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    Jupyter / Cloud deployments working group === [30 minute plenary slides here]( https://docs.google.com/presentation/d/15a6ORNJvavmtdLXW3IZzlVIhuxb81AWG8VPCxh1eEv4/edit?usp=sharing) :::info - **Location:** Alder 107 - **Date:** August 20 10-11 AM (PST) - **Agenda** 1. Create a wishlist`15min` > prioritized wishlist of features that are lacking (sky is the limit!) 2. Discussion `30min` 3. Recap `15min` - **Participants (please add name):** - - YOUR NAME (affiliation) - Scott Henderson (UW eScience) - Yuvi Panda (Project Jupyter) - James Munroe (MUN) - Scott Black (UWRL) - Matt! Rocklin (NVIDIA) - Rodrigo Manzanas (IPCC) - Hillary Scannell (UW) - Shreyas Cholia (LBL) - Tim Crone (Lamont-Doherty Earth Observatory) - Mary Romelfanger (STScI Space Telescope Science Institute) - Friedrich Knuth (UW) - Kirstie Haynie (USGS) - Nicholas Sofroniew (CZI) - Jacob Matuskey (STScI Space Telescope Science Institute) - **Contact:** Scott <scottyh@uw.edu> ::: ## Wishlist ### User convenience - Possibility to execute line by line within the same cell? - Secure push / pull integration with GitHub / GitLab - SSH / SCP / FTP access to your home directory - Propagate user environments from notebooks to dask workers - Submit batch jobs rather than interactive notebooks - Ability for users to have a single S3 bucket that is accessible only to them and a common bucket accessible from the Hub and non-hub machines (e.g. HPCs, laptops, lab servers) ### Performance - Continued development of mixing on demand and spot instances for notebook servers and dask workers. - Faster spinup/teardown of workers ### IT Control - Secure PVC / home directories - Expand community of operations folks working in this space - tutorials on operationalizing your JupyterHub - Avoid giving users advanced permissions on Kubernetes - Quotas on User home directories (we're currently giving unlimited NFS storage space!) - Segregate git-crypt keys or use another system - Remove staging and test deployment to temporary deployment/release ### Generalize - Defining Pangeo for X - what pieces of pangeo would make sense in other domain sciences - Abstract out a generic pangeo-cloud deployment from specific deployments - More turnkey deployments on traditional HPC environments (may need some coordination for HPC centers for deploying hubs etc.) - Using images built for cloud deployments on HPC, and vice versa - The Littlest Pangeo JupyterHub - The Littles BinderHub ### Diagnostics - JupyterLab widgets to track Kubernetes pods - Log usage statistics and have system to visualize. Track costs at user level. ### Now prioritize the above list 1. Top priority 2. Second priority ## Discussion Notes ### prompts - What are the most effective ways to communicate and collaborate among open-source projects that share depencies (Discourse, GitHub - issues, pull requests, project boards), Gitter, Slack)? - How to “operationalize” the hubs? User management. Track usage at cluster, hub, and user levels. - How to prevent disruptions between staging and production environments (current issues with pangeo cloud federation) - Should we focus on making hubs easy to deploy in each cloud region, or focus on a single hub deployment and then tools that can operated on data in different regions (e.g. dask distributed refactor, submitting batch jobs) - What is the long-term vision for sustaining projects that are currently subsidized by Cloud credits and short-term grants that support administrator/developer salary

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