Day 1: MATLAB Platform and Data Analytics

Introduction and Welcome - Andy Grace

Session Q&A slots: http://community.thingspeak.com/feedback Enter: first name, last name, comapny: comment/question

Text: (508) 433-3693

R2017b Pre-Release coming week of June 12, 2017

MATLAB Keynote - Roy Lurie

  • MATLAB is designed for engineers and scientists, not a general purpose programming system

  • MATLAB has apps for interactive exploration, and automated code generation for deployment on production systems

  • MATLAB makes data science easy and accessible for Engineers and Scientists

  • MATLAB Strategy Highlights

    • Live Editor

      • Good for algorithm exploration and publishing

      • Not just a place code runs, but a place to tell the story of what is happening

    • App Designer

      • Catching up to GUIDE

      • No comment about sluggish performance for even modest sized apps

    • Engineering Data Analytics

      • JSON

      • Tall arrays

      • Big data algorithms

      • Spark (and Hadoop) integration

    • Deep Learning

      • Existing deep learning tools are for programmers

      • MATLAB makes deep learning easy and accessible for Engineers

        • net = alexnet;

          • An already trained network readily available for download

      • R2017b is a major release for Deep Learning

        • World-Class Prediction Performance

          • CUA compiled with GPU coder

          • Much faster than TensorFlow and Caffe

        • New Algorithms

          • Image segmentation analysis is easier to do now

    • MATLAB makes Internet of Things easy and accessible fo Engineers and Scientists

      • New application called ThingSpeak

    • Online offerings

      • MatWorks cloud ecosystem is complete in 2017

        • MATLAB Drive

          • Akin to Microsoft Live, DropBox, Google Drive

        • MATLAB Online

          • Available to all named users

        • Mobile editor

MATLAB Platform Experience - Loren Dean

  • MATLAB Platform

    • 2015 - Architecture

    • 2016 - Design Points

    • 2017 - Workflows

      • Individual Workflow

        • Personal code

        • Single discpline

        • Single desktop or small cluster

      • Team Workflow

        • Multidisciplinary work

          • Engineering domain

          • Job function (IT, DevOps, ...)

        • Servers and databases

          • CI Server

          • Cloud

          • Large cluster

          • Databases

          • Data marts

          • Production system

      • Value Drivers in MATLAB similar to model-based design in Simulink

      • MathWorks Cloud Framework

        • Questions revolving around Amazon Web Services (AWS) / Microsoft Azure

          • Cloud Center is used to spin up a cluser on AWS

MATLAB Code Development Tools - John Glass

  • Live Editor

    • Creating Live Scripts

      • Equation Editor (R2016b)

      • Interactive Plots (R2016b/R2017a)

      • Editable Figures (R2016b/R2017a)

      • Functions in Scripts (R2016b)

        • Also available in plain-text editor

      • Function Assistance (R2017b)

      • Evaluation is faster, typing is smoother in R2017b

        • Auto and graphical merge

MATLAB Language - Dave Foti

  • Simplifying common programmint tasks

    • String Arrays (R2016b)

    • Library of new functions for string manipulation (R2016b)

    • Use of strings with double quotes (R2017a)

    • New cell array display (R2017b)

    • No file left behind (R2017b)

      • New help tip for adding a path to a file that isn't yet visible

  • Simplifying class and function authoring

    • Object property validation (R2017a)

      • Declaration properties

      • Function input arguments

      • Define multiple functions with different argument signatures

    • Defining code modules

      • Define interfaces

        • Not worry about helper functions being on the path

      • Defining dependencies

  • Connecting with other langauges

    • Interfaces

      • Use libraries from inside MATLAB

    • Engine

      • Embed MATLAB as a computation engine

    • C++

      • Building out C++ Engine API in R2017b

    • HTTP and RESTful web services

      • jsonencode and jsondecode (R2016b)

MATLAB Performance, Code Compatibility, and Code Testing - Vadim Teverovsky

  • Performance Update

    • R2017b Examples:

