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more content for institutional faq

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Chris Holdgraf
2019-11-13 16:20:25 -08:00
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docs/source/getting-started/institutional-faq.md
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@@ -20,14 +20,14 @@ Here is a quick breakdown of these three tools:
* **The Jupyter Notebook** is a document specification (the `.ipynb`) file that interweaves * **The Jupyter Notebook** is a document specification (the `.ipynb`) file that interweaves
narrative text with code cells and their outputs. It is also a graphical interface narrative text with code cells and their outputs. It is also a graphical interface
that allows users to edit these documents that allows users to edit these documents. There are also several other graphical interfaces
that allow users to edit the `.ipynb` format (nteract, Jupyer Lab, Google Colab, Kaggle, etc).
* **JupyterLab** is a flexible and extendible user interface for interactive computing. It * **JupyterLab** is a flexible and extendible user interface for interactive computing. It
has several extensions that are tailored for using Jupyter Notebooks, as well as extensions has several extensions that are tailored for using Jupyter Notebooks, as well as extensions
for other parts of the data science stack. for other parts of the data science stack.
* **JupyterHub** is an application that can manage **multiple users** with interactive computing * **JupyterHub** is an application that manages interactive computing sessions for **multiple users**.
sessions, as well as connect with infrastructure those users wish to access. It can provide It also connects them with infrastructure those users wish to access. It can provide
remote access to Jupyter Notebooks and Jupyter Lab for many people, and can connect them with remote access to Jupyter Notebooks and Jupyter Lab for many people.
other compute infrastructure.
# For management # For management
@@ -36,7 +36,7 @@ Here is a quick breakdown of these three tools:
JupyterHub provides a shared platform for data science and collaboration. JupyterHub provides a shared platform for data science and collaboration.
It allows users to utilize familiar data science workflows (such as the scientific python stack, It allows users to utilize familiar data science workflows (such as the scientific python stack,
the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also allows administrators the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also allows administrators
some control over access to resources, security, authentication, and user identity. some control over access to resources, security, environments, and authentication.
## Is JupyterHub mature? Why should we trust it? ## Is JupyterHub mature? Why should we trust it?
@@ -57,17 +57,18 @@ industry, and governmental research labs. It is most-commonly used by two kinds
## How does JupyterHub compare with hosted products, like Google Colaboratory, RStudio.cloud, or Anaconda Enterprise? ## How does JupyterHub compare with hosted products, like Google Colaboratory, RStudio.cloud, or Anaconda Enterprise?
Like the tools listed above, JupyterHub provides access to interactive computing JupyterHub puts you in control of your data, infrastructure, and coding environment.
environments in the cloud. However, JupyterHub is more flexible, more customizable, In addition, it is vendor neutral, which reduces lock-in to a particular vendor or service.
free, and gives administrators more control over their setup and hardware. JupyterHub provides access to interactive computing environments in the cloud (similar to each of these services).
Compared with the tools above, it is more flexible, more customizable, free, and
gives administrators more control over their setup and hardware.
Because JupyterHub is an open-source, community-driven tool, it can be extended and Because JupyterHub is an open-source, community-driven tool, it can be extended and
modified to fit an institution's needs. It plays nicely with the open source data science modified to fit an institution's needs. It plays nicely with the open source data science
stack, and can serve a variety of computing enviroments, user interfaces, and stack, and can serve a variety of computing enviroments, user interfaces, and
computational hardware. computational hardware. It can also be deployed anywhere - on enterprise cloud infrastructure, on
High-Performance-Computing machines, on local hardware, or even on a single laptop, which
Finally, JupyterHub can be deployed anywhere - on enterprise cloud infrastructure, on is not possible with most other tools for shared interactive computing.
High-Performance-Computing machines, on local hardware, or even on a single laptop.
# For IT # For IT
@@ -108,6 +109,11 @@ The short answer: yes. JupyterHub as a standalone application has been battle-te
level for several years, and makes a number of "default" security decisions that are reasonable for most level for several years, and makes a number of "default" security decisions that are reasonable for most
users. users.
* For security considerations in the base JupyterHub application,
[see the JupyterHub security page](https://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html)
* For security considerations when deploying JupyterHub on Kubernetes, see the
[JupyterHub on Kubernetes security page](https://zero-to-jupyterhub.readthedocs.io/en/latest/security.html).
The longer answer: it depends on your deployment. Because JupyterHub is very flexible, it can be used The longer answer: it depends on your deployment. Because JupyterHub is very flexible, it can be used
in a variety of deployment setups. This often entails connecting your JupyterHub to **other** infrastructure in a variety of deployment setups. This often entails connecting your JupyterHub to **other** infrastructure
(such as a [Dask Gateway service](https://gateway.dask.org/)). There are many security decisions to be made (such as a [Dask Gateway service](https://gateway.dask.org/)). There are many security decisions to be made