Podcasts about jupyterhub

Azure Container Storage offers highly scalable, cost-efficient, and performant storage, built natively for containers. Azure Container Storage simplifies management, deployment, and orchestration of storage volumes seamlessly across volume types including Azure Disk Storage, Azure Elastic SAN, and ephemeral disks. Vybava Ramadoss joins Scott Hanselman to show how you can scale up to multiple users in real time on JupyterHub with Azure Kubernetes Service and Azure Container Storage. Chapters 00:00 - Introduction 02:23 - Why Azure Container Storage? 04:10 - Inside Azure Container Storage 06:15 - Demo setup 07:45 - Demo 16:10 - Wrap-up Recommended resources What is Azure Container Storage? Transforming containerized applications with Azure Container Storage—now in preview Quickstart: Use Azure Container Storage Preview with Azure Kubernetes Service Public preview: Azure Container Storage Create a Pay-as-You-Go account (Azure) Create a free account (Azure) Connect Scott Hanselman | Twitter: @SHanselman Azure Friday | Twitter: @AzureFriday Azure | Twitter: @Azure Contact the team

Azure Container Storage offers highly scalable, cost-efficient, and performant storage, built natively for containers. Azure Container Storage simplifies management, deployment, and orchestration of storage volumes seamlessly across volume types including Azure Disk Storage, Azure Elastic SAN, and ephemeral disks. Vybava Ramadoss joins Scott Hanselman to show how you can scale up to multiple users in real time on JupyterHub with Azure Kubernetes Service and Azure Container Storage. Chapters 00:00 - Introduction 02:23 - Why Azure Container Storage? 04:10 - Inside Azure Container Storage 06:15 - Demo setup 07:45 - Demo 16:10 - Wrap-up Recommended resources What is Azure Container Storage? Transforming containerized applications with Azure Container Storage—now in preview Quickstart: Use Azure Container Storage Preview with Azure Kubernetes Service Public preview: Azure Container Storage Create a Pay-as-You-Go account (Azure) Create a free account (Azure) Connect Scott Hanselman | Twitter: @SHanselman Azure Friday | Twitter: @AzureFriday Azure | Twitter: @Azure Contact the team

In this 4th episode of our ByteSized RSE mini series, we'll talk about Continuous Integration and Deployment. Both of each play an essential part in today's software development practices and can help you in your engineering tasks. There are a number of tools available for this to get you started, and they are listed below. In addition to that, check out Martin Fowler's block post as well as the code review pyramid links. After a brief introduction to the topic, I will be talking to Sarah Gibson from 2i2c. Sarah and I talked about JupyterHub in an episode last year. In this episode she talks about how important Continuous Integration and Deployment are in her daily workTools (not an exhaustive list - there is more):https://github.com/features/actions GitHub actions. https://about.gitlab.com/features/continuous-integration/ CI with GitLabhttps://www.jenkins.io/ Jenkinshttps://www.travis-ci.com/ TravisBlogs and other links:https://martinfowler.com/articles/continuousIntegration.html Martin Fowler's blog on Continuous Integrationhttps://www.morling.dev/blog/the-code-review-pyramid/ Code Review Pyramid by Gunnar Morlinghttps://github.com/sgibson91 - Sarah Gibson's web-sitehttps://2i2c.org/ International Interactive Computing Collaboration, the company helping you built your Jupyter Hub infrastructure.Byte-sized RSE is presented in collaboration with the UNIVERSE-HPC project.https://www.imperial.ac.uk/computational-methods/rse/events/byte-sized-rse/ ByteSized RSE link to Imperial CollegeSupport the Show.Thank you for listening and your ongoing support. It means the world to us! Support the show on Patreon https://www.patreon.com/codeforthought Get in touch: Email mailto:code4thought@proton.me UK RSE Slack (ukrse.slack.com): @code4thought or @piddie US RSE Slack (usrse.slack.com): @Peter Schmidt Mastadon: https://fosstodon.org/@code4thought or @code4thought@fosstodon.org LinkedIn: https://www.linkedin.com/in/pweschmidt/ (personal Profile)LinkedIn: https://www.linkedin.com/company/codeforthought/ (Code for Thought Profile) This podcast is licensed under the Creative Commons Licence: https://creativecommons.org/licenses/by-sa/4.0/

