GitBook: [#197] change notion

Author: Jeremy Ravenel

.gitbook/assets/Screenshot 2020-10-05 at 14.13.00.png

@@ -6,25 +6,27 @@ description: How to start using Naas in minutes.
 
 Naas **augments Jupyter Notebooks** by adding micro-services accessible in low-code **to easily access data, automation, and AI.**
 
-![](.gitbook/assets/screenshot-2021-06-27-at-02.42.14.png)
+![](<.gitbook/assets/Screenshot 2021-06-27 at 02.42.14.png>)
 
 The product is based on 3 elements: **features, drivers, and templates.**
 
-  
-The **templates** enable "data geeks" to kickstart projects in minutes. They may include low-code **drivers** that act as super connectors to facilitate access to tools, and complex libraries \(database, API, ML algorithm...\) while the low-code **features** \(scheduling, asset sharing, notifications...\) enable faster iteration and deployment of outputs to end-users, in a headless way.  
+\
+The **templates** enable "data geeks" to kickstart projects in minutes. They may include low-code **drivers** that act as super connectors to facilitate access to tools, and complex libraries (database, API, ML algorithm...) while the low-code **features** (scheduling, asset sharing, notifications...) enable faster iteration and deployment of outputs to end-users, in a headless way.\
 
 
-Naas is forever free to use with 100 credits/month on its hosted version [www.naas.ai](https://www.naas.ai/).  
-[👉Open your account](https://www.naas.ai/free-forever)  
+Naas is forever free to use with 100 credits/month on its hosted version [www.naas.ai](https://www.naas.ai).\
+[👉Open your account](https://www.naas.ai/free-forever)\
 
 
 _If you contribute to this library of open-source notebooks templates, you can X2 your monthly credits 🏆_
 
 ## Local installation
 
-If you want to use Naas on your local Jupyter environment, it's free and open-source, just follow the procedure below  : 
+If you want to use Naas on your local Jupyter environment, it's free and open-source, just follow the procedure below  :&#x20;
 
-{% page-ref page="advanced/local-install.md" %}
+{% content-ref url="advanced/local-install.md" %}
+[local-install.md](advanced/local-install.md)
+{% endcontent-ref %}
 
 ### Why Naas?
 
@@ -48,14 +50,16 @@ import naas
 
 ### Schedule your notebook
 
-Send in production this notebook and run it, every day at 9:00 
+Send in production this notebook and run it, every day at 9:00&#x20;
 
 ```python
 # do stuff in your notebook
 naas.scheduler.add(recurrence="0 9 * * *")
 ```
 
-{% page-ref page="features/scheduler.md" %}
+{% content-ref url="features/scheduler.md" %}
+[scheduler.md](features/scheduler.md)
+{% endcontent-ref %}
 
 ### Add a dependency
 
@@ -65,7 +69,9 @@ Send in production any file type like `test.csv` as a dependency:
 naas.dependency.add("test.csv")
 ```
 
-{% page-ref page="features/dependency.md" %}
+{% content-ref url="features/dependency.md" %}
+[dependency.md](features/dependency.md)
+{% endcontent-ref %}
 
 ### Add a secret key
 
@@ -81,14 +87,20 @@ Remove the previous line and get your secret key with :
 naas.secret.get(name="MY_API_KEY")
 ```
 
-This allows you to push your notebook in production without sensitive data getting exposed. 
+This allows you to push your notebook in production without sensitive data getting exposed.&#x20;
 
-{% page-ref page="features/secret.md" %}
+{% content-ref url="features/secret.md" %}
+[secret.md](features/secret.md)
+{% endcontent-ref %}
 
 ## Advanced features
 
 If you use Naas cloud they all work natively, otherwise go to :
 
+{% content-ref url="broken-reference" %}
+[Broken link](broken-reference)
+{% endcontent-ref %}
+
 ### Use Notebooks as API
 
 Copy in production this notebook and allow to run it by calling the returned URL:
@@ -99,23 +111,27 @@ naas.api.add()
 
 Call the URL with your navigator you will get a message and see the notebook has run.
 
