Talks | Parallel Computing
2022
Iacopo Colonnelli, Dario Tranchitella
Dossier: multi-tenant distributed Jupyter Notebooks Miscellaneous
DoK Talks 141, 2022, (Invited talk).
Abstract | Links | BibTeX | Tags: across, deephealth, hpc4ai, jupyter-workflow
@misc{22:data-on-kubernetes,
title = {Dossier: multi-tenant distributed Jupyter Notebooks},
author = {Iacopo Colonnelli and Dario Tranchitella},
url = {https://datacloud.di.unito.it/index.php/s/RNqTGmTqWS66qHT},
year = {2022},
date = {2022-07-01},
address = {Virtual event},
abstract = {When providing data analysis as a service, one must tackle several problems. Data privacy and protection by design are crucial when working on sensitive data. Performance and scalability are fundamental for compute-intensive workloads, e.g. training Deep Neural Networks. User-friendly interfaces and fast prototyping tools are essential to allow domain experts to experiment with new techniques. Portability and reproducibility are necessary to assess the actual value of results. Kubernetes is the best platform to provide reliable, elastic, and maintainable services. However, Kubernetes alone is not enough to achieve large-scale multi-tenant reproducible data analysis. OOTB support for multi-tenancy is too rough, with only two levels of segregation (i.e. the single namespace or the entire cluster). Offloading computation to off-cluster resources is non-trivial and requires the user's manual configuration. Also, Jupyter Notebooks per se cannot provide much scalability (they execute locally and sequentially) and reproducibility (users can run cells in any order and any number of times). The Dossier platform allows system administrators to manage multi-tenant distributed Jupyter Notebooks at the cluster level in the Kubernetes way, i.e. through CRDs. Namespaces are aggregated in Tenants, and all security and accountability aspects are managed at that level. Each Notebook spawns into a user-dedicated namespace, subject to all Tenant-level constraints. Users can rely on provisioned resources, either in-cluster worker nodes or external resources like HPC facilities. Plus, they can plug their computing nodes in a BYOD fashion. Notebooks are interpreted as distributed workflows, where each cell is a task that one can offload to a different location in charge of its execution.},
howpublished = {DoK Talks 141},
note = {Invited talk},
keywords = {across, deephealth, hpc4ai, jupyter-workflow},
pubstate = {published},
tppubtype = {misc}
}
When providing data analysis as a service, one must tackle several problems. Data privacy and protection by design are crucial when working on sensitive data. Performance and scalability are fundamental for compute-intensive workloads, e.g. training Deep Neural Networks. User-friendly interfaces and fast prototyping tools are essential to allow domain experts to experiment with new techniques. Portability and reproducibility are necessary to assess the actual value of results. Kubernetes is the best platform to provide reliable, elastic, and maintainable services. However, Kubernetes alone is not enough to achieve large-scale multi-tenant reproducible data analysis. OOTB support for multi-tenancy is too rough, with only two levels of segregation (i.e. the single namespace or the entire cluster). Offloading computation to off-cluster resources is non-trivial and requires the user's manual configuration. Also, Jupyter Notebooks per se cannot provide much scalability (they execute locally and sequentially) and reproducibility (users can run cells in any order and any number of times). The Dossier platform allows system administrators to manage multi-tenant distributed Jupyter Notebooks at the cluster level in the Kubernetes way, i.e. through CRDs. Namespaces are aggregated in Tenants, and all security and accountability aspects are managed at that level. Each Notebook spawns into a user-dedicated namespace, subject to all Tenant-level constraints. Users can rely on provisioned resources, either in-cluster worker nodes or external resources like HPC facilities. Plus, they can plug their computing nodes in a BYOD fashion. Notebooks are interpreted as distributed workflows, where each cell is a task that one can offload to a different location in charge of its execution.
Iacopo Colonnelli
StreamFlow Miscellaneous
2nd HealthyCloud Workshop: Analysis of existing orchestration mechanisms for distributed computational analyses, 2022, (Invited talk).
