can ai tell the difference between fried chicken a ... · learning ibm watson document conversion...

Can AI tell the difference between fried chicken a labradoodle? Can Machine Learning help with GDPR? Yes, fast, accurately and easily!

David SpurwayIBM Power Systems Product Manager, UKI

3 © IBM Corporation, 2016

Agenda

• How PowerAI can help with GDPR

• AI anyone? We have the best tools for that job!

• Introduction to Machine Learning

• How PowerAI makes Deep Learning easier, with demo

• How PowerAI Vision makes working with datasets much easier, with demo


Example GDPR Case Study


Elinar Oy Ltd

“We specialize in ECM [Enterprise Content

Management] solutions, and recognized that AI

could be key to tackling the huge amounts of

unstructured data that companies must contend

with every day. For business processes such as

sales order processing, invoice automation and

GDPR [European Union General Data

Protection Regulation] discovery, we could

teach AI solutions to replace the human element,

saving time, eliminating effort and uncovering

insights that are far beyond current biological

and technological capabilities. By becoming one

of the first in the AI space, we could seize first-

mover advantage ahead of our competitors.”

Ari Juntunen, CTO at Elinar Oy Ltd

http://ecc.ibm.com/case-study/us-

en/ECCF-POC03326USEN

http://ecc.ibm.com/case-study/us-en/ECCF-POC03326USEN


Built on IBM Power Systems

“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.”

YES

PHASE 1

Training mode

TRAINING MODULE(Teached by customer)

PRE MADE AI

FI, SE, NO (NY), NO (BN), DK, UK, ES Incremental learning

IBM Watson

Document conversion

Natural Language

understanding

IS GDPR

AI(Caffe2)

?

NO

Feedback loop

PHASE 2

Configuration

AnnotationNLU (Watson)

/ AQL(BigInsights)

EXTRACTOR AI

AI GROUPER

Configuration

RULES

EXPORTER

Name, date of birth, address

Name, address

Social security number, customer number

PHASE 3

Rules, definedby client,

when document has GDPR data, AI needed?

Validation API: Customer teaching,

time delay

Level 1

Level 3

Level 2

Natural Language Classifier

Custom defines is data GDPR or not.

Rev.2

My Career History

Date range Role

July 1995 – December 1996 RS/6000 Support Specialist

January 1997 – October 1998 UK HACMP Technical Advisor

October 1998 – December 1999 RS/6000 Presales Technical Specialist

January 2000 – September 2004 RS/6000 Technical Specialist

September 2004 – March 2006 STG Product Services Team Leader – pSeries

March 2006 – December 2006 System p Technical Services Manager

January 2007 – December 2007 System z Technical Sales Specialist

January 2008 – October 2008 System p & System z Presales Technical Specialist

October 2008 – December 2009 Logicalis Senior Enterprise Technical Specialist

January 2010 – May 2011 Logicalis Technical Architect

May 2011 – November 2012 IBM UK STG Systems Architect, FSS

November 2012 – Present IBM Power Systems Product Manager

Now and going forward

November 2012 – Present: IBM Power Systems Product Manager

https://www.linkedin.com/pulse/how-ibm-power-systems-like-batman-david-spurway?articleId=8112406449121429124

https://www.linkedin.com/pulse/how-ibm-power-systems-like-batman-david-spurway?articleId=8112406449121429124


Who is the boss?

Robert Picciano

SVP IBM Cognitive Solutions

IBM Systems

Stefanie Chiras

Vice President, Power Systems Hardware

Offerings


Augmented intelligence, Artificial Intelligence, Cognitive

driving innovation Faster


My friends at Uni…


Hello, Machine Learning - MNIST


GPUs are like minions

The individual cores in a GPU

are not very powerfulhttps://devblogs.nvidia.com/parallelforall/inside-pascal/

But gather loads together, and

remarkable things can happen!

https://devblogs.nvidia.com/parallelforall/inside-pascal/


OBSERVATION DECISIONINTERPRETATION EVALUATION

010101010101010111100010011001010111

0000000000010101010100000000000 111101011

11000 000000000000 111111 010101 101010

10101010100

PrescriptiveBest Outcomes?

DescriptiveWhat Has Happened?

CognitiveLearn Dynamically

PredictiveWhat Could Happen?


