Collaborative Calculation and Data Science

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There are now 3 new [compute server](https://doc.cocalc.com/compute_server.html) images:

- Anaconda
- JupyterHub
- Kubernetes Node

<img width="816" alt="image" src="https://github.com/sagemathinc/cocalc/assets/1276278/d698c96c-f2b8-4067-ac9f-28ebbcd32d8d">

## Anaconda

The Anaconda image is a lightweight image with the conda command installed and configured (via mambaforge), and two channels, anaconda and conda-forge, enabled by default. You get Python 3.11 and can very easily install packages into your compute server's environment using the conda command, e.g., install Matplotlib:

```sh
(compute-server-1540) ~/anaconda$ conda install matplotlib
```

The packages you install are stored in /conda only on the compute server, so installing and using the packages is fast, and if you make the compute server disk large, you can install many packages.

<img width="1014" alt="image" src="https://github.com/sagemathinc/cocalc/assets/1276278/f657d682-4151-4a0b-adf9-53a6031a0e74">


## JupyterHub

Th JupyterHub image is a single-node Kubernetes install of JupyterHub, which can be fully customized by you [exactly as explained](https://z2jh.jupyter.org/en/stable/jupyterhub/customization.html) in the official docs (or email help@sagemath.com for support!).  Click to create it, and wait for everything to install.  **It can take several minutes to start the first time, so please be patient.**  There is a random registration token which has to be entered to connect to JupyterHub; once you do that the default auth is that anybody can then sign in with any login/password (that's just the JupyterHub default).   The default image is also very simple, but you can easily change it as documented above. 

This is a single node deployment by default, but scaling up to multiple nodes does work, though it requires some copy/paste on the command line.  (We will automate this in the future.)

## Kubenetes Node

You can create a Kubernetes node. This is a single node Kubernetes cluster by default.  However, you can join it to an existing cluster following the microk8s directions.  E.g., you could expand a JupyterHub install to have multiple nodes.   


Compute Servers: Anaconda and JupyterHub

Nested Virtualization

Compute Servers -- Serial Port Output

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