The world of Machine Learning, Deep Learning and PowerAI

© IBM Corporation, 2016

The World of Machine Learning, Deep Learning

and PowerAI – Deeper Dive

Power Up Live with SCC – 6th June 2017

Presented by David Spurway

IBM Power Systems Product Manager

IBM Systems, UK and Ireland

2 © IBM Corporation, 2016

Augmented intelligence, Artificial Intelligence, Cognitive

driving innovation Faster


My friends at Uni…


From Big Data to AI client journey


OBSERVATION DECISIONINTERPRETATION EVALUATION

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PrescriptiveBest Outcomes?

DescriptiveWhat Has Happened?

CognitiveLearn Dynamically

PredictiveWhat Could Happen?


010101010101010111100010011001010111 0000000000010101010100000000000 11110101111000 000000000000 111111 010101 101010 10101010100

OBSERVATION DECISIONINTERPRETATION EVALUATION





ACTIONDATA

How many fraudsduring last month? Per Country ?

Which Transactions will be fraudulent ?

What is the best action in light of potential fraud ? In Natural Language

: « Explain me whythis transaction is

fraudulent ?






- Artificial -Intelligence

- Big Data -

NLP

Robot

KnowledgeBase

Deep LearningMachine Learning010101010101010111100010011001010111

1000101

1000101

1000101

111010111010

00000000000010101010100000000000 111101011


Prepare the data

ALL DATA

Input VAR

Training Data

Test Data

Machine Learning Algorithms

Predictive Model PREDICTION ? Test Data

ACCURACY ?

Machine Learning Algorithms use training data to create a

predictive model: its accuracy is tested on holdback data

Machine Learning

TensorFlow Caffee Torch Theano Chainer Spark ML ……

Prepare Data Build/Train Model Deploy/Score Monitor/Refine

Iterative Development


My recent buyer’s journey…


Some challenges…

https://uk.pinterest.com/pin/197595502370835426/sent/?sender=5279

06524979412201&invite_code=7fc292525ac44aafa6736bdec95dd1b5

Speziale Floral Lace Fit & Flare Dress

Items in this section are

temporarily out of stock

https://uk.pinterest.com/pin/197595502370835426/sent/?sender=527906524979412201&invite_code=7fc292525ac44aafa6736bdec95dd1b5

http://www.marksandspencer.com/Speziale-per-una-Womens/b/545829031#ie=UTF8&node=545829031&useParentAsinsInResult=false&sort=-product_site_launch_date&page=3&forceNoKeyword=false&size=12&pf_rd_r=1ST1EKGBHXE7FH296QDX&pf_rd_m=A2BO0OYVBKIQJM&pf_rd_t=101&pf_rd_i=545829031&pf_rd_p=215570647&pf_rd_s=related-items-3


Where I ended up going…

Petite Clothing

Update your wardrobe with Wallis'

stunning must have petite range.

Designed for women who are 5'3" and

under


Deep Learning Goes to the Dogs

• https://openpowerfoundation.org/blogs/deep-learning-goes-to-the-dogs/

• http://vision.stanford.edu/aditya86/ImageNetDogs/

• The Stanford Dogs dataset contains images of 120 breeds of dogs from

around the world. This dataset has been built using images and annotation

from ImageNet for the task of fine-grained image categorization.

• https://www.youtube.com/watch?v=6ZRuTWpIo4M

https://openpowerfoundation.org/blogs/deep-learning-goes-to-the-dogs/

http://vision.stanford.edu/aditya86/ImageNetDogs/

https://www.youtube.com/watch?v=6ZRuTWpIo4M


Example of Datasets available

http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html

http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html


DeepFashion: In-shop Clothes Retrieval

Details

In-shop Clothes Retrieval

Benchmark evaluates the performance of in-

shop Clothes Retrievel. This is a large subset of

DeepFashion, containing large pose and scale

variations. It also has large diversities, large

quantities, and rich annotations, including

• 7,982 number of clothing items;

• 52,712 number of in-shop clothes images,

and ~200,000 cross-pose/scale pairs;

• Each image is annotated by bounding

box, clothing type and pose type.