Links | BibTeX | Tags: across, deephealth, eupex, streamflow, textarossa
@misc{22:healthycloud-workshop,
title = {StreamFlow},
author = {Iacopo Colonnelli},
url = {https://datacloud.di.unito.it/index.php/s/Taz8qtzmkmn9ffT},
year = {2022},
date = {2022-07-01},
address = {Virtual event},
howpublished = {2nd HealthyCloud Workshop: Analysis of existing orchestration mechanisms for distributed computational analyses},
note = {Invited talk},
keywords = {across, deephealth, eupex, streamflow, textarossa},
pubstate = {published},
tppubtype = {misc}
}
Iacopo Colonnelli
StreamFlow: a topology-aware WMS Miscellaneous
ELIXIR Cloud, Data & AAI Bi-weekly Technical Calls, 2022, (Invited talk).
Links | BibTeX | Tags: across, deephealth, eupex, streamflow, textarossa
@misc{22:elixir-streamflow,
title = {StreamFlow: a topology-aware WMS},
author = {Iacopo Colonnelli},
url = {https://datacloud.di.unito.it/index.php/s/Z9GsKnRCxmBdMd3},
year = {2022},
date = {2022-06-01},
address = {Virtual event},
howpublished = {ELIXIR Cloud, Data & AAI Bi-weekly Technical Calls},
note = {Invited talk},
keywords = {across, deephealth, eupex, streamflow, textarossa},
pubstate = {published},
tppubtype = {misc}
}
Marco Aldinucci
HPC-cloud convergence is the missing link between scientific computing and applied-AI Miscellaneous
Machine Learning for Astrophysics (ML4ASTRO), 2022, (Keynote talk).
Abstract | Links | BibTeX | Tags: across, deephealth, eupex, eupilot
@misc{22:ml4astrotalk,
title = {HPC-cloud convergence is the missing link between scientific computing and applied-AI},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/2SGswkcip7MoMoH},
year = {2022},
date = {2022-06-01},
address = {Catania, Italy},
abstract = {First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.},
howpublished = {Machine Learning for Astrophysics (ML4ASTRO)},
note = {Keynote talk},
keywords = {across, deephealth, eupex, eupilot},
pubstate = {published},
tppubtype = {misc}
}
First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.
Iacopo Colonnelli, Dario Tranchitella
OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes Miscellaneous
J on The Beach 2022, 2022.
Links | BibTeX | Tags: across, deephealth, hpc4ai, jupyter-workflow, streamflow
@misc{22:jotb22,
title = {OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes},
author = {Iacopo Colonnelli and Dario Tranchitella},
url = {https://datacloud.di.unito.it/index.php/s/n6J7STNnwdyqtET},
year = {2022},
date = {2022-04-01},
address = {Malaga, Spain},
howpublished = {J on The Beach 2022},
keywords = {across, deephealth, hpc4ai, jupyter-workflow, streamflow},
pubstate = {published},
tppubtype = {misc}
}
Iacopo Colonnelli
Distributed workflows with Jupyter Miscellaneous
J on The Beach 2022, 2022, (Workshop).
Links | BibTeX | Tags: across, deephealth, jupyter-workflow, streamflow
@misc{22:jotb22-workshop,
title = {Distributed workflows with Jupyter},
author = {Iacopo Colonnelli},
url = {https://datacloud.di.unito.it/index.php/s/om89q55S6ePf2Ji},
year = {2022},
date = {2022-04-01},
address = {Malaga, Spain},
howpublished = {J on The Beach 2022},
note = {Workshop},
keywords = {across, deephealth, jupyter-workflow, streamflow},
pubstate = {published},
tppubtype = {misc}
}
Iacopo Colonnelli
The OpenDeepHealth toolkit Miscellaneous
DeepHealth Winter School, 2022.
Links | BibTeX | Tags: deephealth, hpc4ai
@misc{22:DHWinterSchool,
title = {The OpenDeepHealth toolkit},
author = {Iacopo Colonnelli},
url = {https://datacloud.di.unito.it/index.php/s/cJ8pRNsWRrfwPqr},
year = {2022},
date = {2022-01-01},
address = {Torino, Italy},
howpublished = {DeepHealth Winter School},
keywords = {deephealth, hpc4ai},
pubstate = {published},
tppubtype = {misc}
}
2021
Marco Aldinucci
The modernization of HPC applications for the cloud era Miscellaneous
Fifth EAGE Workshop on High Performance Computing for Upstream, 2021, (Keynote talk).
