find errors, please raise an issue or contribute a better definition! Basics 1 ML Cheatsheet Documentation 2 Basics CHAPTER 1 Linear Regression • Introduction • Simple regression – Making predictions – Cost function – Gradient descent – Training – Model evaluation – Summary • Multivariable regression – Growing complexity – Normalization – Making predictions – Initialize weights – Cost function – Gradient descent – Simplifying with matrices – Bias term – Model evaluation 3 ML Cheatsheet Documentation 1.1 Introduction Linear Regression is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope. It’s used to predict values within a continuous range, (e.g. sales, price) rather than trying to classify them into categories (e.g. cat, dog). There are two main types: Simple regression Simple linear regression uses traditional slope-intercept form, where m and b are the variables our algorithm will try to “learn” to produce the most accurate predictions. -
1.2 Le Zhang, Microsoft
Objective • “Taking recommendation technology to the masses” • Helping researchers and developers to quickly select, prototype, demonstrate, and productionize a recommender system • Accelerating enterprise-grade development and deployment of a recommender system into production • Key takeaways of the talk • Systematic overview of the recommendation technology from a pragmatic perspective • Best practices (with example codes) in developing recommender systems • State-of-the-art academic research in recommendation algorithms Outline • Recommendation system in modern business (10min) • Recommendation algorithms and implementations (20min) • End to end example of building a scalable recommender (10min) • Q & A (5min) Recommendation system in modern business “35% of what consumers purchase on Amazon and 75% of what they watch on Netflix come from recommendations algorithms” McKinsey & Co Challenges Limited resource Fragmented solutions Fast-growing area New algorithms sprout There is limited reference every day – not many Packages/tools/modules off- and guidance to build a people have such the-shelf are very recommender system on expertise to implement fragmented, not scalable, scale to support and deploy a and not well compatible with enterprise-grade recommender by using each other scenarios the state-of-the-arts algorithms Microsoft/Recommenders • Microsoft/Recommenders • Collaborative development efforts of Microsoft Cloud & AI data scientists, Microsoft Research researchers, academia researchers, etc. • Github url: https://github.com/Microsoft/Recommenders • Contents • Utilities: modular functions for model creation, data manipulation, evaluation, etc. • Algorithms: SVD, SAR, ALS, NCF, Wide&Deep, xDeepFM, DKN, etc. • Notebooks: HOW-TO examples for end to end recommender building. • Highlights • 3700+ stars on GitHub • Featured in YC Hacker News, O’Reily Data Newsletter, GitHub weekly trending list, etc. -
Alibaba Amazon
Online Virtual Sponsor Expo Sunday December 6th Amazon | Science Alibaba Neural Magic Facebook Scale AI IBM Sony Apple Quantum Black Benevolent AI Zalando Kuaishou Cruise Ant Group | Alipay Microsoft Wild Me Deep Genomics Netflix Research Google Research CausaLens Hudson River Trading 1 TALKS & PANELS Scikit-learn and Fairness, Tools and Challenges 5 AM PST (1 PM UTC) Speaker: Adrin Jalali Fairness, accountability, and transparency in machine learning have become a major part of the ML discourse. Since these issues have attracted attention from the public, and certain legislations are being put in place regulating the usage of machine learning in certain domains, the industry has been catching up with the topic and a few groups have been developing toolboxes to allow practitioners incorporate fairness constraints into their pipelines and make their models more transparent and accountable. AIF360 and fairlearn are just two examples available in Python. On the machine learning side, scikit-learn has been one of the most commonly used libraries which has been extended by third party libraries such as XGBoost and imbalanced-learn. However, when it comes to incorporating fairness constraints in a usual scikit- learn pipeline, there are challenges and limitations related to the API, which has made developing a scikit-learn compatible fairness focused package challenging and hampering the adoption of these tools in the industry. In this talk, we start with a common classification pipeline, then we assess fairness/bias of the data/outputs using disparate impact ratio as an example metric, and finally mitigate the unfair outputs and search for hyperparameters which give the best accuracy while satisfying fairness constraints. -