010101010101010111100010011001010111

0000000000010101010100000000000 111101011

11000 000000000000 111111 010101 101010

10101010100

OBSERVATION DECISIONINTERPRETATION EVALUATION

PrescriptiveBest Outcomes?

DescriptiveWhat Has Happened?

CognitiveLearn Dynamically

PredictiveWhat Could Happen?

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 ?


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

Recognising Patterns

Use Cases / Industries

Face Detection Spoken Words

Transportation Security

Extracting Insight

From Text/Video Avoiding spam

Healthcare improved diag

Discovering Anomalies

Financial Frauds Sensor Readings

Manufacturing optimisation

Making Predictions

Stock Trade Client Behavior

Retail shopping / promotion


enterprise-ready

software distribution

built on open source

tools for ease

of development

performance

faster training times

for data scientists


Had a drive, had an idea…

@PositivelyPOWER

https://twitter.com/PositivelyPOWER


Other People’s work involving dogs

https://openpowerfoundation.org/blog

s/deep-learning-goes-to-the-dogs/

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


A server and a friend


The real work begins

• Started here:

https://openpowerfoundation.org/blog

s/deep-learning-goes-to-the-dogs/

• “Doggy Docker for Deep Learning”

•“We put our Caffe model and our

classification code…”

• “Requirements”

• “Install the caffe framework

following the instructions here,…”

• Prerequisites

• Caffe has several dependencies:

• CUDA is required for GPU mode.

• library version 7+ and the

latest driver version are

recommended, but 6.* is fine

too

• 5.5, and 5.0 are compatible but

considered legacy

• BLAS via ATLAS, MKL, or

OpenBLAS.

• Boost >= 1.55

• protobuf, glog, gflags, hdf5


https://github.com/Junsong-Wang/pet-breed

http://caffe.berkeleyvision.org/

http://caffe.berkeleyvision.org/installation.html

https://developer.nvidia.com/cuda-zone

http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms

http://www.boost.org/


Installing CUDA

https://developer.nvidia.com/cuda-

downloads?target_os=Linux&target_arch=

ppc64le&target_distro=Ubuntu&target_vers

ion=1604

Installation Instructions:

• `sudo dpkg -i cuda-repo-ubuntu1604-

9-0-local_9.0.176-1_ppc64el.deb`

• `sudo apt-key add /var/cuda-repo-

<version>/7fa2af80.pub`

• `sudo apt-get update`

• `sudo apt-get install cuda`

https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=ppc64le&target_distro=Ubuntu&target_version=1604


Installing BLAS

BLAS: install ATLAS by sudo apt-get install libatlas-base-dev or

install OpenBLAS by sudo apt-get install libopenblas-dev


Installing Boost

5.1 Easy Build and Install

Issue the following commands in the shell (don't type $; that represents the shell's

prompt):

$ cd path/to/boost_1_61_0

$ ./bootstrap.sh --help

Select your configuration options and invoke ./bootstrap.sh again without the --help

option. Unless you have write permission in your system's /usr/local/ directory, you'll

probably want to at least use

$ ./bootstrap.sh --prefix=path/to/installation/prefix

to install somewhere else. Also, consider using the --show-libraries and --with-

libraries=library-name-list options to limit the long wait you'll experience if you build

everything. Finally,

$ ./b2 install

will leave Boost binaries in the lib/ subdirectory of your installation prefix. You will

also find a copy of the Boost headers in the include/ subdirectory of the installation

prefix, so you can henceforth use that directory as an #include path in place of the

Boost root directory.


Installing Caffe

• Compilation with Make

–Configure the build by copying and modifying the example Makefile.config for your

setup. The defaults should work, but uncomment the relevant lines if using Anaconda

Python.

cp Makefile.config.example Makefile.config

# Adjust Makefile.config (for example, if using Anaconda Python, or if

cuDNN is desired)

make all

make test

make runtest–For CPU & GPU accelerated Caffe, no changes are needed.

Wait a minute – is

there not an easier

way?!


Use PowerAI instead!

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

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


Step by step install guide


Or, follow this demo from Chris Parsons (takes 4 mins!)


