DEEP-Hybrid Datacloud: a project summary

D1.5 - Implementation od dissemination and exploitation report

DEEP-Hybrid-DataCloud consortium

January 2019

AVAILABLE SOON.Project: DEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensiv...

Entrenamiento Escalable de Modelos de Deep Learning sobre Infraestructuras Cloud

Jorge Cano, Javier

January 2019

En este Trabajo Final de Máster se pretende desplegar y aprovisionar de forma desatendida y distribu...

Orchestrated satellite data management

García, Daniel
Aguilar, Fernando

June 2020

Resumen del trabajo presentado a IBERGRID: Delivering Innovative Computing and Data services to Rese...

DEEP-Hybrid Datacloud: present and future

López García, Álvaro

June 2020

Resumen del trabajo presentado a IBERGRID: Delivering Innovative Computing and Data services to Rese...

DEEP: Hybrid Approach for Deep Learning

Alic, Andy S.
Antonacci, Marica
Caballer, Miguel
Campos, Isabel
David, Mario
Dlugolinsky, Stefan
Donvito, Giacinto
Duma, Doina Cristina
Gomes, Jorge
Hardt, Marcus
Heredia, Ignacio
Hluchy, Ladislav
Kozlov, Valentin
Lloret Iglesias, Lara
López García, Álvaro
Marco, Jesús
Matyska, Ludek
Moltó, Germán
Nguyen, Giang
Orviz, Pablo
Plociennik, Marcin
Castell, Wolfgang zu

June 2020

Trabajo presentado al ISC High Performance, celebrado en Frankfurt (Alemania) del 16 al 20 de junio ...

DEEP-HybridDataCloud

Donvito, Giacinto
Gomes, Jorge
Ferrer, A. Juan
Kozlov, Valentin
López García, Álvaro
Matyska, Ludek
Meyer, Norbert
Moltó, Germán
Tran, Viet
Castell, Wolfgang zu

November 2018

Póster presentado al ISC High Performance, celebrado en Frankfurt (Alemania) del 24 al 28 de junio d...

Integration of Apache Mesos over GPUs resources in the DEEP Hybrid DataCloud project

Palacio, Aida
Antonacci, Marica
López García, Álvaro

June 2020

Trabajo presentado a IBERGRID: Delivering Innovative Computing and Data services to Researchers, cel...

A cloud-based framework for machine learning workloads and applications

López García, Álvaro
Marco, Jesús
Antonacci, Marica
Castell, Wolfgang zu
David, Mario
Hardt, Marcus
Lloret Iglesias, Lara
Moltó, Germán
Plociennik, Marcin
Tran, Viet
Alic, Andy S.
Caballer, Miguel
Campos, Isabel
Costantini, Alessandro
Dlugolinsky, Stefan
Duma, Doina Cristina
Donvito, Giacinto
Gomes, Jorge
Heredia, Ignacio
Ito, Keiichi
Kozlov, Valentin
Nguyen, Giang
Orviz, Pablo
Šustr, Zdenêk
Wolniewicz, Pawel

January 2020

In this paper we propose a distributed architecture to provide machine learning practitioners with a...

D1.2 - Updated report from DEEP meetings

DEEP-Hybrid-DataCloud consortium

August 2018

AVAILABLE SOON.Project: DEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensiv...

D6.4 - Final implementation of the DEEP as a Service solution

DEEP-Hybrid-DataCloud consortium

January 2020

AVAILABLE SOON.Project: DEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensiv...

D1.4 - Dissemination and Exploitation Plan

DEEP-Hybrid-DataCloud consortium

August 2018

AVAILABLE SOON.Project: DEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensiv...

A cloud-based framework for machine learning workloads and applications.