Alex Feiszli from GRAVITL joins the adventure to discuss how to securely connect Kubernetes clusters across clouds from one cluster to another. The discussion spans how to make secure connections and how the connections might be used. Panel Charles Max WoodJillian RoweJonathan HallWill Button Guest Alex Feiszli Sponsors Dev Influencers AcceleratorLevel Up | Devchat.tvPodcastBootcamp.io Links GitHub | gravitl/netmakerGRAVITLGRAVITL - YouTube LinkedIn: GRAVITL Picks Alex- Norm MacdonaldAlex- ITNEXTCharles- Tribe of MillionairesCharles- How to Make Sh*t HappenCharles- God and MoneyCharles- Leviathan WakesCharles- GrooveFunnelsCharles- Riverside.fmJillian- Terraform RegistryJillian- Elastic Kubernetes Service on AWS - EKSJillian- Jupyterhub and RShiny Cluster with Autoscaling Dask on EKSJillian- HPC on AWS with BatchJillian- AWS EFS on EKSJillian- Bioinformatics Docker Images - Jupyterhub, RStudio and DaskJonathan- CommitmentWill- The InstituteWill- Mind-ReachWill- Use docker compose to build and debug Node.js Express applications 10 minutes Contact Charles: Devchat.tvDevChat.tv | FacebookTwitter: DevChat.tv ( @devchattv ) Contact Jillian: GitHub: Jillian Rowe ( jerowe )LinkedIn: Jillian RoweTwitter: Jillian Rowe ( @jillianerowe ) Contact Jonathan: Jonathan HallGitHub: Jonathan Hall ( flimzy )Twitter: Jonathan Hall ( @TinyDevOps ) Contact Will: DevOps For Developers Special Guest: Alex Feiszli.

In this podcast, Bright's Director of Product Management, Robert Stober is once again joined by Adnan Khaleel, the Global Sales Strategist for HPC, AI and Deep Learning at Dell EMC.Together they discuss the Bright Jupyter integration, which is a combination of JupyterHub, JupyterLab, and Jupyter Enterprise Gateway. They look at how the Bright Jupyter integration makes it easy for customers to use Bright for Data Science through JupyterLab notebooks, and allows users to run their notebooks through a supported HPC scheduler, Kubernetes, or on the server running JupyterHub.

See the full show notes for this episode on the website at talkpython.fm/201.

In this episode of DataFramed, Hugo speaks with Brian Granger, co-founder and co-lead of Project Jupyter, physicist and co-creator of the Altair package for statistical visualization in Python.They’ll speak about data science, interactive computing, open source software and Project Jupyter. With over 2.5 million public Jupyter notebooks on github alone, Project Jupyter is a force to be reckoned with. What is interactive computing and why is it important for data science work? What are all the the moving parts of the Jupyter ecosystem, from notebooks to JupyterLab to JupyterHub and binder and why are they so relevant as more and more institutions adopt open source software for interactive computing and data science? From Netflix running around 100,000 Jupyter notebook batch jobs a day to LIGO’s Nobel prize winning discovery of gravitational waves publishing all their results reproducibly using Notebooks, Project Jupyter is everywhere. Links from the show FROM THE INTERVIEWBrian on Twitter Project JupyterBeyond Interactive: Notebook Innovation at Netflix (Ufford, Pacer, Seal, Kelley, Netflix Tech Blog)Gravitational Wave Open Science Center (Tutorials)JupyterCon YouTube Playlistjupyterstream Github RepositoryFROM THE SEGMENTSMachines that Multi-Task (with Friederike Schüür of Fast Forward Labs)Part 1 at ~24:40Brief Introduction to Multi-Task Learning (By Friederike Schüür)Overview of Multi-Task Learning Use Cases (By Manny Moss)Multi-Task Learning for the Segmentation of Building Footprints (Bischke et al., arXiv.org)Multi-Task as Question Answering (McCann et al., arXiv.org)The Salesforce Natural Language Decathlon: A Multitask Challenge for NLP Part 2 at ~44:00Rich Caruana’s Awesome Overview of Multi-Task Learning and Why It WorksSebastian’s Ruder’s Overview of Multi-Task Learning in Deep Neural NetworksMassively Multi-Task Network for Drug Discovery, 259 Tasks (!) (Ramsundar et al. arXiv.org)Brief Overview of Multi-Task Learning with Video of Newsie, the Prototype (By Friederike Schüür) Original music and sounds by The Sticks.