Gap envisions a future with augmented-reality 'dressing rooms'

https://www.engadget.com/2017/01/30/gap-augmented-reality-dressing-rooms/

https://www.engadget.com/2017/01/30/gap-augmented-reality-dressing-rooms/


Machine Learning 101

Presented by Chris Parsons

Consultant – Artificial Intelligence

IBM Systems Lab Services, UK and Ireland

IBM Systems | 17

• What is Machine

Learning?

• ML Use Cases by

Industry

• How to spot

opportunities for

Machine Learning in

your business

Agenda


Hello, Machine Learning - MNIST






Why has Machine Learning taken off?


Simple Classification Problems

For example, height and weight. The

orange group represents children (low

height/weight) and the blue adults.


Simple Classification Problems

For example, height and weight. The

orange group represents children (low

height/weight) and the blue adults.


Cluster in a Cluster

Time series data, complex sample

surveys and longitudinal studies


ML Use Cases By Industry


Manufacturing

Retail

Healthcare & Life SciencesFinancial Services

HospitalityUtilities

• Predictive

Maintenance

• Process Optimisation

• Demand Forecasting

• Risk Analysis

• Cross/Up Selling

• Credit Checks

• Customer

Segmentation

• Patient Triage

• Proactive Health

Management

• Real Time Alerts and

Diagnostics

• Disease Identification

• Inventory Planning

• Cross-Channel

marketing

• Customer ROI and

Lifetime Value

• Smart Grid

Management

• Carbon Emissions

• Customer Specific

Pricing

• Scheduling

• Pricing

• Social Media

Analytics and

Customer Sentiment


90%Reduction in

inspection times

Significant

Decreasein inspection times

Significant

Increasein checkable quantities/ day

Significantly

Decreasedrate of Safety Risks

• The utility provider inspects its vast

transmission network via hand, with

skilled workers placed into high-risk

environments. This method is costly,

occasionally dangerous, and difficult to

scale.

• To address this and augment worker

productivity, the provider is seeking to

deploy drones to make visual inspections

of transmission towers.

• To automate the image processing, the

provider is using PowerAI to train a

deep learning network to ID potential

maintenance issues captured by the

drones.

• IBM is the only vendor who can provide

the unique supremacy of NVIDIA Tesla

P100 GPUs connected to POWER8

CPUs with NVIDIA NVLink technology

for deep learning.

• IBM’s integrated portfolio of solutions also

allows the provider to not only apply deep

learning but also in-memory DBMS and

high speed storage to store and analyze

various data using Power Systems and

IBM ESS and Spectrum Scale.

Asian Electric

Utility Provider

Maintenance

Inspection


90%Reduction in

inspection times

ImprovedAccuracy of Risk analysis in

credit application process

IncreasedCapital available for

Investment and other

revenue-generating

opportunities

Increased Responsiveness to clients.

Accelerated

Time to respond to clients.

Applying inference in real

time shortens the time to

answer for all clients:

improving the customer

experience.

• A major bank in Oceania is seeking to

apply deep learning to the credit risk

analysis for credit card applications. Their

main goal from this undertaking is to

explore options to add self-learning

capabilities to the current credit risk

marking process.

• By using deep learning to improve the

accuracy of risk analysis, the bank can

determine how much capital needs to be

held to cover that risk.

• Even an improvement of just 1%

accuracy in marking credit risk would

reduce their capital holding requirements,

allowing the freed up capital to be

invested, generating more income for the

bank and it’s account holders.

• The solution uses the IBM S822LCs for

HPC systems, each with four NVIDIA Tesla

P100 GPUs and 1TB of memory, and the

IBM Data Engine for NoSQL CAPI-

attached flash system.

• In addition, IBM is providing Apache Spark

via IBM Spectrum Conductor for SQL and

Spectrum Scale.

Large BankCredit Risk

Analysis



Why Now?

Explosion of data – all sources and availability of storage

Processing capability and GPU’s

Deep Learning frameworks




Introducing PowerAI:

Get Started Fast with Deep Learning

Enabled by High Performance Computing Infrastructure

Package of Pre-Compiled Major Deep Learning

Frameworks

Easy to install & get started with Deep Learning with Enterprise-Class Support

Optimized for Performance To Take Advantage of

NVLink


PowerAI Platform

Caffe NVCaffe TorchIBMCaffe

DL4JTensorFlow

OpenBLAS

Theano

Deep Learning

Frameworks

Accelerated

Servers and

Infrastructure

for Scaling

Spectrum Scale:High-Speed Parallel

File System

Scale toCloud

Cluster of NVLink Servers

Coming

Soon

Bazel DIGITSNCCLDistributed

Frameworks

Supporting

Libraries

Chainer


•“AI Vision,” a tool designed for developers with limited knowledge of deep learning to train and deploy deep learning models for

computer vision.

•Tools for data preparation: Integration with IBM Spectrum Conductor cluster virtualization software that integrates Apache Spark

to ease the process of transforming unstructured as well as structured data sets to prepare them for deep learning training

•Decreased training time: A distributed computing version of TensorFlow, a popular open-source machine learning framework first

built by Google. This distributed version of TensorFlow takes advantage of a virtualized cluster of GPU-accelerated servers using

cost-efficient, high-performance computing methods to bring deep learning training time down from weeks to hours

•Easier model development: A new software tool called “DL Insight” that enables data scientists to rapidly get better accuracy from

their deep learning models. This tool monitors the deep learning training process and automatically adjusts parameters for peak

performance.


“We are also working on a new solution that we call the

Elinar GDPR AI Miner. Built on the IBM Power Systems

and PowerAI platform combined with IBM BigInsights

Text Analytics, it will use our unique AI capabilities to

enable customers to mine huge amounts of GDPR

data. Specifically, we will offer AI models for GDPR

consent identification and data identification and

extraction, which will help users to achieve compliance

at lower cost and higher quality. These are just two of

countless potential applications.”

“The IBM Power S822LC server

provides at least twice the

performance of our x86 platform;

everything is faster and easier.

As a result, we can get new

solutions to market very quickly,

protecting our edge over the

competition.”

—Ari Juntunen, CTO, Elinar

Oy Ltd


Deep Learning – Example Industries

Automotive and Transportation

Security and PublicSafety

Consumer Web, Mobile, Retail

Medicine and Biology Broadcast, Media and Entertainment

• Autonomous driving:• Pedestrian detection• Accident avoidance

Auto, trucking, heavy equipment, Tier 1 suppliers (Hyundai, Toyota, Komatsu, General Motors, Volvo)

Titles: Director of Research, New Applications, “autonomous” in title

• Video Surveillance• Image analysis• Facial recognition and

detection

Local and national police, public and private safety/ security (ADT, IViz, Pinkerton, Sentry)

Titles: Head of Analytics

• Image tagging• Speech recognition• Natural language • Sentiment analysis

Hyperscale web companies, large retail (Google photos, Twitter, Woolworths, Aeon)

Titles: VP/Dir Marketing, Chief Customer Officer, New Application Research

• Drug discovery• Diagnostic assistance• Cancer cell detection

Pharmaceutical, Medical equipment, Diagnostic labs (Takeda, Asian Pharma, Pfizer)

Titles: Principal investigators, Dir of Scientific research

• Captioning• Search• Recommendations• Real time translation

Consumer facing companies with large streaming of existing media, or real time content

Titles: VP/Dir of Marketing, Closed captioning roles, Dir Translation services


How to spot opportunities for Machine

Learning in your business


Collecting Data

What if you didn’t use a form?

Images, Speech, Translation


Improving Business Processes

Talk to your teams

If it’s easy, repeated work – you could

probably automate it


Preempt Your Users

Make common tasks easy

Save your users time

Keep your users coming back


Who to talk to?


Innovation Leads Developer TeamsData Scientists

• Looking for new ways

to solve existing

problems

• Open to new ideas

• Likely looking for

”quick wins”

• Motivated by big

differences for small

cost

• Great understanding

of existing data

• Looking for insight

• Understand the

doman

• Hurt by long wait

times for existing DL

workloads

• Don’t want to worry

about the

software/hardware

stack

• Probably being asked

to look at ML/DL

• Trying to integrate

Machine Learning

models into existing

applications

• Driven by ease of

use

• Performance nerds

• Code portability a

must


Differences between the PowerAI

frameworks


TensorFlow

Theano

DIGITSCaffe

More..Torch

• Becoming De Facto

standard

• Easy to learn

• Designed to support

GPU execution

• Awesome for Image

Recognition (original

use case)

• Poor support for

language modelling –

due to legacy

limitations

• Web GUI

• Easy to use

• Abstracts features of

other frameworks

• Used @ Twitter and

Facebook

• Better debugging –

Automatic

Differentiation

(reverse-mode)