What is PowerAI?PowerAI R3 PowerAI R4

OSUbuntu16.04

Ubuntu 16.04

CUDA 8.0 8

cuDNN 5.1 6

Built w/ MASS Yes Yes

OpenBLAS 0.2.19 0.2.19

Caffe 1.0 rc5 1.0 rc5

NVIDIA Caffe0.14.5 + 0.15.14 0.15.14

IBM Caffe 1.0 rc3 1.0 rc3

Chainer 1.20.1 1.23

NVIDIA DIGITS 5 5

Torch 7 7

Theano 0.9 0.9

TensorFlow1.0.0+0.12 1.1.0

GPU 4 x P100 4 x P100

Base System S822LC/HPC S822LC/HPC


Steps involved

1. Download & install NVIDIA CUDA

2. Download & install NVIDIA cuDNN

6.0 for CUDA 8 POWER8 Deb

packages

3. Install Deep Learning frameworks

–All, if you like

–Or separately

4. Go!

PowerAI R3 PowerAI R4

OSUbuntu16.04

Ubuntu 16.04

CUDA 8.0 8

cuDNN 5.1 6

Built w/ MASS Yes Yes

OpenBLAS 0.2.19 0.2.19

Caffe 1.0 rc5 1.0 rc5

NVIDIA Caffe0.14.5 + 0.15.14 0.15.14

IBM Caffe 1.0 rc3 1.0 rc3

Chainer 1.20.1 1.23

NVIDIA DIGITS 5 5

Torch 7 7

Theano 0.9 0.9

TensorFlow1.0.0+0.12 1.1.0


On to the training…

Example of how to train the dog breed model based on stanforddog datasetPlese make sure your caffe framewok was installed, and the path of caffe/.build_release/tools has been added to the system PATH.1. Download the stanford dog datasetDownload the images, annotations and lists from stanford dogs dataset and extract them to data/standord_dogs2. Parse the dataset to generate the train and test datasetcd data/standord-dogs && python dog_parse.py

3. Creat the lmdb data for traincd data/standord-dogs && ./create_image.sh

4. Train the model based on GoogleNet.Download GoogleNet Model to models/bvlc_googlenet/Run the following command to start the train.cd models/bvlc_googlenet/ && train.sh

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

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

https://vision.stanford.edu/aditya86/ImageNetDogs/lists.tar

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

http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel


A problem with my idea…

Stanford Dogs Dataset

Aditya Khosla Nityananda Jayadevaprakash Bangpeng Yao Li

Fei-Fei

Stanford University

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. Contents

of this dataset:

•Number of categories: 120

•Number of images: 20,580

•Annotations: Class labels, Bounding boxes

http://people.csail.mit.edu/khosla/

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

http://ai.stanford.edu/~bangpeng/

http://vision.stanford.edu/


Examples of the images (Bernese Mountain Dog)


The GPUs we began with…


The results with the S824L


Deep Learning Goes to the Dogs

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

• For IBMers with permission:

–https://9.196.133.13/netaccess/loginuser.html

–Dogs Classification Demos

• 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://9.196.133.13/netaccess/loginuser.html

http://9.196.147.65:5000/



But then, along came a Minksy!


Using some real GPUs, quite hard!


From 24 hours to 2 hours 38 minutes…


Faster and more accurate

IBM Power S824L

24 hours

Accuracy =

IBM Power S822LC for HPC (Minsky)

2 hour 38 minutes

Accuracy =

0.7% more accurate


Acceleration training …. days become hours

9Days

4 H

ou

rs

Recognition

Shape

Attenuation

Boundary

Recognition

Shape

Attenuation

Boundary

54x

Learning

runs with

POWER8

4 H

ou

rs

4 H

ou

rs

4 H

ou

rs

4 H

ou

rs

. . . . . . . . .

. . . . . .

4 H

ou

rs

What will you do?

Iterate more and create more accurate models?

Create more models?

Both?

100x

Learning

runs with

POWER9


Power Systems and NVIDIA GPU Roadmap

NVIDIA GPU NVIDIA GPU with NVLink

2015

Power Chip Power Chip

with NVLink

2016

80 GB/s

Peak*PCIe x1632 GB/s


Just to mention, in Azure…

NC6 NC12 NC24 NC24r

Cores 6 12 24 24

GPU 1 x K80 GPU 2 x K80 GPUs 4 x K80 GPUs 4 x K80 GPUs

Memory 56 GB 112 GB 224 GB 224 GB

Disk 380 GB SSD 680 GB SSD 1.44 TB SSD 1.44TB SSD

Network Azure

Network

Azure Network Azure Network InfiniBand &

Azure Network

https://azure.microsoft.com/en-us/blog/azure-n-series-general-availability-on-december-1/

https://azure.microsoft.com/en-us/blog/azure-n-series-general-availability-on-december-1/


A bit of a fail…


Answering questions like this…

https://twitter.com/drjuliashaw/status/87

4293864814845952

https://twitter.com/drjuliashaw/status/874293864814845952


How do I get data into PowerAI?