Jessica Forde, Yuvi Panda and Chris Holdgraf join Melanie and Mark to discuss Project Jupyter from it’s interactive notebook origin story to the various open source modular projects it’s grown into supporting data research and applications. We dive specifically into JupyterHub using Kubernetes to enable a multi-user server. We also talk about Binder, an interactive development environment that makes work easily reproducible. Jessica Forde Jessica Forde is a Project Jupyter Maintainer with a background in reinforcement learning and Bayesian statistics. At Project Jupyter, she works primarily on JupyterHub, Binder, and JuptyerLab to improve access to scientific computing and scientific research. Her previous open source projects include datamicroscopes, a DARPA-funded Bayesian nonparametrics library in Python, and density, a wireless device data tool at Columbia University. Jessica has also worked as a machine learning researcher and data scientist in a variety of applications including healthcare, energy, and human capital. Yuvi Panda Yuvi Panda is the Project Jupyter Technical Operations Architect in the UC Berkeley Data Sciences Division. He works on making it easy for people who don’t traditionally consider themselves “programmers” to do things with code. He builds tools (e.g., Quarry, PAWS, etc.) to sidestep the list of historical accidents that constitute the “command line tax” that people have to pay before doing productive things with computing. Chris Holdgraf Chris Holdgraf is a is a Project Jupyter Maintainer and Data Science Fellow at the Berkeley Institute for Data Science and a Community Architect at the Data Science Education Program at UC Berkeley. His background is in cognitive and computational neuroscience, where he used predictive models to understand the auditory system in the human brain. He’s interested in the boundary between technology, open-source software, and scientific workflows, as well as creating new pathways for this kind of work in science and the academy. He’s a core member of Project Jupyter, specifically working with JupyterHub and Binder, two open-source projects that make it easier for researchers and educators to do their work in the cloud. He works on these core tools, along with research and educational projects that use these tools at Berkeley and in the broader open science community. Cool things of the week Dragonball hosted on GC / powered by Spanner blog and GDC presentation at Developer Day Cloud Text-to-Speech API powered by DeepMind WaveNet blog and docs Now you can deploy to Kubernetes Engine from Gitlab blog Interview Jupyter site JupyterHub github Binder site and docs JupyterLab site Kubernetes site github Jupyter Notebook github LIGO (Laser Interferometer Gravitational-Wave Observatory) site and binder Paul Romer, World Bank Chief Economist blog and jupyter notebook The Scientific Paper is Obsolete article Large Scale Teaching Infrastructure with Kubernetes - Yuvi Panda, Berkeley University video Data 8: The Foundations of Data Science site Zero to JupyterHub site JupyterHub Deploy Docker github Jupyter Gitter channels Jupyter Pop-Up, May 15th site JupyterCon, Aug 21-24 site Question of the week How did Google’s predictions do during March Madness? How to build a realt time prediction model: Architecting live NCAA predictions Final Four halftime - fed data from first half to create prediction on second half and created a 30 second spot that ran on CBS before game play sample prediction ad Kaggle Competition site Where can you find us next? Melanie is speaking about AI at Techtonica today, and April 14th will be participating in a panel on Diversity and Inclusion at the Harker Research Symposium

Vai uma pizza à beira-mar? Nesse episódio recebemos Filipe Fernandes, um oceanógrafo que vai nos mostrar que oceanografia não tem nada a ver com empurrar tartaruguinhas de volta ao mar, mas sim com dados! E põe dados nisso!Agradecimento especial aos nossos ParceirosEsse episódio não seria possível sem o apoio especial dos nossos parças do Data Bootcamp, o maior bootcamp de Data Science do Brasil! Aprenda a organizar, extrair e interpretar os dados da sua empresa com as tecnologias mais avançadas usadas no mercado. Confira as datas dos próximos cursos no calendário.Os padrinhos desse episódioEsse episódio não teve padrinhos. Se você quiser apadrinhar episódios do Pizza manda um e-mail pra gente. Tópicos abordados neste episódioQuem é e o que faz nosso convidadoDe um blog pessoal à maior agência de oceanografia dos Estados UnidosLidando com arquivos gigantescosArquivos tipo NetCDFConda-Forge: como começou e como chegou aonde chegouSoftware Carpentry: tudo o que queríamos quando começamos na ciênciaEscute agora Pessoas nesse episódioLeticia Portella Twitter Jessica Temporal Twitter Gustavo Coelho Twitter Filipe Fernandes Twitter   Apoie o pizza:Nós também temos uma campanha de financiamento recorrente para ajudar a pagar a edição dos episódios. Se você gosta do nosso trabalho considere doar. Apoiar o Pizza LinksCoisas que mencionamos durante esse episódio:“The Visual Display of Quantitative Information” de Edward Tufte“The Back of the Napkin: Solving Problems and Selling Ideas with Pictures” de Dan Roam“Como mentir com estatística” de Darrell Huff.NOAABlog inicial do FilipeIOOS Integrated OceanicGOOSJupyter NotebooksContinous Integration ou Integração ContínuaCompliance checkerGeoJSONJSONXMLCSVDaskAPIpandasLazy evaluation ou avaliações preguiçosasSparkCyToolzJVMNetCDFHDFClimate ForecastingFortranlzmalz4blosczlibAnaconda Incconda-forgepipCondaUK Met officeGitHubapt-getNLTKSoftware CarpentryControle de versãoGitShellSVNMercurialMatLabProjeto dos supercomputadores: Pangeo e se quiser conectar no JupyterHub deles o link é esse aquiPérolas?! Temos algumasUma pizza que a gente achou pelo cheiro… fizemos engenharia reversaEu tenho que agradecer ao povo que me demitiu naquela semanaEu não sou um bom desenvolvedor, eu sou um bom “copia e cola”Essa é a definição do oceanógrafo: sabe um pouco de tudo e nada de nadaAchar lógica no mercado financeiro? Você tem que ser um gandalf cientista de dados!Você é muito modesto. Aceita esse parabéns aí!Eu não fico empurrando tartaruga na praiaEdiçãoEsse episódio foi editado pelo Johnny. Valeu Johnny!Escute: