Noviembre18, 2017 Concepción, Chile. #sqlsatconce

Transcription

1 Noviembre8, 27 Concepción, Chile #sqlsatconce

2 SQL Server 27 - Deep Learning, clasificación de imágenes usando Azure Data Science Virtual Machine Nombre Speaker: Adrián J. Fernandez Cargo : Especialista técnico en Datos e Inteligencia Artificial (Microsoft TSP Data & AI) Adrian.Fernandez@microsoft.com Blog: chile.pass.org

3 Patrocinadores del SQL Saturday SQL Saturday #684 Concepcion, Chile

4 Agenda Classifying galaxy using neural networks in the R language Data Science and Deep Learning Virtual Machine Demos: Galaxies classification / SQL Server R Services WW Telescope SQL Saturday #684 Concepcion, Chile

5 Microsoft R

6 Microsoft R Microsoft R Server family SQL Saturday #684 Concepcion, Chile

7 Microsoft R Server family From Data To Action On Premises and In the Cloud Data Sources People Apps Microsoft R Apps Windows SQL Server Sensors and devices Hadoop Teradata Linux Automated Systems DATA INTELLIGENCE ACTION

8 Microsoft R Server Scales Analytics to Big Data Scales via parallelization Scales via in-cluster execution Escapes R s memory limitations Reduces data movement & duplication Deploys into multiple platforms Windows, Linux SQL Server (R Services) Hadoop/Spark Teradata R Open R Server

9 ScaleR Parallel + Big Data Our ScaleR algorithms work inside multiple cores / nodes in parallel at high speed Stream data in to RAM in blocks. Big Data can be any data size. We handle Megabytes to Gigabytes to Terabytes XDF file format is optimised to work with the ScaleR library and significantly speeds up iterative algorithm processing. Interim results are collected and combined analytically to produce the output on the entire data set

10 Scale R Parallelized Algorithms & Functions Data Preparation Data import Delimited, Fixed, SAS, SPSS, OBDC Variable creation & transformation Recode variables Factor variables Missing value handling Sort, Merge, Split Aggregate by category (means, sums) Descriptive Statistics Min / Max, Mean, Median (approx.) Quantiles (approx.) Standard Deviation Variance Correlation Covariance Sum of Squares (cross product matrix for set variables) Pairwise Cross tabs Risk Ratio & Odds Ratio Cross-Tabulation of Data (standard tables & long form) Marginal Summaries of Cross Tabulations Statistical Tests Chi Square Test Kendall Rank Correlation Fisher s Exact Test Student s t-test Sampling Subsample (observations & variables) Random Sampling Predictive Models Sum of Squares (cross product matrix for set variables) Multiple Linear Regression Generalized Linear Models (GLM) exponential family distributions: binomial, Gaussian, inverse Gaussian, Poisson, Tweedie. Standard link functions: cauchit, identity, log, logit, probit. User defined distributions & link functions. Covariance & Correlation Matrices Logistic Regression Classification & Regression Trees Predictions/scoring for models Residuals for all models Variable Selection Stepwise Regression Simulation Simulation (e.g. Monte Carlo) Parallel Random Number Generation Cluster Analysis K-Means Classification Decision Trees Decision Forests Gradient Boosted Decision Trees Naïve Bayes Custom Development rxdatastep rxexec PEMA-R API Custom Algorithms

11 ScaleR: Dramatic Performance and Capacity

12 minutes In-Database Acceleration R Open on a server pulling data via SQL Microsoft R on a server Invoking MRS ScaleR Inside the EDW rows

13 Times faster than CRAN R MRS on Spark Compared to Open Source R HDInsight - Logistic Regression Comparisons Preliminary measure Number of rows (millions) rxlogit in HDInsight (Spark CC) CRAN R glm 5 Spark Nodes is 22X Faster (~25x/node) than One CRAN R node running GLM Configuration: HDI cluster size: 5 nodes - Edge node:d4 V2 (6 cores, 2GB) - Worker Nodes: D2 (4 cores, 28GB) Dataset: Airlines dataset (text format) Number of columns: 44

14 Times faster than local CC MRS on Spark Compared to MRS on Hadoop 25 rxlogit in HDInsight Spark 2 5 Preliminary measure R Server on Spark generally 6x faster than MapReduce but local is the speed champion for smaller files Number of rows (millions) MapReduce Local Configuration: HDI cluster size: 5 nodes - Edge node:d4 V2 (6 cores, 2GB) - Worker Nodes: D2 (4 cores, 28GB) Dataset: Airlines dataset Number of columns: 44

15 TIME (SECONDS) Scalability rxlogit on a node HDInsight cluster Preliminary measure 5E+9 E+ NUMBER OF ROWS Linear Scale out to 2 Billons Rows Configuration: HDI cluster size: nodes - Edge node:d4 V2 (6 cores, 2GB) - Worker Nodes: D2 (4 cores, 28GB) Dataset: NY City taxi trip dataset Number of columns:??