On to the training…

Example of how to train the dog breed model based on stanforddog datasetPlese make sure your caffe framewok was installed, and the path of caffe/.build_release/tools has been added to the system PATH.1. Download the stanford dog datasetDownload the images, annotations and lists from stanford dogs dataset and extract them to data/standord_dogs2. Parse the dataset to generate the train and test datasetcd data/standord-dogs && python dog_parse.py

3. Creat the lmdb data for traincd data/standord-dogs && ./create_image.sh

4. Train the model based on GoogleNet.Download GoogleNet Model to models/bvlc_googlenet/Run the following command to start the train.cd models/bvlc_googlenet/ && train.sh

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

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

https://vision.stanford.edu/aditya86/ImageNetDogs/lists.tar


http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel


Preparing the data…

redbooks@pts153:~/pet-breed/data/stanford-dogs$ pg dog_parse.py

import scipy.io

import os

import cv2

import random

import xml.etree.ElementTree as ET

dog_images = './Images'

dog_annotation = './Annotation'

dog_train_annos_mat = './train_list.mat'

dog_test_annos_mat = './test_list.mat'

output_dir = './dog_detected'

output_txt_dir = '.'

def dog_annotation_processing(dog_annos_mat):

mat = scipy.io.loadmat(dog_annos_mat)

file_list = mat['file_list']

annotation_list = mat['annotation_list']

labels = mat['labels']

file_str = ''

labels_str = ''

for i, filename_item in enumerate(file_list):

filename = filename_item[0][0]

annotation_filename = annotation_list[i][0][0]

label = labels[i][0]

root = ET.parse(dog_annotation + '/' + annotation_filename)

bb = [int(root.find('object').find('bndbox').find('xmin').text), \

int(root.find('object').find('bndbox').find('ymin').text), \

int(root.find('object').find('bndbox').find('xmax').text), \

int(root.find('object').find('bndbox').find('ymax').text)]

print filename, bb, label

img = cv2.imread(dog_images + '/' + filename)

outBgr = img[bb[1]:bb[3], bb[0]:bb[2]]

category = filename.split('/')[0]

if not os.path.exists(output_dir + '/' + category):

os.system('mkdir -p ' + output_dir + '/' + category)

category_split = category.split('-')

labels_str += category_split[0] + ' ' + category_split[1] + '\n'

output_path = os.path.join(output_dir, filename)

cv2.imwrite(output_path, outBgr)

file_str += filename + ' ' + str(int(label)-1) + '\n'

return file_str, labels_str

if not os.path.exists(output_dir):

os.system('mkdir -p ' + output_dir)

train_str, labels_words = dog_annotation_processing(dog_train_annos_mat)

fp = open(output_txt_dir + '/train.txt', 'w')

fp.write(train_str)

fp.close

fp = open(output_txt_dir + '/synset_words.txt', 'w')

fp.write(labels_words)

fp.close

val_str, labels_words = dog_annotation_processing(dog_test_annos_mat)

fp = open(output_txt_dir + '/val.txt', 'w')

fp.write(val_str)

fp.close


Annotation files…

redbooks@pts153:~/pet-breed/data/stanford-dogs/Annotation/n02107683-

Bernese_mountain_dog$ cat n02107683_1003

<annotation>

<folder>02107683</folder>

<filename>n02107683_1003</filename>

<source>

<database>ImageNet database</database>

</source>

<size>

<width>500</width>

<height>375</height>

<depth>3</depth>

</size>

<segment>0</segment>

<object>

<name>Bernese_mountain_dog</name>

<pose>Unspecified</pose>

<truncated>0</truncated>

<difficult>0</difficult>

<bndbox>

<xmin>115</xmin>

<ymin>119</ymin>

<xmax>248</xmax>

<ymax>246</ymax>

</bndbox>

</object>


What format does my data need to be in for Machine

Learning?

• Labeled data

–Metadata (this is a dog, cat, etc.)