Lopez Garcia, A.
Tran, V.
Alic, A.S.
Caballer, M.
Plasencia, I.C.
Costantini, A.
Dlugolinsky, S.
Duma, D.C.
Donvito, G.
Gomes, J.
Heredia Cacha, I.
De Lucas, J.M.
Ito, K.
Kozlov, V.Y.
Nguyen, G.
Orviz Fernandez, P.
Sustr, Z.
Wolniewicz, P.
Antonacci, M.
zu Castell, W.
David, M.
Hardt, M.
Lloret Iglesias, L.
Molto, G.
Plociennik, M.

January 2020

In this paper we propose a distributed architecture to provide machine learning practitioners with a...

D6.2 - Design for the DEEP as a Service solution

DEEP-Hybrid-DataCloud consortium

April 2018

This document provides a description of the design, architecture and work plan the DEEPHybridDataClo...

Machine learning and deep learning frameworks and libraries for large-scale data mining: A survey

Nguyen, Giang
Dlugolinsky, Stefan
Bobak, Martin
Tran, Viet
López García, Álvaro
Heredia, Ignacio
Malik, Peter
Hluchy, Ladislav

January 2019

The combined impact of new computing resources and techniques with an increasing avalanche of large ...

The DeepHealth Toolkit: A unified framework to boost biomedical applications

Cancilla, Michele
Canalini, Laura
Bolelli, Federico
Allegretti, Stefano
Carrión Ponz, Salvador
Paredes Palacios, Roberto
Gómez Adrián, Jon A.
Leo, Simone
Piras, Marco Enrico
Pireddu, Luca
Badouh, Asaf
Marco-Sola, Santiago
Álvarez Martí, Lluc
Moreto Planas, Miquel
Grana, Costantino

January 2021

Given the overwhelming impact of machine learning on the last decade, several libraries and framewor...

D1.5 - Implementation od dissemination and exploitation report

DEEP-Hybrid-DataCloud consortium

January 2019

AVAILABLE SOON.Project: DEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensiv...

Entrenamiento Escalable de Modelos de Deep Learning sobre Infraestructuras Cloud

Jorge Cano, Javier

January 2019

En este Trabajo Final de Máster se pretende desplegar y aprovisionar de forma desatendida y distribu...

Orchestrated satellite data management

García, Daniel
Aguilar, Fernando

June 2020

Resumen del trabajo presentado a IBERGRID: Delivering Innovative Computing and Data services to Rese...

DEEP-Hybrid Datacloud: present and future

López García, Álvaro

June 2020

Resumen del trabajo presentado a IBERGRID: Delivering Innovative Computing and Data services to Rese...

DEEP: Hybrid Approach for Deep Learning

Alic, Andy S.
Antonacci, Marica
Caballer, Miguel
Campos, Isabel
David, Mario
Dlugolinsky, Stefan
Donvito, Giacinto
Duma, Doina Cristina
Gomes, Jorge
Hardt, Marcus
Heredia, Ignacio
Hluchy, Ladislav
Kozlov, Valentin
Lloret Iglesias, Lara
López García, Álvaro
Marco, Jesús
Matyska, Ludek
Moltó, Germán
Nguyen, Giang
Orviz, Pablo
Plociennik, Marcin
Castell, Wolfgang zu

June 2020

Trabajo presentado al ISC High Performance, celebrado en Frankfurt (Alemania) del 16 al 20 de junio ...

DEEP-HybridDataCloud

Donvito, Giacinto
Gomes, Jorge
Ferrer, A. Juan
Kozlov, Valentin
López García, Álvaro
Matyska, Ludek
Meyer, Norbert
Moltó, Germán
Tran, Viet
Castell, Wolfgang zu

November 2018

Póster presentado al ISC High Performance, celebrado en Frankfurt (Alemania) del 24 al 28 de junio d...

Integration of Apache Mesos over GPUs resources in the DEEP Hybrid DataCloud project

Palacio, Aida
Antonacci, Marica
López García, Álvaro

June 2020

Trabajo presentado a IBERGRID: Delivering Innovative Computing and Data services to Researchers, cel...