Abstract | BibTeX | Tags: across, admire, deephealth, streamflow
@misc{21:eni:streamflow,
title = {The modernization of HPC applications for the cloud era},
author = {Marco Aldinucci},
year = {2021},
date = {2021-09-01},
address = {Virtual event},
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g., clouds, supercomputers, and both of them. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments (such as Kubernetes and SLURM), making it possible to execute onto multiple sites not sharing a common data space. Streamflow clearly distinguishes it from many other workflow management systems because it decouples the data dependencies from the deployment of (containerized) workflow steps. Streamflow also leverages CAPIO (Cross-Application Programmable I/O) to move data from one step to another efficiently. CAPIO captures the POSIX file system and streams it in parallel and in-memory to the workflow's next step, possibly enabling in-transit data filtering.},
howpublished = {Fifth EAGE Workshop on High Performance Computing for Upstream},
note = {Keynote talk},
keywords = {across, admire, deephealth, streamflow},
pubstate = {published},
tppubtype = {misc}
}
Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g., clouds, supercomputers, and both of them. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments (such as Kubernetes and SLURM), making it possible to execute onto multiple sites not sharing a common data space. Streamflow clearly distinguishes it from many other workflow management systems because it decouples the data dependencies from the deployment of (containerized) workflow steps. Streamflow also leverages CAPIO (Cross-Application Programmable I/O) to move data from one step to another efficiently. CAPIO captures the POSIX file system and streams it in parallel and in-memory to the workflow's next step, possibly enabling in-transit data filtering.
Marco Aldinucci
From skeletons to workflows in the cloud-edge era Miscellaneous
14th Intl. Symposium on High-Level Programming and Applications (HLPP), 2021, (Keynote talk).
Abstract | Links | BibTeX | Tags: across, admire, deephealth, streamflow
@misc{21:hlpp:streamflow,
title = {From skeletons to workflows in the cloud-edge era},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/RyRPjNBse5PKnab},
year = {2021},
date = {2021-07-01},
address = {Virtual event},
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments and that makes it possible to execute multiple sites not sharing a common data space. StreamFlow supports both task and data parallelism and enables the reproducible and scalable execution of workflows, such as AI pipelines, in hybrid cloud-HPC environments. As a running example, we use the novel ``universal COVID-19 pipeline'' that explore the whole optimisation space of the training of different DNNs to classify COVID-19 lung lesions.},
howpublished = {14th Intl. Symposium on High-Level Programming and Applications (HLPP)},
note = {Keynote talk},
keywords = {across, admire, deephealth, streamflow},
pubstate = {published},
tppubtype = {misc}
}
Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments and that makes it possible to execute multiple sites not sharing a common data space. StreamFlow supports both task and data parallelism and enables the reproducible and scalable execution of workflows, such as AI pipelines, in hybrid cloud-HPC environments. As a running example, we use the novel ``universal COVID-19 pipeline'' that explore the whole optimisation space of the training of different DNNs to classify COVID-19 lung lesions.
Marco Aldinucci
Reproducibility in the AI era Miscellaneous
Penta Scientific Meeting, 2021.
Abstract | Links | BibTeX | Tags: across, admire, deephealth
@misc{21:penta:covid,
title = {Reproducibility in the AI era},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/GLpf7kKSJRH733A},
year = {2021},
date = {2021-07-01},
address = {Virtual event},
abstract = {TBD},
howpublished = {Penta Scientific Meeting},
keywords = {across, admire, deephealth},
pubstate = {published},
tppubtype = {misc}
}
TBD
Marco Aldinucci
DeepHealth perspective Miscellaneous
Future challenges in IoT, AI, and convergence of HPC & Cloud & Big Data – BDVA Data Week, 2021.
Abstract | BibTeX | Tags: deephealth
@misc{21:dataweek:deephealth,
title = {DeepHealth perspective},
author = {Marco Aldinucci},
year = {2021},
date = {2021-05-01},
address = {Virtual event},
abstract = {TBD},
howpublished = {Future challenges in IoT, AI, and convergence of HPC & Cloud & Big Data – BDVA Data Week},
keywords = {deephealth},
pubstate = {published},
tppubtype = {misc}
}
TBD
Marco Aldinucci, Marco Beccuti
DeepHealth: Deep Learning ad alte prestazioni per applicazioni in ambito medico Miscellaneous
Reserach meeting of the PoloICT, 2021.
