DEEP-H -Hybrid rid-DataCloud ud Digi gita tal I Inf nfrastr - - PowerPoint PPT Presentation

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DEEP-H -Hybrid rid-DataCloud ud Digi gita tal I Inf nfrastr - - PowerPoint PPT Presentation

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DEEP-H -Hybrid rid-DataCloud ud Digi gita tal I Inf nfrastr tructu uctures f for R Research ( ch (DI4R) lva varo L Lpez Garc ca Lisbon (Portugal) aloga@ifca.unican.es October 10, 2018 Spanish National Research Council

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DEEP-HybridDataCloud has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.

DEEP-H

  • Hybrid

rid-DataCloud ud

Álva varo L López Garcí cía aloga@ifca.unican.es Spanish National Research Council Digi gita tal I Inf nfrastr tructu uctures f for R Research ( ch (DI4R) Lisbon (Portugal) October 10, 2018

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DEEP-HybridDataCloud 10/10/18 2/14

Our Our vis visio ion

  • We need to build added value and advanced services on top of bare IaaS

and PaaS infrastructures

  • Ease and lower the entry barrier for non-skilled scientjsts

– Transparent executjon on e-Infrastructures – Build ready to use modules and ofger them through a catalog or marketplace – Implement common sofuware development techniques also for scientjst’s

applicatjons (DevOps)

  • Build and promote the use of intensive computjng services by difgerent

research communitjes and areas, an the support by the corresponding e- Infrastructure providers and open source projects

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DEEP-HybridDataCloud 10/10/18 3/14

Who ho is is the us user er?

domain expertise machine chine le learni ning ng expert rtise t t e e c c h h n n

  • l

l

  • g

g i i c c a a l l e e x x p p e e r r t t i i s s e e

level of knowledge being required depends on the specifjc use case and the user profjle

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DEEP-HybridDataCloud 10/10/18 4/14

Deep eep lea earnin ning us use e ca cases es

  • Category 1: Deploy a readily trained network for somebody else to

use it on his/her data set

  • Domain knowledge
  • Category 2: Retrain (parts of) a trained network to make use of its

inherent knowledge and to solve a new learning task

  • Domain + machine learning knowledge
  • Category 3: Completely work through the deep learning cycle with

data selectjon, model architecture, training and testjng

  • Domain + machine + technological knowledge
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DEEP-HybridDataCloud 10/10/18 5/14

Previo evious usly ly...

  • Scientjsts create a deep learning

applicatjon on their personal computers

  • The deep learning model is trained in

a GPU node (maybe also locally)

– What happens if they do not have

access to one?

  • The work is published (or not)

– Model architecture, confjguratjon,

scientjfjc publicatjon, etc.

  • But:

– How can a scientjst easily ofger it to a

broader audience?

– What about dependencies?

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DEEP-HybridDataCloud 10/10/18 6/14

Ofg Ofger ering ng develo developed ed mo models els as a ser ervice

  • Development of APIs and web

applicatjons

  • Scientjsts need to know what an

API is

– REST, GET, POST, PUT...

  • Lack of API consistency → hard

for external developers to consume them

  • Provide users with a generic API

(OpenAPI) component where they applicatjon can be plugged

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DEEP-HybridDataCloud 10/10/18 7/14

Ser Service ice compo mposit itio ion

  • An applicatjon may consist on several components that need to be

deployed, confjgured, etc → service compositjon

  • Service compositjon, if done properly, provide a way to re-deploy the

same topology over difgerent infrastructures → catalog of components

  • Scientjsts should not need to deal with technologies and infrastructures

they do not care at all

  • We need therefore difgerent roles, to perform difgerent tasks

– Comparison with laboratory technician

  • In INDIGO-DataCloud we started with this approach, but this needs to be

generalized (and the roles recognized)

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DEEP-HybridDataCloud 10/10/18 8/14

DEEP P Open Open Cat atalo log

  • Collectjon of ready-to-use modules

– Comprising machine learning, deep learning, big data

analytjcs tools

– ML Marketplace

htups://marketplace.deep-hybrid-datacloud.eu

– GitHub

htups://github.com/indigo-dc?utg8= &q=DEEP-OC

  • – DockerHub

htups://hub.docker.com/u/deephdc/

  • Based on DEEPaaS API component

– Expose underlying model functjonality with a common API – Based on OpenAPI specifjcatjons – Minimal modifjcatjons to user applicatjons.

  • Goal: execute the same module on any infrastructure:
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DEEP-HybridDataCloud 9/14

Difg ifger erent ent roles les fo for difg difgerent ent tas asks

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O u t p u t U s e r I n p u t

1 : N

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DEEP-HybridDataCloud 10/10/18 10/14

DEEP P hig high h lev evel l Archi hitecture

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DEEP-HybridDataCloud 10/10/18 11/14

DEEP P 1st rele elease: e: co comp mponen nents

Softw tware component Functionalities PaaS Orchestrator

  • Hybrid deployments on multiple sites
  • Support to specifying specialized computing hardware
  • Improved support for deployment failures

Infrastructure Manager (IM)

  • Improved support for hybrid deployments
  • Support for additional TOSCA types

DEEPaaS API

  • Support for training a machine learning application
  • Support for performing inferences/analisys/predictions
  • Support only for synchronous requests
  • OpenID Connect support
  • Support for standalone service & OpenWhisk action

Alien4Cloud

  • Suppor for visual composition of TOSCA templates
  • PaaS orchestrator support

Virtual Router

  • Improvements to reach production level
  • Virtualized routing over distributed infrastructures

cloud-info-provider

  • Support for GPU and Infibinand resources

uDocker

  • Improved support for GPUs and Infiniband
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DEEP-HybridDataCloud 10/10/18 12/14

DEEP P 1st rele elease: e: ser ervices ices

Servic vice Functionalities Preview endpoint Visual application topology composition and deployment

  • Graphical composition of complex application

topologies

  • Deployment through PaaS orchestrator

https://a4c.ncg.ingrid.pt DEEP as a Service

  • Deployment of DEEP Open Catalog

components as server-less functions https://vm028.pub.cloud.ifca.es/ DEEP Open Catalog

  • Ready-to-use machine learning and deep

learning applications, including: ➢ Machine learning frameworks + JupyterLab ➢ Machine learning ready to use models ➢ Deep learning ready to use models ➢ BigData analytic tools https://marketplace.deep-hybrid-datacloud.eu

  • All services are OIDC-ready, following AARC blueprint recommendatjons
  • Also work on:

–

TOSCA templates and TOSCA types

–

Documentatjon and confjguratjon recipes for GPU supoort

–

Patches to upstream projects (Apache Libcloud, Apache OpenWhisk, OpenStack)

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DEEP-HybridDataCloud 10/10/18 14/14

Cont ntact cts

Web page: htups://deep-hybrid-datacloud.eu Email: deep-info@listas.csic.es htups://twituer.com/DEEP_eu

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DEEP-HybridDataCloud

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.

Thank you Any ny Q Que uestions ions?