      • Support for AVX2 instructions

      • Improved performance of small arrays

      • Imrpoved try/catch performance

    • Faster MATLAB Startup after reboot on a representative machine

      • 39 sec (R2016a) -> 24.5 sec (R2016b) -> 14.5 sec (R2017a)

  • Upgrading to a New Release

    • Read release notes for potential issues

      • R2017a

        • Grouped by category

        • Can filter by incompatibility

    • Find these issues in your code (MAB 2016: The most time-consuming step)

      • R2017b

        • MATLAB Code Compatibility Report

          • Detect potential incompatibilities

    • Figure out how to update your code

    • Update your code

    • Test updated code

  • MATLAB Code Testing

    • Unit Testing

      • Unit Testing allows you to lock down the behavior of your code

      • Scales from simple to large-scale testing

      • Framework

        • Easy creation and execution of your tests

          • Write tests as scripts, functions, or classes

          • Industry-standard xUnit style

        • R2017b

          • Integration into Live Script

    • Performance Testing

    • Mocking Framework

      • R2017a

      • Isolate a portion of a system to test by imitating behavior of dependencies

    • App Testing Framework

      • App Designer apps only

Scaling MATLAB Applications from Desktop to HPC - Jos Martin

  • Context for Scalability

    • Problem Sizes Grow

    • What to do?

      • More Machines

        • More system complexity

  • Scaling Data

    • Big data, GPU, Deep Learning

    • Datatypes

      • tall / Disks / Pre-processing, statistics, machine learning

      • distributed / Cluster / Sparse and dense numerics

      • gpuArray

    • GPU-Accelerated Deep Learning

      • R2017b - CPU Coder

    • Set training options to specify multi-gpu/parallel

  • Scaling Compute

    • Standard method: parfor & parfeval

    • R2017a - Interactive Parallel Programming

      • Problem:

        • You have a long running paralle program

      • Solution: DataQueue

    • Options

      • Parallel Computing Toolbox

      • MATLAB Parallel Cloud

      • Cloud Center

        • Virtual Private Cloud Support

        • GPU Clusters (P2)

      • Custom Cloud

      • MATLAB Distributed Computing Cluster

  • Scaling Simulink Workflows

    • Running many simulations

      • Traditional method: parfor + sim

      • New parsim in R2017a

        • Setup simulation inputs

        • Run simulation in parallel using parsim

      • R2017b

        • Live streaming of simulation results in Simulation Data Inspector

        • Simulation Manager

Deploying MATLAB Code in Embedded Systems - Fred Smith

  • MATLAB Coder - The Most Interesting App in the World

    • Generates embeddable C code

      • ANSI C

    • Trend

      • Performance

        • GPU

        • SIMD

        • Multi-core

      • Algorithms

        • Machine Learning

        • Computer Vision

      • Integration

        • OpenCV

        • AUTOSAR

        • Your code

    • R2017a

      • Algorithms: Dynamic memory allocation

      • Performance: Loop invariant code motion

      • Performance; Fast FFT

    • R2017b

      • Performace: CPU Coder

        • Utilizes code for fast deep learning performance

Deploying MATLAB Code for Production Use - Jim Stewart

  • Application deployment

    • Share code with other MATLAB users

      • Create your toolboxes and share with others using Toolbox Packaging

        • Package toolboxes as a single installer file

        • Automatic inclusion of dependencies

    • Share code with non-MATLAB users

      • Package code as

        • Standaline executables

        • Excel add-ins

        • Big Data applications using MATLAB compiler

      • Integrate with

        • Applications written in other languages

      • MATLAB Compiler and MATLAB Compiler SDK

      • Deploy App Designer Web Apps on Intranet

        • Beta program in R2017a/R2017b

        • Provide a web-app server that can be installed on internal network

      • Deploy Big Data Applications

        • Apache Spark (R2016b)

    • Deploy into production systems with MATLAB Production Server

      • Deploy updates without server restarts

      • Downloadable Performance report (R2017a)

      • IT admins manage software using Dashboard (R2017a)

      • Operationalize your MATLAB analytics

      • Availability of Cloud Formation Scripts for AWS and Azure (R2017a)