Links | BibTeX | Tags: deephealth, hpc4ai
@misc{21:poloict:deephealth,
title = {DeepHealth: Deep Learning ad alte prestazioni per applicazioni in ambito medico},
author = {Marco Aldinucci and Marco Beccuti},
url = {https://datacloud.di.unito.it/index.php/s/2F5Net5HdfJTysa},
year = {2021},
date = {2021-04-01},
address = {Torino, Italy},
howpublished = {Reserach meeting of the PoloICT},
keywords = {deephealth, hpc4ai},
pubstate = {published},
tppubtype = {misc}
}
Marco Aldinucci, Iacopo Colonnelli
The Universal Cloud-HPC Pipeline for the AI-Assisted Explainable Diagnosis of COVID-19 Pneumonia Miscellaneous
NVidia GTC'21, 2021, (Invited talk).
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai, streamflow
@misc{21:gtc:clairecovid,
title = {The Universal Cloud-HPC Pipeline for the AI-Assisted Explainable Diagnosis of COVID-19 Pneumonia},
author = {Marco Aldinucci and Iacopo Colonnelli},
url = {https://datacloud.di.unito.it/index.php/s/AkQLbPpEEtDzbbm},
year = {2021},
date = {2021-04-01},
address = {Virtual event},
abstract = {We'll present a methodology to run DNN pipelines on hybrid cloud+HPC infrastructure. We'll also define a "universal pipeline" for medical images. The pipeline can reproduce all state-of-the-art DNNs to diagnose COVID-19 pneumonia, which appeared in the literature during the first Italian lockdown and following months. We can run all of them (across cloud+HPC platforms) and compare their performance in terms of sensitivity and specificity to set a baseline to evaluate future progress in the automated diagnosis of COVID-19. Also, the pipeline makes existing DNNs explainable by way of adversarial training. The pipeline is easily portable and can run across different infrastructures, adapting the performance-urgency trade-off. The methodology builds onto two novel software programs: the streamflow workflow system and the AI-sandbox concept (parallel container with user-space encrypted file system). We reach over 92% accuracy in diagnosing COVID pneumonia.},
howpublished = {NVidia GTC'21},
note = {Invited talk},
keywords = {deephealth, hpc4ai, streamflow},
pubstate = {published},
tppubtype = {misc}
}
We'll present a methodology to run DNN pipelines on hybrid cloud+HPC infrastructure. We'll also define a "universal pipeline" for medical images. The pipeline can reproduce all state-of-the-art DNNs to diagnose COVID-19 pneumonia, which appeared in the literature during the first Italian lockdown and following months. We can run all of them (across cloud+HPC platforms) and compare their performance in terms of sensitivity and specificity to set a baseline to evaluate future progress in the automated diagnosis of COVID-19. Also, the pipeline makes existing DNNs explainable by way of adversarial training. The pipeline is easily portable and can run across different infrastructures, adapting the performance-urgency trade-off. The methodology builds onto two novel software programs: the streamflow workflow system and the AI-sandbox concept (parallel container with user-space encrypted file system). We reach over 92% accuracy in diagnosing COVID pneumonia.
Iacopo Colonnelli
StreamFlow: cross breeding cloud with HPC Miscellaneous
2021 CWL Mini Conference, 2021, (Invited talk).
Abstract | Links | BibTeX | Tags: deephealth, streamflow
@misc{21:CWLMiniConference,
title = {StreamFlow: cross breeding cloud with HPC},
author = {Iacopo Colonnelli},
url = {https://datacloud.di.unito.it/index.php/s/Le9gg4PfjRxBwXD},
year = {2021},
date = {2021-02-01},
address = {Virtual event},
abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space.},
howpublished = {2021 CWL Mini Conference},
note = {Invited talk},
keywords = {deephealth, streamflow},
pubstate = {published},
tppubtype = {misc}
}
Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space.
Marco Aldinucci
On HPC, AI and their Fatal Attraction Miscellaneous
CNR IEIIT, Thursday seminars (11 Feb 2021), 2021, (Invited talk).
Links | BibTeX | Tags: deephealth, hpc4ai
@misc{21:CNR:hpcai,
title = {On HPC, AI and their Fatal Attraction},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/pSDxNPncic8gEy8},
year = {2021},
date = {2021-02-01},
address = {Virtual event},
howpublished = {CNR IEIIT, Thursday seminars (11 Feb 2021)},
note = {Invited talk},
keywords = {deephealth, hpc4ai},
pubstate = {published},
tppubtype = {misc}
}
Marco Aldinucci
Lung Nodules Segmentation in CT scans by DeepHealth toolkit Miscellaneous
25th Intl. Conference on Pattern Recognition, 2021, (Demo).