16 Machine Learning Templates With SQL Server R Services

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18 2 trillion galaxies in observable universe Galaxy shape tells us about evolution Spiral galaxies Elliptical galaxies NGC 325 M M5 Collisions and other events ESO 325-G4 Forming Ancient NASA, ESA, K. Kuntz (JHU), F. Bresolin (University of Hawaii), J. Trauger (Jet Propulsion Lab), J. Mould (NOAO), Y.-H. Chu (University of Illinois, Urbana), and STScI

19 How to classify galaxies? Professional astronomers Citizen data science Computer vision The Astronomer by Johannes Vermeer (Wikipedia)

20 How do computers see? Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations HonglakLee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng

21 What computers see Neural network Spiral Neural network Elliptical

22 What computers see Match pieces of the image Convolution Then repeat across the entire image Matches specific shape (kernel) across entire image Automatic feature generation

23 Deep Learning: Convolutional Neural Network 678c5b4b463?lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3BgQjKSKavROmoHrZL8fXioQ%3D%3D

24 Deep stacking Layers can be repeated several (or many) times. Spiral Convolution Convolution Pooling Pooling Elliptical

25 Galaxy Characterization via DNN Rapid Characterization of Celestial Bodies Uses New Microsoft ML Package Exploits GPU Acceleration Part of New Microsoft Investments in R Ease of Use Powerful Capability for R Users SQL Server, Windows, HPC, Batch Services

26 R Server inside SQL Server Call to remote SQL Server instance with R inside Fast linear learner (SDGA) Fast trees and forests One-class SVM Regularized logistic regression (L and L2) Neural networks

27 R code outline library library Load the required R packages

28 R code outline library(revoscaler) library(microsoftml) Load the required R packages multiclass Run the neural network rxneuralnet Neural networks for regression modeling and for Binary and multi-class classification. A character string denoting Fast Tree type: "binary" for the default binary classification neural network. "multiclass" for multi-class classification neural network. "regression" for a regression neural network.

29 R code outline library(revoscaler) library(microsoftml) model <- rxneuralnet( formula, data = galaxy_data, netdefinition = netdefinition, type = "multiclass" gpu 32 ) Load the required R packages Run the neural network Use GPU acceleration Getting started with GPU acceleration for MicrosoftML s rxneuralnet

30 R code outline library(revoscaler) library(microsoftml) model <- rxneuralnet( formula, data = galaxy_data, netdefinition = netdefinition, type = "multiclass" acceleration = "gpu", minibatchsize = 32 initwtsdiameter =., 5) Load the required R packages Run the neural network Use GPU acceleration Specify hyperparameters

31 R code outline library(revoscaler) library(microsoftml) model <- rxneuralnet( formula, data = galaxy_data, netdefinition = netdefinition, type = "multiclass" acceleration = "gpu", minibatchsize = 32 initwtsdiameter =., numiterations = 5) The Net# definition of the structure of the neural network NET#

32 Network definition #NET and Hidden Layers input [3, 5, 5] rlinear [3, 5, 5] 64 convolve Input images 64 maps [, 4, 4] response norm normalize [, 3, 3] max pool max pooling output [3] softmax all all fully connected output

33 Demo: Galaxy Image Classification using Deep Learning New Microsoft machine learning package with GPU-powered deep learning

34 Galaxy image classification

35 Demo Galaxies Classifier

36 Azure is the Microsoft cloud service Scalable computing, storage and services

37 R Server GPU support Train neural network using GPU on Azure GPU = Graphical processing unit x increase in speed

38 Deployment on Azure

39 Results ~K Training images 8 Layers in deep network 3 hours Computing time on Azure GPU 95% Overall accuracy - training data 8% Overall accuracy - test data The technique works, but has scope for improvement!

40 8% Overall accuracy on test data

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43 Deploy to SQL server Conclusion Convolutional neural nets can predict galaxy class You can use R Server to train and deploy a model Use Azure GPU machines for faster training

44 Demo WWT

45 A faster, more efficient, more intelligent cloud Data explosion: ZB ZB ML, DNN, AI are driving requirements up faster Autonomous decision making Real-time insights into connected devices Interactive user experiences Cloud-scale services Searches and recommendations (Indexing the Internet!) The need for SCALE The need for LOW-LATENCY The need for THROUGHPUT ZB 44 ZB Source: IDC 24

46 Azure AI Supercomputer

47 Silicon alternatives TRAINING CPUs and GPUs, limited FPGAs, ASICs under investigation EVALUATION CPUs and FPGAs, ASICs under investigation Registers Control Unit (CU) CPUs Arithmetic Logic Unit (ALU) GPUs FPGAs ASICs FLEXIBILITY EFFICIENCY

48 Visualization Virtual Machines Powered by NVIDIA GRID NV6 NV2 NV24 Cores GPU M6 GPU (/2 Physical Card) 2 M6 GPUs ( Physical Card) 4 M6 GPUs (2 Physical Cards) Memory 56 GB 2 GB 224 GB Disk ~38 GB SSD ~68 GB SSD ~.5 TB SSD Network Azure Network Azure Network Azure Network

49 Compute Azure Virtual Machines NC6 NC2 NC24 NC24r Cores GPU K8 GPU (/2 Physical Card) 2 K8 GPUs ( Physical Card) 4 K8 GPUs (2 Physical Cards) 4 K8 GPUs (2 Physical Cards) Memory 56 GB 2 GB 224 GB 224 GB Disk ~38 GB SSD ~68 GB SSD ~.5 TB SSD ~.5 TB SSD Network Azure Network Azure Network Azure Network InfiniBand

50 Sitio de la Comunidad en Chile chile.pass.org SQL Saturday #684 Concepcion, Chile

51 Sitio de la Comunidad Global SQL Saturday #684 Concepcion, Chile

52 Sea cual sea su pasión datos hay uncapítulo virtual para usted! SQL Saturday #684 Concepcion, Chile

53 Preguntas SQL Saturday #684 Concepcion, Chile

54 Gracias por vuestra asistencia!