DoK Talks #141 - Dossier: multi-tenant distributed Jupyter Notebooks // Iacoppo Colonnelli & Dario Tranchitella

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How to get your confidence back 41:05

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When negative feedback shakes your confidence, it can be difficult to get back to feeling like yourself at work. In this episode, Anne and Frances help a struggling listener who has spent years toning herself down in the workplace after being told that she was too assertive — now, she feels that her modest approach is holding her back. Together, they use Anne and Frances’s “trust triangle” framework to explore how empathy, authenticity, and logic can help you rebuild confidence and trust with your colleagues, and share helpful confidence hacks for getting comfy with discomfort. What problems are you dealing with at work? Text or call 234-FIXABLE or email fixable@ted.com to be featured on the show. For the full text transcript, visit ted.com/podcasts/fixable-transcripts Want to help shape TED’s shows going forward? Fill out our survey ! Hosted on Acast. See acast.com/privacy for more information.…

۳ سال پیش 1:00:10

M4A•خانه قسمت

https://go.dok.community/slack
https://dok.community
ABSTRACT OF THE TALK

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.

BIO

Iacopo Colonnelli is a Computer Science research fellow. He received his Ph.D. with honours in Modeling and Data Science at Università di Torino with a thesis on novel workflow models for heterogeneous distributed systems, and his master’s degree in Computer Engineering from Politecnico di Torino with a thesis on a high-performance parallel tracking algorithm for the ALICE experiment at CERN. His research focuses on both statistical and computational aspects of data analysis at large scale and on workflow modeling and management in heterogeneous distributed architectures.

Dario is an SWE that turned DevOps, and he's regretting this choice day by day. Besides making memes on Twitter that gain more reactions than technical discussions, leading the development of Open Source projects at CLASTIX, an Open Source-based start-up focusing on Multi-Tenancy in Kubernetes.

KEY TAKE-AWAYS FROM THE TALK

From this talk, people will learn:
- The different requirements of Data analysis as a service
- How to configure for multi-tenancy at the cluster level with Capsule
- How to write distributed workflows as Notebooks with Jupyter Workflows
- How to combine all these aspects into a single platform: Dossier

All the software presented in the talk is OpenSource, so attendees can directly play with them and include them in their experiments with no additional restrictions.

243 قسمت

#Tech #Demetrios

DoK Talks #141 - Dossier: multi-tenant distributed Jupyter Notebooks // Iacoppo Colonnelli & Dario Tranchitella

Data on Kubernetes Community

27 subscribers

published ۳ سال پیش

اشتراک گذاری

M4A•خانه قسمت

https://go.dok.community/slack
https://dok.community
ABSTRACT OF THE TALK

BIO

KEY TAKE-AWAYS FROM THE TALK

All the software presented in the talk is OpenSource, so attendees can directly play with them and include them in their experiments with no additional restrictions.

243 قسمت

#Tech #Demetrios

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۳ years پیش16:00

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From the DoK Day North America 2022 (https://youtu.be/YWTa-DiVljY) Abstract Working with Terabytes of data is a major challenge for organizations both in terms of architecture and cost. In recent years, a new paradigm has emerged in the world of Big Data, that is, implementing the entire architecture for processing massive data from a microservices approach. This architecture proved to be scalable, efficient, with excellent performance, failsafe, portable between different cloud providers and at a lower cost than conventional architectures. In this talk, I will present how my team managed to implement this architecture based on Kubernetes and what were the main gains we had with the innovation.…

Data on Kubernetes Community

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Dok Talks #153 - CRD Panel // Eyar Zilberman & Álvaro Hernández 58:05

۳ years پیش58:05

58:05

https://go.dok.community/slack https://dok.community We are going to speak about CRDs, and discuss considering them as higher level entities that we normally consider them. CRDs normally are kind of a byproduct of an operator. But in reality, they can be considered as the user-facing API of the operator surface. And as such, we would like to introduce conversations about even standardizing some of them, or creating "specifications" out of some CRDs. Take this for example: there are some CRDs that could be, potentially, common across multiple operators. Think of a CRD to represent an S3 (or any other storage object) bucket. For operators that store data in object stores, each and every one of them are creating their own versions. But shouldn't we have some "standardized" `ObjectStorage` CRD, without a controller, that all could use? We'll be joined by John Sanda of DataStax who works on the K8ssandra project, Alvaro Hernandez who is the CEO of Ongres and Postgres superhero, and Eyar Zilberman Co-Founder and Chief Product Officer at Datree.…

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Data on Kubernetes Community « » DoK Talks #141 - Dossier: multi-tenant distributed Jupyter Notebooks // Iacoppo Colonnelli & Dario Tranchitella

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DoK Talks #141 - Dossier: multi-tenant distributed Jupyter Notebooks // Iacoppo Colonnelli & Dario Tranchitella