Links | BibTeX | Tags: deephealth
@misc{21:icpr:demodeephealth,
title = {Lung Nodules Segmentation in CT scans by DeepHealth toolkit},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/KYJMcT3pfpat2Hx},
year = {2021},
date = {2021-01-01},
publisher = {BDVA},
address = {Milano. Italy},
howpublished = {25th Intl. Conference on Pattern Recognition},
note = {Demo},
keywords = {deephealth},
pubstate = {published},
tppubtype = {misc}
}
Marco Aldinucci
HPC application cloudification: the streamflow toolkit Miscellaneous
PARMA-DITAM (co-localed with HiPEAC), 2021, (Keynote talk).
Links | BibTeX | Tags: deephealth, hpc4ai
@misc{21:parmaditam:hpc4ai,
title = {HPC application cloudification: the streamflow toolkit},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/HWZijXPqmwfoYCp},
year = {2021},
date = {2021-01-01},
address = {Virtual event},
howpublished = {PARMA-DITAM (co-localed with HiPEAC)},
note = {Keynote talk},
keywords = {deephealth, hpc4ai},
pubstate = {published},
tppubtype = {misc}
}
Marco Aldinucci
High-performance computing and AI team up for COVID-19 diagnostic imaging Miscellaneous
AIhub, 2021, ((magazine)).
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai
@misc{21:covid:aihub,
title = {High-performance computing and AI team up for COVID-19 diagnostic imaging},
author = {Marco Aldinucci},
url = {https://aihub.org/2021/01/12/high-performance-computing-and-ai-team-up-for-covid-19-diagnostic-imaging/},
year = {2021},
date = {2021-01-01},
abstract = {The Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) taskforce on AI & COVID-19 supported the creation of a research group focused on AI-assisted diagnosis of COVID-19 pneumonia. The first results demonstrate the great potential of AI-assisted diagnostic imaging. Furthermore, the impact of the taskforce work is much larger, and it embraces the cross-fertilisation of artificial intelligence (AI) and high-performance computing (HPC): a partnership with rocketing potential for many scientific domains.},
howpublished = {AIhub},
note = {(magazine)},
keywords = {deephealth, hpc4ai},
pubstate = {published},
tppubtype = {misc}
}
The Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) taskforce on AI & COVID-19 supported the creation of a research group focused on AI-assisted diagnosis of COVID-19 pneumonia. The first results demonstrate the great potential of AI-assisted diagnostic imaging. Furthermore, the impact of the taskforce work is much larger, and it embraces the cross-fertilisation of artificial intelligence (AI) and high-performance computing (HPC): a partnership with rocketing potential for many scientific domains.
2020
Iacopo Colonnelli, Sergio Rabellino
JupyterFlow: Jupyter Notebooks su larga scala Miscellaneous
Workshop GARR 2020, 2020.
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai, jupyter-workflow
@misc{20:GarrWorkshop,
title = {JupyterFlow: Jupyter Notebooks su larga scala},
author = {Iacopo Colonnelli and Sergio Rabellino},
url = {https://datacloud.di.unito.it/index.php/s/ASPEmyXAj5QscgC},
year = {2020},
date = {2020-11-01},
address = {Virtual event},
abstract = {I Jupyter Notebook sono largamente utilizzati sia in ambito industriale che accademico come strumento di didattica, prototipazione e analisi esplorative. Purtroppo il sistema runtime standard di Jupyter non è abbastanza potente per sostenere un carichi di lavoro reali e spesso l'unica soluzione è quella di riscrivere il codice da zero in una tecnologia con supporto HPC. Intrgrando lo stack Jupyter con StreamFlow (https://streamflow.di.unito.it/) è possibile creare i Notebook tramite un'interfaccia web su cloud ed eseguirli in maniera trasparente in remoto su una VM con GPU o su nodi HPC.},
howpublished = {Workshop GARR 2020},
keywords = {deephealth, hpc4ai, jupyter-workflow},
pubstate = {published},
tppubtype = {misc}
}
I Jupyter Notebook sono largamente utilizzati sia in ambito industriale che accademico come strumento di didattica, prototipazione e analisi esplorative. Purtroppo il sistema runtime standard di Jupyter non è abbastanza potente per sostenere un carichi di lavoro reali e spesso l'unica soluzione è quella di riscrivere il codice da zero in una tecnologia con supporto HPC. Intrgrando lo stack Jupyter con StreamFlow (https://streamflow.di.unito.it/) è possibile creare i Notebook tramite un'interfaccia web su cloud ed eseguirli in maniera trasparente in remoto su una VM con GPU o su nodi HPC.