CLIPPER™ C100 32-Bit Compute Engine
INTErG?RH CLIPPER™ C100 32-Bit Compute Engine Dec. 1987 Advanced Processor Division Data Sheet Intergraph is a registered trademark and CLIPPER is a trademark of Intergraph Corporation. UNIX is a trademark of AT&T Bell Laboratories. Copyright © 1987 Intergraph Corporation. Printed in U.S.A. T CLIPPER !! C100 32-Bit Compute Engine Advance Information Table of Contents 7. Cache and MMU .......................... 40 7.1. Functional Overview ...................... 40 1. Introduction ............................... 1 7.2. Memory Management Unit (MMU) ........... 43 1.1. CPU .................................... 3 7.2.1. Translation Lookaside Buffer (TLB) .... 43 1 .1.1. Pipelining and Concurrency ............ 3 7.2.2. Fixed Address Translation ........... 46 1.1.2. Integer Execution Unit ................ 4 7.2.3. Dynamic Translation Unit (DTU) ....... 48 1.1.3. Floating-Point Execution Unit (FPU) ..... 4 7.3. Cache ................................. 51 1.1.4. Macro Instruction Unit ................ 5 7.3.1. Cache Une Description .............. 51 1.2. CAMMU ................................. 6 7.3.2. Cache Data Selection ............... 52 1.2.1. Instruction and Data Caches ........... 6 7.3.3. Prefetch .......................... 52 1.2.2. Memory Management Unit (MMU) ...... 6 7.3.4. Quadword Data Transfers ............ 53 1.3. Clock Control Unit ......................... 6 7.4. System Tag ............................ 53 2. Memory Organization ....................... 6 7.4.1. System Tags 0 - 5 .................. 53 2.1. Data Types .............................. 8 7.4.2. System Tag 6-Cache Purge ......... 54 3. Programming Model ........................ 8 7.4.3. System Tag 7-Slave 110 ........... 54 3.1. Register Sets ........................... 10 7.5. Bus Watch Modes ....................... 55 3.1.1. User and Supervisor Registers ........ 11 7.6. Internal Registers ........................ 56 3.1.2. -
Computer Architectures an Overview
Computer Architectures An Overview PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 25 Feb 2012 22:35:32 UTC Contents Articles Microarchitecture 1 x86 7 PowerPC 23 IBM POWER 33 MIPS architecture 39 SPARC 57 ARM architecture 65 DEC Alpha 80 AlphaStation 92 AlphaServer 95 Very long instruction word 103 Instruction-level parallelism 107 Explicitly parallel instruction computing 108 References Article Sources and Contributors 111 Image Sources, Licenses and Contributors 113 Article Licenses License 114 Microarchitecture 1 Microarchitecture In computer engineering, microarchitecture (sometimes abbreviated to µarch or uarch), also called computer organization, is the way a given instruction set architecture (ISA) is implemented on a processor. A given ISA may be implemented with different microarchitectures.[1] Implementations might vary due to different goals of a given design or due to shifts in technology.[2] Computer architecture is the combination of microarchitecture and instruction set design. Relation to instruction set architecture The ISA is roughly the same as the programming model of a processor as seen by an assembly language programmer or compiler writer. The ISA includes the execution model, processor registers, address and data formats among other things. The Intel Core microarchitecture microarchitecture includes the constituent parts of the processor and how these interconnect and interoperate to implement the ISA. The microarchitecture of a machine is usually represented as (more or less detailed) diagrams that describe the interconnections of the various microarchitectural elements of the machine, which may be everything from single gates and registers, to complete arithmetic logic units (ALU)s and even larger elements. -
A Rapid and Scalable Approach to Planetary Defense Against Asteroid Impactors
THE LEAGUE OF EXTRAORDINARY MACHINES: A RAPID AND SCALABLE APPROACH TO PLANETARY DEFENSE AGAINST ASTEROID IMPACTORS Version 1.0 NASA INSTITUTE FOR ADVANCED CONCEPTS (NIAC) PHASE I FINAL REPORT THE LEAGUE OF EXTRAORDINARY MACHINES: A RAPID AND SCALABLE APPROACH TO PLANETARY DEFENSE AGAINST ASTEROID IMPACTORS Prepared by J. OLDS, A. CHARANIA, M. GRAHAM, AND J. WALLACE SPACEWORKS ENGINEERING, INC. (SEI) 1200 Ashwood Parkway, Suite 506 Atlanta, GA 30338 (770) 379-8000, (770)379-8001 Fax www.sei.aero [email protected] 30 April 2004 Version 1.0 Prepared for ROBERT A. CASSANOVA NASA INSTITUTE FOR ADVANCED CONCEPTS (NIAC) UNIVERSITIES SPACE RESEARCH ASSOCIATION (USRA) 75 5th Street, N.W. Suite 318 Atlanta, GA 30308 (404) 347-9633, (404) 347-9638 Fax www.niac.usra.edu [email protected] NIAC CALL FOR PROPOSALS CP-NIAC 02-02 PUBLIC