    • MATLAB client for MATLAB Production Server

      • Algorithm and version management

Harware and Analytics in the IoT Workflow - Rob Purser

  • Pull it all together

    • IoT Challenges:

      • New technologies

        • Sensor analytics, cloud, machine learning, and more

    • Multidisciplinary teams

      • Engineers, solution architects, IT, DevOps, and more

    • Software in multiple places

      • Embedded, in the cloud, on desktops, in browser

    • Steps to pulling it all together

      • Start with an idea...Explore using MATLAB

        • Pysics-based models

        • Collect test data

          • Data acquisition and instrumentation

          • Cmaeras

      • Refine your idea with your team

        • Transform your idea to algorithm

      • Prototype with hardware

        • Code generation from MATLAB and Simulink

      • Validate with multiple IoT devices

        • Analytic IoT Platform

          • ThingSpeak

            • TMW's IoT analytic platform (R2015a)

              • Collect

              • Analyze

              • Act

      • Develop production online analytics

        • Can deploy algorithm to ThingSpeak, instead of running locally

      • Go to Production or Scale Up

    • Tools

      • Predictive Maintenance Toolbox

      • Sensor Fusion and Tracking Toolbox (R2017b)

MATLAB Data Analytics - Mary Ann Freeman

  • Predictive Maintenance

    • Modeling

      • First principles modeling

        • Physical models

        • Kalman filters

      • Data-drive modeling

        • Curve fitting

        • Machine learning

    • Generate failure data using Simulink

    • Features

      • Access data with readtable, datastore (R2016b)

      • Preprocessing with timetable, tall (R2017a)

      • Work with Simulink logged output (R2017a)

      • Explore graphically (R2017a)

      • Machine learning models (R2017b)

  • Deep Learning

    • Deep learning is a neural network that learns directly from the data

      • Deep learning model can extract features automatically to classify objects

      • Deep learning is transforming machine learning

        • Data

          • Computer vision, text, and speech

          • Recognition fields revolutionized

        • Key projects

          • Tensorflow, caffe, et al

        • Issue

          • Difficult to learn and apply

    • Deep learning enablers

      • Massive sets of labeled data

      • Acceleration with GPUs

  • Topics to come

    • Data handling: time-stamped & big data

    • Deep learning & machine learning

    • App building & web apps

Time-Stampled Data Handling with Timetables - Nick Haddad

  • timetable overview (R2016b)

    • Used in accessing and exploring & analysis

    • table2timetable

    • timetable flows seemlessly in functions such as

      • rmmissing

      • filloutliers

      • smoothdata

    • synchronize is used to synchronize data

    • Import timetable into Simulation Data Inspector (R2017b)

    • Signal Analyzer App (R2017a)

  • Comparision with timeseries

    • timeseries

    • timetable

  • timetable Roadmap

    • MATLAB end-toend workflows

    • Adoption across products

    • Collections

    • Events

    • Time-based preprocessing

    • Visulization

Big Data Handling with Tall Arrays - Penny Anderson

  • Tall Arrays through R2017b

    • Overviews

      • Gives capability to work with data that doesn't otherwise fit in the computing machine's memory

      • Introduced in R2016b

      • Ideal for tabular/columnar data

      • Overall data size is too big to fit into memory

    • Functions specific to Tall Arrays

      • Access data with datastore and tall

      • Calculat statistics using gather

  • Roadmap for Tall Arrays

    • synchronize will work with Tall Arrays

    • ThingSpeak

    • Access: Write your own datastore (R2017b)

    • Machine Learning with Tall Arrays

      • R2016b

        • kmeans

        • fitlm

        • fitglm

        • fitcdiscr

        • cvpartition

      • R2017a

        • fitclinear

        • fitcnb

Machine Learning & Deep Learning with MATLAB - Jon Cherrie

  • Deep Learning with MATLAB

    • Pre-trained network: net = alexnet;

    • Inspect the network: net.Layers;

  • Topics

    • Easy to develop systems with machine learning & deep learning

      • Developing predictive models

        • Apps

          • Ground Truth Labeler (R2017a - Automated Driving System Toolbox)