Marco Aldinucci
Polmonite da COVID-19, diagnosi con l'intelligenza artificiale: Italia in prima fila Miscellaneous
Agenda Digitale, 2020, ((magazine)).
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai
@misc{20:covid:ag,
title = {Polmonite da COVID-19, diagnosi con l'intelligenza artificiale: Italia in prima fila},
author = {Marco Aldinucci},
url = {https://www.agendadigitale.eu/sanita/polmonite-da-covid-19-allo-studio-la-diagnosi-tramite-intelligenza-artificiale-italia-in-prima-fila/},
year = {2020},
date = {2020-11-01},
abstract = {La Task Force su AI&COVID-19 della confederazione europea dei laboratori di ricerca sull'intelligenza artificiale (CLAIRE) ha sostenuto la creazione di un gruppo di ricerca focalizzato sulla diagnosi della polmonite da COVID assistita dall'Intelligenza Artificiale. I primi risultati sono incoraggianti},
howpublished = {Agenda Digitale},
note = {(magazine)},
keywords = {deephealth, hpc4ai},
pubstate = {published},
tppubtype = {misc}
}
La Task Force su AI&COVID-19 della confederazione europea dei laboratori di ricerca sull'intelligenza artificiale (CLAIRE) ha sostenuto la creazione di un gruppo di ricerca focalizzato sulla diagnosi della polmonite da COVID assistita dall'Intelligenza Artificiale. I primi risultati sono incoraggianti
Marco Aldinucci
The DeepHealth project Miscellaneous
HPC, Big Data, IoT and AI future industry-driven collaborative strategic topics virtual workshop — HPC / HPDA spectrum, 2020, (Invited talk).
Links | BibTeX | Tags: deephealth
@misc{20:bdva:deephealth,
title = {The DeepHealth project},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/ortwLJHS2q96irb},
year = {2020},
date = {2020-07-01},
publisher = {BDVA},
howpublished = {HPC, Big Data, IoT and AI future industry-driven collaborative strategic topics virtual workshop — HPC / HPDA spectrum},
note = {Invited talk},
keywords = {deephealth},
pubstate = {published},
tppubtype = {misc}
}
Marco Aldinucci
Building avenues for AI-assisted diagnosis over the bridge from HPC to AI Miscellaneous
CLAIRE COVID webinar, 2020, (Invited talk).
Links | BibTeX | Tags: deephealth
@misc{20:claire:taskforce,
title = {Building avenues for AI-assisted diagnosis over the bridge from HPC to AI},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/RqpNCHyyL6wc5ds},
year = {2020},
date = {2020-07-01},
address = {Torino, Italy},
howpublished = {CLAIRE COVID webinar},
note = {Invited talk},
keywords = {deephealth},
pubstate = {published},
tppubtype = {misc}
}
Marco Aldinucci
StreamFlow: cross-breeding cloud with HPC Miscellaneous
Computability in Europe 2020 (CIE), 2020, (Invited talk).
Links | BibTeX | Tags: deephealth
@misc{20:CIE:streamflow,
title = {StreamFlow: cross-breeding cloud with HPC},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/Ltqo4SmJj42wjyo},
year = {2020},
date = {2020-06-01},
howpublished = {Computability in Europe 2020 (CIE)},
note = {Invited talk},
keywords = {deephealth},
pubstate = {published},
tppubtype = {misc}
}
Marco Aldinucci
Machine Learning: the treacherous journey from data to knowledge (with examples from HPC4AI@UNITO platform) Miscellaneous
Machine Learning Meets Chemistry @ the Department of Chemistry, University of Torino, 2020, (Invited talk).
Links | BibTeX | Tags: deephealth, hpc4ai
@misc{20:chem:HPCAI,
title = {Machine Learning: the treacherous journey from data to knowledge (with examples from HPC4AI@UNITO platform)},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/ffyZYYqNQpkza4F},
year = {2020},
date = {2020-02-01},
address = {Torino, Italy},
howpublished = {Machine Learning Meets Chemistry @ the Department of Chemistry, University of Torino},
note = {Invited talk},
keywords = {deephealth, hpc4ai},
pubstate = {published},
tppubtype = {misc}
}