RELEASE IS AUTHORIZED The League of Extraordinary Machines: NIAC CP-NIAC 02-02 Phase I Final Report A Rapid and Scalable Approach to Planetary Defense Against Asteroid Impactors Table of Contents List of Acronyms ________________________________________________________________________________________ iv Foreword and Acknowledgements___________________________________________________________________________ v Executive Summary______________________________________________________________________________________ vi 1.0 Introduction _________________________________________________________________________________________ 1 2.0 Background _________________________________________________________________________________________ -
Programming Environments Manual for 32-Bit Implementations
Programming Environments Manual For 32- bit Implementations Of The Powerpctm Architecture Programming Environments Manual for 32-Bit Implementations. PowerPC™ Architecture (freescale.com/files/product/doc/MPCFPE32B.pdf) Some implementations of these architectures recognize data prefetch (6) The IA-32 Intel Architecture Software Developer's Manual, Volume 2: Instruction (14) PowerPC Microprocessor 32-bit Family: The Programming Environments, page. 1 /* 2 * Contains the definition of registers common to all PowerPC variants. used in the Programming Environments Manual For 32-Bit 6 * Implementations of the 0x4 /* Architecture 2.06 */ 353 #define PCR_ARCH_205 0x2 /* Architecture. x86 and x86_64. ARM32 and ARM64. POWER and PowerPC. MIPS32 Implementations may return false when they should have returned true but not vice versa. For shared-memory JS programming (if applicable) it will be natural to The ARMv8 manual states that 8 byte aligned accesses (in 64-bit mode). 5.1 32-bit PowerPC, 5.2 64-bit PowerPC, 5.3 Gaming consoles, 5.4 Desktop computers The RT was a rapid design implementing the RISC architecture. Programming Environments Manual for 32-bit Implementations of the PowerPC. IBM PowerPC Microprocessor Family. Vector/SIMD Multimedia Extension Technology Programming Environments Manual, 2005. 10. Intel Corporation. IA-32 Architectures Optimization Reference Manual, 2007. 11. Christoforos E. Technology: Architecture and Implementations, IEEE Micro, v.19 n.2, p.37-48, March 1999. Programming Environments Manual For 32-bit Implementations Of The Powerpctm Architecture Read/Download The 32-bit mtmsr is _ implemented on all POWER ISA compliant CPUs (see Programming Environments Manual for 64-bit Microprocessors _ _ Version 3.0 _ _ July 15, 2005. -
Microsoft Recommenders Tools to Accelerate Developing Recommender Systems Scott Graham, Jun Ki Min, Tao Wu {Scott.Graham, Jun.Min, Tao.Wu} @ Microsoft.Com
Microsoft Recommenders Tools to Accelerate Developing Recommender Systems Scott Graham, Jun Ki Min, Tao Wu {Scott.Graham, Jun.Min, Tao.Wu} @ microsoft.com Microsoft Recommenders repository is an open source collection of python utilities and Jupyter notebooks to help accelerate the process of designing, evaluating, and deploying recommender systems. The repository is actively maintained and constantly being improved and extended. It is publicly available on GitHub and licensed through a permissive MIT License to promote widespread use of the tools. Core components reco_utils • Dataset helpers and splitters • Ranking & rating metrics • Hyperparameter tuning helpers • Operationalization utilities notebooks • Spark ALS • Inhouse: SAR, Vowpal Wabbit, LightGBM, RLRMC, xDeepFM • Deep learning: NCF, Wide-Deep • Open source frameworks: Surprise, FastAI tests • Unit tests • Smoke & Integration tests Workflow Train set Recommender train Environment Data Data split setup load Validation set Hyperparameter tune Operationalize Test set Evaluate • Rating metrics • Content based models • Clone & conda-install on a local / • Chronological split • Ranking metrics • Collaborative-filtering models virtual machine (Windows / Linux / • Stratified split • Classification metrics • Deep-learning models Mac) or Databricks • Non-overlap split • Docker • Azure Machine Learning service (AzureML) • Random split • Tuning libraries (NNI, HyperOpt) • pip install • Azure Kubernetes Service (AKS) • Tuning services (AzureML) • Azure Databricks Example: Movie Recommendation -
Using the GNU Compiler Collection
