Continuous Machine Learning - AI Ukraine · Easily deploy and run datacenter-wide app services such Docker, Cassandra, Spark pooled on a single platform DC/OS Powered by Apache Mesos
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Continuous Machine Continuous Machine LearningLearning
AI Ukraine’16AI Ukraine’16
Kostiantyn Bokhan, PhDKostiantyn Bokhan, PhDProject Lead at Samsung R&D UkraineProject Lead at Samsung R&D UkraineKharkiv, October 2016Kharkiv, October 2016
●ML dev. workflows ML dev. workflows
●ML dev. issuesML dev. issues
●ML dev. solutions ML dev. solutions
●Continuous machine learning (CML)Continuous machine learning (CML)
●Aspects of CMLAspects of CML
●CML infrastructureCML infrastructure
●CML – deliveryCML – delivery
Agenda Agenda AI Ukraine’16AI Ukraine’16
K.BokhanK.Bokhan 2
ML dev. workflows ML dev. workflows AI Ukraine’16AI Ukraine’16
K.BokhanK.Bokhan 3
RequirementsRequirements PlanningPlanning
Gathering Datasets
Gathering Datasets
Featuredesign Featuredesign
Model TrainingModel
TrainingModel
ValidationModel
ValidationModel TestingModel Testing
Train framework (Python/R/Matlab)
TestsTests FEFE Modeltools
Modeltools ClassifierClassifier UIUI
QAQA MarketMarket
Application (C++/Java)
ML dev. workflows ML dev. workflows AI Ukraine’16AI Ukraine’16
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Define the Problem
Define the Problem
Gather DatasetsGather
Datasets
Clean DatasetsClean
Datasets
Visualize,Explore
Visualize,Explore
Measure,EvaluateMeasure,Evaluate
Hypothesize,Model
Hypothesize,Model
DeployDeploy
Data selection
Data selection
Data transformation
Data transformation
Feature design
ML dev. workflows ML dev. workflows AI Ukraine’16AI Ukraine’16
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Datasets
Train data
Test data
labels
FeatureExtraction
Trainingthe
model
Evalthe
modelModelfeatures
Training
Input Data
FeatureExtractio
n PredictLabels
Modelfeatures
Predicting
ML dev. issues ML dev. issues AI Ukraine’16AI Ukraine’16
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SmallBig
Bigger
Complex
Simple
Complex
DataSize
Modelcomplexity
1996 2006 2016
ML dev. issues ML dev. issues AI Ukraine’16AI Ukraine’16
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Perf
orm
ance
Perf
orm
ance
Machine Learningon PC
ML dev. issues ML dev. issues AI Ukraine’16AI Ukraine’16
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Perf
orm
ance
Perf
orm
ance
Machine Learningon PC
My other computer is Amazon EC2
Machine Learning on AWS
ML dev. issues ML dev. issues AI Ukraine’16AI Ukraine’16
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Perf
orm
ance
Perf
orm
ance
Machine Learningon PC
Machine Learning on dedicated cluster
My other computer is Amazon EC2
Machine Learning on AWS
ML dev. issues ML dev. issues AI Ukraine’16AI Ukraine’16
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Unce
rtain
tyU
nce
rtain
ty
ML dev. issues ML dev. issues AI Ukraine’16AI Ukraine’16
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Vari
ety
Vari
ety
ML dev. issues ML dev. issues AI Ukraine’16AI Ukraine’16
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Relia
bili
tR
elia
bili
tyy
ML dev. issues ML dev. issues AI Ukraine’16AI Ukraine’16
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Reso
urc
e
Reso
urc
e
man
ag
em
en
tm
an
ag
em
en
t
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+
Performance Performance
ML dev. solutions ML dev. solutions AI Ukraine’16AI Ukraine’16
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+
Performance + Reliability Performance + Reliability
Mesos
ML dev. solutions ML dev. solutions AI Ukraine’16AI Ukraine’16
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Variety Variety
ML dev. solutions ML dev. solutions AI Ukraine’16AI Ukraine’16
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Resource management Resource management
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Memory CPU Storage GPU
Kernel →
Init.rd → Marathon
Mesos
dcos CLI Singularity Aurora
MPI
CUDA
Theano
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Source: https://dcos.io/docs/1.8/overview/architecture/
Mesosphere Enterprise DC/OS is an enterprise grade datacenter-scale operating system, providing a single platform for running containers, big data, and distributed apps in production.
Services & Applications
Easily deploy and run datacenter-wide app services such Docker, Cassandra, Spark pooled on a single platform
DC/OS Powered by Apache Mesos
Runtime, tools and best practices built-in to simplify operations and deliver a production self-healing infrastructure
Run Anywhere
Bare-metal, virtual, cloud or hybrid – DC/OS runs on it all – only requirement is a modern Linux distro, Windows support coming soon :)
ML dev. solutions ML dev. solutions AI Ukraine’16AI Ukraine’16
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Source: https://dcos.io/docs/1.8/overview/architecture/
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Apache Mesos is the open-source distributed systems kernel at the heart of the Mesosphere DC/OS. It abstracts the entire datacenter into a single pool of computing resources, simplifying running distributed systems at scale.
Sources: http://nvidia.com, http://blog.arungupta.me/docker-apache-mesos-marathon/
CML Resources CML Resources AI Ukraine’16AI Ukraine’16
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Virtual nodesBare-metal nodes
with GPUTest devices
Hybrid
ML dev. solutions ML dev. solutions AI Ukraine’16AI Ukraine’16
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Uncertainty Uncertainty
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Continuous Machine Continuous Machine LearningLearning
We can’t remove uncertainty but we can automate routines We can’t remove uncertainty but we can automate routines especially delivery and integrationespecially delivery and integration
Aspects of CML Aspects of CML AI Ukraine’16AI Ukraine’16
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Continuous
Continuous
Development
Continuous
Integration Continuous
Deployment
Continuous Delivery
Continuous Everything
CML Infrastructure CML Infrastructure AI Ukraine’16AI Ukraine’16
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Train Validation Test
Build Test Verification
GIT Jenkins Dockerregistry
Developers
Data scientists
DevelopersDevelopers
MesosMaster
StandbyMaster
StandbyMaster
MesosAgent
Spark job
Batchdocker
jobSparkCUDA
job
MesosAgent
SparkDocker
job
MesosAgent
Spark job
CUDA job
MesosAgent
Singularity Marathon
Singularity Marathon
Pool ofDevices
CML – deploy CML – deploy AI Ukraine’16AI Ukraine’16
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CML – deploy CML – deploy AI Ukraine’16AI Ukraine’16
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CML – deploy CML – deploy AI Ukraine’16AI Ukraine’16
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CML – deploy CML – deploy AI Ukraine’16AI Ukraine’16
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Questions?Questions?
AI Ukraine’16AI Ukraine’16
K.BokhanK.Bokhan Samsung R&D Institute UkraineSamsung R&D Institute Ukraine36
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