          • Classification Learner (R2015a - Statistics and Machine Learning Toolbox)

          • Regression Learner (R2017a - Statics and Machine Learning Toolbox)

        • Model Optimization

          • Bayesian Optimization now built into many functions natively

        • Access to the Latest Research

          • Reference models

            • AlexNet, VGG-16, VGG-19

            • TensorFlow importer in the works

          • Caffe model importer

        • Deployment

    • Accessiclbe to non-experts

      • Tutorials - Deep Learning in 11-lines of MATLAB Code

    • High perfromance embedded implementation

      • Code generation workflow

        • Train your model

        • Implement a wrapper function

        • Generate code

      • Support for predictive models

        • R2017b

          • fitcdiscr

          • fitcknn

          • fitrsvm

          • fitrensemble

          • fitrgp

Text Analytics: Deriving Information from Text Data - Ben Hinkle

  • New product

    • Text Analytics Toolbox (R2017b)

      • extractFileText

      • eraseTags

      • removeWords

      • bagOfWords

      • predict

  • Applications

    • Document classification

      • Field reports

      • Bug reports

    • Sentiment analysis

      • Survey data

      • Trial notes

      • Social media

    • Predictive maintenance

      • Equipment log notes

      • Combine notes with numerica data

Deep Learning Deployment on FPGAs, CPUs, and GPUs - Girish Venkataramani

  • Deep learning workflow

    • Training

      • Definte network

      • Injest data

    • Deployment

      • Challenges

        • Fitting on embedded

          • Research DNNs are general

          • Application-specific DNN can be tailored to be much smaller

            • Re-architect network > weight compression > quantization to FP16, INT8

        • Scale performance on the platform of choice

          • NVIDIA GPU

            • R2017a

              • 14x faster than Caffe

              • 4x faster than TensorFlow

          • A deep learning (DL) coder infrastructure

            • GPU Coder > CUDA code (R2017b)

App Building Advancements and Web Apps - Chris Portal

  • Feedback (2014-2016)

    • Components

      • Parity with GUIDe

      • More Components + Richer Configurations

        • Layout Management

    • App Sharing

      • Run apps on Mobile and Browser platforms

    • App Designer

      • Resources for GUIDE to App Designer migration

  • Developments

    • Components

      • R2017a

        • Graphics Pan & Zoom

      • R2017b

        • 3D Plotting (App Designer)

        • UIMENU (App Designer)

        • UITREE (MATLAB Interface)

    • Graphics

      • R2017a

        • Heatmap

      • R2017b

        • Tall plot

    • Moving to the Web

      • End goal is targeting web-based dashboards and data-centric apps

      • Extending MATLAB platform across MATLAB Online & MATLAB Drive (R2017b)

Migrating GUIDE Work to App Designer - Jyoti Gandhe

  • Resources being developed to automate current workflow

    • fig2mlapp()

  • Migration workflow

    • Migrate Components & Callbacks (will be automated)

      • Lay out components

      • Configure component properties

      • Tag components with names

      • Wire callbacks to callback functions

      • Copy callback functions over

    • Update Code (will remain a manual process)

      • Use App Designer style code

      • Reconcile component API differences

      • Run migrated app to validate/troubleshoot

  • Roadmap

    • Migration tool will be available as an R2017b add-on

Breakout Sessions

MATLAB Testing: Frameworks and Workflows - Andy Campbell

  • TMW Developer Blog: blogs.mathworks.com

  • Visit their tech showcase for examples and demo of workflow

  • Unit test framework

    • Script-based/ function-based, class-based

      • TMW encourags exploration of script-based tests

  • Performance test framework

  • App test framework

  • Mocking framework

Continuous integration - Andy Campbell

  • Most users in the session are using Jenkins

    • Go to the blog about continuous integration

  • Integrations

    • TAPPlugin (Original format)

    • XMLPlugin (Junit format)

    • TAPPlugin (Version 13)

    • TestReportPlugin (html)

    • CodeCoveragePlugin (Cobertura output)