Using the GNU Compiler Collection Richard M. Stallman Last updated 20 April 2002 for GCC 3.2.3 Copyright c 1988, 1989, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002 Free Software Foundation, Inc. For GCC Version 3.2.3 Published by the Free Software Foundation 59 Temple Place—Suite 330 Boston, MA 02111-1307, USA Last printed April, 1998. Printed copies are available for $50 each. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation; with the Invariant Sections being “GNU General Public License”, the Front-Cover texts being (a) (see below), and with the Back-Cover Texts being (b) (see below). A copy of the license is included in the section entitled “GNU Free Documentation License”. (a) The FSF’s Front-Cover Text is: A GNU Manual (b) The FSF’s Back-Cover Text is: You have freedom to copy and modify this GNU Manual, like GNU software. Copies published by the Free Software Foundation raise funds for GNU development. i Short Contents Introduction ...................................... 1 1 Compile C, C++, Objective-C, Ada, Fortran, or Java ....... 3 2 Language Standards Supported by GCC ............... 5 3 GCC Command Options .......................... 7 4 C Implementation-defined behavior ................. 153 5 Extensions to the C Language Family ................ 157 6 Extensions to the C++ Language ................... 255 7 GNU Objective-C runtime features.................. 267 8 Binary Compatibility ........................... 273 9 gcov—a Test Coverage Program ................... 277 10 Known Causes of Trouble with GCC ............... -
Encyclopedia of Windows
First Edition, 2012 ISBN 978-81-323-4632-6 © All rights reserved. Published by: The English Press 4735/22 Prakashdeep Bldg, Ansari Road, Darya Ganj, Delhi - 110002 Email: [email protected] Table of Contents Chapter 1 - Microsoft Windows Chapter 2 - Windows 1.0 and Windows 2.0 Chapter 3 - Windows 3.0 Chapter 4 - Windows 3.1x Chapter 5 - Windows 95 Chapter 6 - Windows 98 Chapter 7 - Windows Me Chapter 8 - Windows NT Chapter 9 - Windows CE Chapter 10 - Windows 9x Chapter 11 - Windows XP Chapter 12 - Windows 7 Chapter- 1 Microsoft Windows Microsoft Windows The latest Windows release, Windows 7, showing the desktop and Start menu Company / developer Microsoft Programmed in C, C++ and Assembly language OS family Windows 9x, Windows CE and Windows NT Working state Publicly released Source model Closed source / Shared source Initial release November 20, 1985 (as Windows 1.0) Windows 7, Windows Server 2008 R2 Latest stable release NT 6.1 Build 7600 (7600.16385.090713-1255) (October 22, 2009; 14 months ago) [+/−] Windows 7, Windows Server 2008 R2 Service Pack 1 RC Latest unstable release NT 6.1 Build 7601 (7601.17105.100929-1730) (September 29, 2010; 2 months ago) [+/−] Marketing target Personal computing Available language(s) Multilingual Update method Windows Update Supported platforms IA-32, x86-64 and Itanium Kernel type Hybrid Default user interface Graphical (Windows Explorer) License Proprietary commercial software Microsoft Windows is a series of software operating systems and graphical user interfaces produced by Microsoft. Microsoft first introduced an operating environment named Windows on November 20, 1985 as an add-on to MS-DOS in response to the growing interest in graphical user interfaces (GUIs). -
With Honors Final Year Peoject 1
FACULTY OF INFORMATICS AND COMPUTING BACHELOR OF COMPUTER SCIENCE (COMPUTER NETWORK SECURITY) WITH HONORS FINAL YEAR PEOJECT 1 CSF 35104 3RD YEAR 5TH SEMESTER SESSION 2018/2019 SPAM DETECTION USING MACHINE LEARNING BASED BINARY CLASSIFIER PREPARED BY : NOR SYAIDATUL AMIRAH BINTI ABDUL HAMID MATRIC NUMBER : BTBL16043660 SUPERVISOR : MR. AHMAD FAISAL AMRI BIN ABIDIN@BHARUN CHAPTER 1 INTRODUCTION 1.1 BACKGROUND Today, spam has become a big trouble over the internet. Up to 2017, the statistic shown spam accounted for 55%of all e-mail messages, same as during the previous year [1]. Spam which is also known as unsolicited bulk email has led to the increasing use of email as email provides the perfect ways to send the unwanted advertisement or junk newsgroup posting at no cost for the sender. This chances has been extensively exploited by irresponsible organizations and resulting to clutter the mail boxes of millions of people all around the world. Evolving from a minor to major concern, given the high offensive content of messages, spam is a waste of time. It also consumed a lot of storage space and communication bandwidth. End user is at risk of deleting legitimate mail by mistake. Moreover, spam also impacted the economical which led some countries to adopt legislation. Text classification is used to determine the path of incoming mail/message either into inbox or straight to spam folder. It is the process of assigning categories to text according to its content. It is used to organized, structures and categorize text. It can be done either manually or automatically. Machine learning automatically classifies the text in a much faster way than manual technique.