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I fixed typo errors and structure

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lumenCodes
2022-10-28 15:50:47 +01:00
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docs/source/getting-started/institutional-faq.md
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@@ -8,10 +8,15 @@ broken down by their roles within organizations.
### Is it appropriate for adoption within a larger institutional context?
Yes! JupyterHub has been used at-scale for large pools of users, as well
as complex and high-performance computing. For example, UC Berkeley uses
as complex and high-performance computing.
For example,
- UC Berkeley uses
JupyterHub for its Data Science Education Program courses (serving over
3,000 students). The Pangeo project uses JupyterHub to provide access
to scalable cloud computing with Dask. JupyterHub is stable and customizable
3,000 students).
- The Pangeo project uses JupyterHub to provide access
to scalable cloud computing with Dask.
JupyterHub is stable and customizable
to the use-cases of large organizations.
### I keep hearing about Jupyter Notebook, JupyterLab, and now JupyterHub. What’s the difference?
@@ -26,7 +31,7 @@ Here is a quick breakdown of these three tools:
has several extensions that are tailored for using Jupyter Notebooks, as well as extensions
for other parts of the data science stack.
- **JupyterHub** is an application that manages interactive computing sessions for **multiple users**.
It also connects them with infrastructure those users wish to access. It can provide
It also connects them(sessions) with infrastructure those users wish to access. It can provide
remote access to Jupyter Notebooks and JupyterLab for many people.
## For management
@@ -35,7 +40,7 @@ Here is a quick breakdown of these three tools:
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,
the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also allows administrators
the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also gives administrators
some control over access to resources, security, environments, and authentication.
### Is JupyterHub mature? Why should we trust it?
@@ -99,12 +104,12 @@ that we currently suggest are:
guide that runs on Kubernetes. Better for larger or dynamic user groups (50-10,000) or more complex
compute/data needs.
- [The Littlest JupyterHub](https://tljh.jupyter.org) is a lightweight JupyterHub that runs on a single
single machine (in the cloud or under your desk). Better for smaller user groups (4-80) or more
machine (in the cloud or under your desk). Better for smaller user groups (4-80) or more
lightweight computational resources.
### Does JupyterHub run well in the cloud?
Yes - most deployments of JupyterHub are run via cloud infrastructure and on a variety of cloud providers.
**Yes** - most deployments of JupyterHub are run via cloud infrastructure and on a variety of cloud providers.
Depending on the distribution of JupyterHub that you'd like to use, you can also connect your JupyterHub
deployment with a number of other cloud-native services so that users have access to other resources from
their interactive computing sessions.
@@ -118,7 +123,8 @@ as more resources are needed - allowing you to utilize the benefits of a flexibl
### Is JupyterHub secure?
The short answer: yes. JupyterHub as a standalone application has been battle-tested at an institutional
The short answer: yes.
JupyterHub as a standalone application has been battle-tested at an institutional
level for several years, and makes a number of "default" security decisions that are reasonable for most
users.
@@ -134,11 +140,11 @@ in these cases, and the security of your JupyterHub deployment will often depend
If you are worried about security, don't hesitate to reach out to the JupyterHub community in the
[Jupyter Community Forum](https://discourse.jupyter.org/c/jupyterhub). This community of practice has many
individuals with experience running secure JupyterHub deployments.
individuals with experience running secure JupyterHub deployments and will be very glad to help you out.
### Does JupyterHub provide computing or data infrastructure?
No - JupyterHub manages user sessions and can _control_ computing infrastructure, but it does not provide these
**No** - JupyterHub manages user sessions and can _control_ computing infrastructure, but it does not provide these
things itself. You are expected to run JupyterHub on your own infrastructure (local or in the cloud). Moreover,
JupyterHub has no internal concept of "data", but is designed to be able to communicate with data repositories
(again, either locally or remotely) for use within interactive computing sessions.
@@ -191,7 +197,7 @@ complex computing infrastructures from the interactive sessions of a JupyterHub.
This is highly configurable by the administrator. If you wish for your users to have simple
data analytics environments for prototyping and light data exploring, you can restrict their
memory and CPU based on the resources that you have available. If you'd like your JupyterHub
to serve as a gateway to high-performance compute or data resources, you may increase the
to serve as a gateway to high-performance computing or data resources, you may increase the
resources available on user machines, or connect them with computing infrastructures elsewhere.
### Can I customize the look and feel of a JupyterHub?