A cloud-based framework for machine learning workloads and applications

López García, Álvaro
Marco, Jesús
Antonacci, Marica
Castell, Wolfgang zu
David, Mario
Hardt, Marcus
Lloret Iglesias, Lara
Moltó, Germán
Plociennik, Marcin
Tran, Viet
Alic, Andy S.
Caballer, Miguel
Campos, Isabel
Costantini, Alessandro
Dlugolinsky, Stefan
Duma, Doina Cristina
Donvito, Giacinto
Gomes, Jorge
Heredia, Ignacio
Ito, Keiichi
Kozlov, Valentin
Nguyen, Giang
Orviz, Pablo
Šustr, Zdenêk
Wolniewicz, Pawel

January 2020

In this paper we propose a distributed architecture to provide machine learning practitioners with a...

D1.2 - Updated report from DEEP meetings

DEEP-Hybrid-DataCloud consortium

August 2018

AVAILABLE SOON.Project: DEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensiv...

D6.4 - Final implementation of the DEEP as a Service solution

DEEP-Hybrid-DataCloud consortium

January 2020

AVAILABLE SOON.Project: DEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensiv...

D1.4 - Dissemination and Exploitation Plan

DEEP-Hybrid-DataCloud consortium

August 2018

AVAILABLE SOON.Project: DEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensiv...

A cloud-based framework for machine learning workloads and applications.

Lopez Garcia, A.
Tran, V.
Alic, A.S.
Caballer, M.
Plasencia, I.C.
Costantini, A.
Dlugolinsky, S.
Duma, D.C.
Donvito, G.
Gomes, J.
Heredia Cacha, I.
De Lucas, J.M.
Ito, K.
Kozlov, V.Y.
Nguyen, G.
Orviz Fernandez, P.
Sustr, Z.
Wolniewicz, P.
Antonacci, M.
zu Castell, W.
David, M.
Hardt, M.
Lloret Iglesias, L.
Molto, G.
Plociennik, M.

January 2020

In this paper we propose a distributed architecture to provide machine learning practitioners with a...

D6.2 - Design for the DEEP as a Service solution

DEEP-Hybrid-DataCloud consortium

April 2018

This document provides a description of the design, architecture and work plan the DEEPHybridDataClo...

Machine learning and deep learning frameworks and libraries for large-scale data mining: A survey

Nguyen, Giang
Dlugolinsky, Stefan
Bobak, Martin
Tran, Viet
López García, Álvaro
Heredia, Ignacio
Malik, Peter
Hluchy, Ladislav

January 2019

The combined impact of new computing resources and techniques with an increasing avalanche of large ...

The DeepHealth Toolkit: A unified framework to boost biomedical applications

Cancilla, Michele
Canalini, Laura
Bolelli, Federico
Allegretti, Stefano
Carrión Ponz, Salvador
Paredes Palacios, Roberto
Gómez Adrián, Jon A.
Leo, Simone
Piras, Marco Enrico
Pireddu, Luca
Badouh, Asaf
Marco-Sola, Santiago
Álvarez Martí, Lluc
Moreto Planas, Miquel
Grana, Costantino

January 2021

Given the overwhelming impact of machine learning on the last decade, several libraries and framewor...

D1.5 - Implementation od dissemination and exploitation report

DEEP-Hybrid-DataCloud consortium

January 2019

AVAILABLE SOON.Project: DEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensiv...

Entrenamiento Escalable de Modelos de Deep Learning sobre Infraestructuras Cloud

Jorge Cano, Javier

January 2019

En este Trabajo Final de Máster se pretende desplegar y aprovisionar de forma desatendida y distribu...

Orchestrated satellite data management

García, Daniel
Aguilar, Fernando

June 2020

Resumen del trabajo presentado a IBERGRID: Delivering Innovative Computing and Data services to Rese...

DEEP-Hybrid Datacloud: a project summary

Abstract

Extracted data

DEEP-Hybrid Datacloud: a project summary

Abstract

Extracted data

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