• No Compile Time

https://github.com/zer0n/deepframeworks• Supported and

Developed by

“Worlds Largest”

Academic DL Lab

• Faster than TF and

with a wider range of

functions

https://github.com/zer0n/deepframeworks


| 48

Introducing IBM Power System S822LC for HPCFirst Custom-Built GPU Accelerator Server with NVLink

2.5x Faster CPU-GPU Data Communication via NVLink

NVLink80 GB/s

GPU

P8

GPU GPU

P8

GPU

PCIe32 GB/s

GPU

x86

GPU GPU

x86

GPU

No NVLink between CPU & GPU for x86 Servers: PCIe Bottleneck

NVIDIA P100 Pascal GPU

POWER8 NVLink Server x86 Servers with PCIe

• Custom-built GPU Accelerator Server• High-Speed NVLink Connections between

CPUs & GPUs and among GPUs• Features novel NVIDIA P100 Pascal GPU

accelerator


Questions?David Spurway – IBM Power Systems Product Manager

Email: [email protected]

Phone: 07717 892 896

Twitter, LinkedIn, YouTube

mailto:[email protected]

https://twitter.com/d_spurway

https://uk.linkedin.com/in/david-spurway-870869

https://www.youtube.com/channel/UCjE0lqgXKIihG3os0nx_OVQ


PowerAI Installation and OptimizationOverview

Designed to optimize deployments of PowerAI on Power System servers. This

offering is appropriate both for those clients who are interested in exploring the

capabilities and benefits of implementing PowerAI, as well as those clients

who may currently have Deep Learning and HPC solutions implemented.

Target Audience

• Clients wanting to purse Deep Learning using PowerAI on Power Systems

• Clients who have a 8335-GTB (Minsky) as part of the insertion program

• Clients who have existing applications built on CAFFE, Torch, TensorFlow,

Theano

Benefits

• The customer will have a working PowerAI proof of concept that they can

use for further evaluation of PowerAI and Deep Learning based solutions

• Skills transfer from our experts helps you fully exploit the capabilities

PowerAI

Qualifying Questions

• Does the customer have an interest in Deep Learning/ Machine Learning?

• Does the customer have a research team that needs assistance with setting

up the infrastructure to take advantage of the NVIDIA Deep Learning

capabilities that PowerAI enables

• Has the account team offered a 8335-GTB (Minsky) as part of a larger

Power transaction that could justify Pre-Sales or Post Sales funding for

implementation

Key Features

• Design, Install and Configure PowerAI Ubuntu infrastructure (servers,

network, file systems )

• Install CUDA Libraries & NVIDIA Drivers

• Verify access to GPU and apply best practices

• Install and validate PowerAI packages and sample applications utilizing such

technologies such as Jupyter notebooks, and Spectrum Conductor

• Install Spectrum Computer (LSF) job scheduling

• BlueMind sofware installation and configuration (China only)

• Provide Power infrastructure skills transfer to customer personnel

• Assist in migration of existing customer ML-DL workload onto PowerAI

• Optional Remote On-Demand Assistance as follow-up

Duration

The service varies depending on the size and complexity of the implementation,

but can be customized to specific client requirements.

Resources

Learn more about PowerAI on IBM Power Systems at:

https://www.ibm.com/us-en/marketplace/deep-learning-platform/

Team Contacts

Owner Americas: Fred Robinson [email protected]

Owner China: Yong Bao Tan [email protected]

Owner Europe: David Uttley [email protected]

Owner India: Subramaniam Meenakshisundaram [email protected]

Owner Japan: Shinichi Niimi [email protected]

Find a Lab Services Opportunity Manager in your area ->

http://ibm.biz/LabServicesOM

IBM Systems Lab Services — Power Systems

http://www-03.ibm.com/systems/power/software/linux/





http://ibm.biz/LabServicesOM


Thank you!David Spurway – IBM Power Systems Product Manager

Email: [email protected]

Phone: 07717 892 896

Twitter, LinkedIn, YouTube


https://twitter.com/d_spurway

https://uk.linkedin.com/in/david-spurway-870869

https://www.youtube.com/channel/UCjE0lqgXKIihG3os0nx_OVQ


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Technology

The world of Machine Learning, Deep Learning and PowerAI