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Deployment automation for Pangeo JupyterHubs on AWS, Google, and Azure

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Upgrades daskhub chart version from `4.5.6` to `2021.12.0`. - [ ] Inspect eventual dependency changes between JupyterHub and Dask-Gateway [here](https://helm.dask.org/). - [ ] If needed, inspect [the JupyterHub chart's changelog](http://github.com/jupyterhub/zero-to-jupyterhub-k8s/tree/master/CHANGELOG.md)....

dependencies

I need to be admin on this cluster.

The staging environment is now already two xarray versions behind (`v0.17.0` vs `v0.19.0` released). This was causing a bunch of issues for users who want to utilize the new xarray...

tried to log into https://aws-uswest2.pangeo.io today but was not getting a server. turns out our core node (running on spot) was shut down and when the cluster-autoscaler pod was recreated...

this is how the current pangeo aws custer was deployed with eksctl and kubectl

I have been noticing very long wait times to get dask workers to come online lately. It just took me ~30 min to get any workers on the pangeo google...

I have noticed for a while now, that when my computations don't work (i.e.a worker die) often times regardless of my biggest effort to kill a cluster, i keep seeing...

Vague conversations with many folks indicated that there has always been interest in some way to transfer large amounts of files efficiently in and out of home directories in JupyterHub....

On my local machine I have a few extensions that I have come to depend on quite a bit. In particular, I find [jupyterlab_code_formatter](https://github.com/ryantam626/jupyterlab_code_formatter) extremely useful. Is there a way...

I keep getting this warning/error message. ![Screen Shot 2021-01-30 at 3 25 41 PM](https://user-images.githubusercontent.com/18236610/106370520-94615c80-630f-11eb-9f63-68f970db3263.png)