–Sub folders named appropriately (/dog /cat)

–CSV. (prevalent)

• TensorFlow

–TFRecords

• Caffe

–Blobs


Transforming Data


Transforming Data (null)


Transforming Data


AI Vision toolset

Experts only becomes

beginner knowledge

requirement to build

image-based neural nets

Tooling lets non-techies

label data – brings expertise

to algorithm from LOB and

mitigates errors

Choose best model and

framework to apply based

on data set

Tools for Ease

of Development


First try, using SuperVessel

• http://ny1.ptopenlab.com:443/AIVision/index.html

http://ny1.ptopenlab.com:443/AIVision/index.html


Begins well…


But this does not look quite so good…


Back to using a Minsky in Southbank…


“Artificial intelligence struggles to tell difference between fried

chicken and Labradoodles.”


AI Vision demo: Enable deep learning capability from data center

to car

© IBM Corporation, 2016

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


What we have gone through

• How PowerAI can help with GDPR

• AI anyone? We have the best tools for that job!

• Introduction to Machine Learning

• How PowerAI makes Deep Learning easier, with demo

• How PowerAI Vision makes working with datasets much easier, with demo

© IBM Corporation, 2016

Thank you!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


Trademarks and notes

IBM Corporation 2015

• IBM, the IBM logo and ibm.com are registered trademarks, and other company, product, or service names may be trademarks or service marks of International Business Machines Corporation in the United States, other countries, or both. A current list of IBM trademarks is available on the web at “Copyright and trademark information” at www.ibm.com/legal/copytrade.shtml

• Other company, product, and service names may be trademarks or service marks of others.

• References in this publication to IBM products or services do not imply that IBM intends to make them available in all countries in which IBM operates.

• IBM and IBM Credit LLC do not, nor intend to, offer or provide accounting, tax or legal advice to clients. Clients should consult with their own financial, tax and legal advisors. Any tax or accounting treatment decisions made by or on behalf of the client are the sole responsibility of the customer.

• IBM Global Financing offerings are provided through IBM Credit LLC in the United States, IBM Canada Ltd. in Canada, and other IBM subsidiaries and divisions worldwide to qualified commercial and government clients. Rates and availability are based on a client’s credit rating, financing terms, offering type, equipment type and options, and may vary by country. Some offerings are not available in certain countries. Other restrictions may apply. Rates and offerings are subject to change, extension or withdrawal without notice.


Special notices

This document was developed for IBM offerings in the United States as of the date of publication. IBM may not make these offerings available in

other countries, and the information is subject to change without notice. Consult your local IBM business contact for information on the IBM offerings

available in your area.

Information in this document concerning non-IBM products was obtained from the suppliers of these products or other public sources. Questions on

the capabilities of non-IBM products should be addressed to the suppliers of those products.

IBM may have patents or pending patent applications covering subject matter in this document. The furnishing of this document does not give you

any license to these patents. Send license inquires, in writing, to IBM Director of Licensing, IBM Corporation, New Castle Drive, Armonk, NY 10504-

1785 USA.

All statements regarding IBM future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

The information contained in this document has not been submitted to any formal IBM test and is provided "AS IS" with no warranties or guarantees

either expressed or implied.

All examples cited or described in this document are presented as illustrations of the manner in which some IBM products can be used and the

results that may be achieved. Actual environmental costs and performance characteristics will vary depending on individual client configurations and

conditions.

IBM Global Financing offerings are provided through IBM Credit Corporation in the United States and other IBM subsidiaries and divisions worldwide

to qualified commercial and government clients. Rates are based on a client's credit rating, financing terms, offering type, equipment type and

options, and may vary by country. Other restrictions may apply. Rates and offerings are subject to change, extension or withdrawal without notice.

IBM is not responsible for printing errors in this document that result in pricing or information inaccuracies.

All prices shown are IBM's United States suggested list prices and are subject to change without notice; reseller prices may vary.

IBM hardware products are manufactured from new parts, or new and serviceable used parts. Regardless, our warranty terms apply.

Any performance data contained in this document was determined in a controlled environment. Actual results may vary significantly and are

dependent on many factors including system hardware configuration and software design and configuration. Some measurements quoted in this

document may have been made on development-level systems. There is no guarantee these measurements will be the same on generally-available

systems. Some measurements quoted in this document may have been estimated through extrapolation. Users of this document should verify the

applicable data for their specific environment.

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