  • Support package for Jenkins

  • Streamlining online, cloud, etc

  • Simulink test support

    • Simulink Coverage

Typical workflow - Stephen Carter

  • Write > Run > Analyze

    • Write

      • Class-based, function-based, script-based

      • Live script compatibility (R2017b+)

      • Artifacts

        • FigureDiagnostic (R2017a)

        • ScreenshotDiagnostic (R2017a)

        • testCase.onFailure(@() doSomething()) convenience (R2017b)

    • Run

      • Formal interface (TestRunner)

    • Analyze

      • results = runner.run(suite); results(1).Details.DiagnosticRecord (R2016a)

      • HTML test report (R2017b)

Extensions & Plugins- David Hrushka

  • Extensions

    • Plugin output streams

    • Constraints & Tolerances

    • Fixtures

    • Diagnostics

  • Test Suite Hierarchy

  • Data Drive Testing

Mocking Framework - David Hrushka

  • Shipping since R2017ag

    • testCase.createMock(?Pet);

App Designer Test Tools - Steve McClure

  • Driving your app

    • Only works with human interaction, not programmatically modifying controls

    • Programmatic gestures

      • Press

      • Choose

      • Drag

      • Type

    • Guiding principles

      • Support testing interactions for what App-authors can do

        • Boost your code coverage

      • Realistic Gestures

        • Eg, respecting z-ordering

      • Isolate the App Under Test

        • App is locked out during tests

      • Interface Philosophy

        • Leveare a small set of functions with common and intuitive APIs

        • Emphasize the App-User rather than the App-Author

The MATLAB Performance Testing Framework - Andy Campbell

  • R2016a

    • runperf

    • Fixed time experiment

    • Variable sample time experiment using classical statistics

  • R2017a

    • Sample summary

    • Leverages the new samplefun function

  • R2017b

    • Multiple measurement points

      • testCase.startMeasuring;

      • testCase.stopMeasureing;

Upgrading to the Current Release: MATLAB, Simulink, and Code Generation - Narfi Stefansson/Richard Adame

  • Release Compatibility

    • Good internal MathWorks processes

      • Understand and reduce customer impact

      • Make chages deliberately

    • Tooling

      • Understand what changed in a release

      • Find the affected artifacts (code/models/files/...)

    • Documentation

      • Document product changes

      • Provide information on how to migrate affected artifacts

    • Test frameworks

      • Know that all changes have been addressed

  • Documentation

    • Release notes can now filter by range of releases

    • Release notes provide summary

    • Explain Differences between functions that are no longer recommended

    • Release Notes are concise

    • Migration documentation should describe:

      • Motivation for change

      • Functionality differences

      • How to update code/model/...

    • Should be accessible after original functionality has been removed

    • White-paper from TMW with all they know about upgrading

  • Upgrading MATLAB Code

    • Finding specific places where code changes are needed

      • Code Compatilibity Report (R2017b)

        • Analyzes your MATLAB codebase

        • Performs ~300 checks to locate incompatibilities

        • Creates report with recommendations

        • Also has a command-line version for integration with scripts

  • Simulink Upgrade Advisor

    • R2016b has a command-line API for automation of upgrade

    • R2017a can upgrade all models in a Simulink Project

    • R2017b has better handling of library blocks

    • Simulink Project Provides Encapsulation

  • Re-Using Generated Code

    • Production code integration across releases is a barrier to upgrading

      • Typical upgrade approaches

        • Manual integration: Difficult

          • Duplicate utilities

          • Non-functional changes

          • Large number of utilities

        • Generate new code for all components: costly re-verification

      • Automatically reuse previously verified code in new releases

        • Avoids re-verification of components

        • System-level integration

        • R2016b

          • Reuse existing shared code

            • Avoid generating duplicate utilities

            • Reduce integration merging

          • Automatically integrate model code

            • Import code from previous releases back to R2010a

          • Generate efficient integrated code

            • Cross-release code generation

    • Presentation - Day 3, Thursday Morning, 8:30am

      • Code Generation and Targeting Session

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