-If you want to download the notebook result instead, add this line: 
+If you want to download the notebook result instead, add this line:&#x20;
 
 ```python
 naas.api.respond_notebook()
 ```
 
-{% page-ref page="features/api.md" %}
+{% content-ref url="features/api.md" %}
+[api.md](features/api.md)
+{% endcontent-ref %}
 
 ### Expose assets
 
-Copy in production this asset \( file \) and allow to get it by calling the returned url:
+Copy in production this asset ( file ) and allow to get it by calling the returned url:
 
 ```python
 link = naas.assets.add("tesla-chart.html")
 ```
 
-{% page-ref page="features/asset.md" %}
+{% content-ref url="features/asset.md" %}
+[asset.md](features/asset.md)
+{% endcontent-ref %}
 
 ### Send notifications
 
@@ -129,7 +145,9 @@ content = "check in the link the chart data maide from fresh dataset : " + link
 naas.notifications.send(email=email, subject=subject, content=content)
 ```
 
-{% page-ref page="features/notification.md" %}
+{% content-ref url="features/notification.md" %}
+[notification.md](features/notification.md)
+{% endcontent-ref %}
 
 ## Help
 
@@ -161,4 +179,3 @@ Show a button to quickly open this documentation from Jupyter
 import naas
 naas.doc()
 ```
-

.gitbook/assets/Screenshot 2020-10-07 at 18.33.19.png

@@ -28,24 +28,24 @@ dl_url = naas.get_download_url(url)
 
 Result :
 
-![](../.gitbook/assets/image%20%287%29%20%282%29.png)
+![](<../.gitbook/assets/image (5).png>)
 
 ```
 https://app.naas.ai/user-redirect/naas/downloader?url=YOURURL
 ```
 
 {% hint style="info" %}
-Options: mode\_api 
+Options: mode\_api&#x20;
 
-with &mode\_api=yes that create the file in the user but don't open it. Useful for admin 
+with \&mode\_api=yes that create the file in the user but don't open it. Useful for admin&#x20;
 {% endhint %}
 
 {% hint style="info" %}
-Options: name 
+Options: name&#x20;
 
 with ?name=toto that create a file in the user with the name provided \`toto\`
 
- if no URL provided it create an empty file. 
+&#x20;if no URL provided it create an empty file.&#x20;
 {% endhint %}
 
 ## Run
@@ -57,16 +57,16 @@ import naas
 naas.run()
 ```
 
-## Changelog \(deprecated\)
+## Changelog (deprecated)
 
-Show changelog 
+Show changelog&#x20;
 
 ```python
 import naas
 naas.changelog()
 ```
 
-## Feature request \(deprecated\)
+## Feature request (deprecated)
 
 show feature request inside Jupyter
 
@@ -98,7 +98,7 @@ naas.reload_jobs()
 
 This represents the Naas env vars, you can get all to make your script work \`naas.n\_env\`
 
-```text
+```
 api_port
 current
 version
@@ -132,12 +132,11 @@ get data in the production of the current running notebook.
 
 For now current gives you this :
 
-![](../.gitbook/assets/image%20%284%29.png)
+![](<../.gitbook/assets/image (4).png>)
 
 ```python
 import naas
 print(naas.n_env.current)
 # This will be empty in dev = in notebook run by you
 # and with vars when run by naas runner ( scheduler or webhook )
 ```
-

.gitbook/assets/Screenshot 2020-10-07 at 18.34.12.png

@@ -6,5 +6,4 @@ description: Naas use CI /CD system to test and deploy new version on the fly
 
 Below the schemat of our CD system
 
-![](../.gitbook/assets/screenshot-2021-04-07-at-02.52.29.png)
-
+![](<../.gitbook/assets/Screenshot 2021-04-07 at 02.52.29.png>)

.gitbook/assets/Screenshot 2020-10-07 at 18.35.42.png

@@ -16,9 +16,9 @@ print(os.getenv('NB_USER'), os.getenv('JUPYTERHUB_USER'), os.getenv('JUPYTERHUB_
 
 #### Env requirements
 
-* `NB_USER` => Should be set as your notebook user, probably `joyvan`
-* `JUPYTERHUB_USER` => Should be set as your machine user, not root
-* `JUPYTERHUB_API_TOKEN` => should be auto-set by your hub
+* `NB_USER` => Should be set as your notebook user, probably `joyvan`
+* `JUPYTERHUB_USER` => Should be set as your machine user, not root
+* `JUPYTERHUB_API_TOKEN` => should be auto-set by your hub
 
 ## Install
 
@@ -36,9 +36,13 @@ You can test on your local computer only the Scheduler feature.
 Full install needs Kubernetes and Docker. Let's talk.
 {% endhint %}
 
+{% content-ref url="broken-reference" %}
+[Broken link](broken-reference)
+{% endcontent-ref %}
+
 ## Start
 
-Start the server in your Jupyter singleuser machine: 
+Start the server in your Jupyter singleuser machine: 
 
 ```bash
 !python -m naas.runner &
@@ -50,7 +54,7 @@ You can now delete previous cells
 
 It will run until you stop your Jupyter server, go back to:
 
-{% page-ref page="../" %}
-
-
+{% content-ref url="../" %}
+[..](../)
+{% endcontent-ref %}
 

.gitbook/assets/Screenshot 2020-11-14 at 14.34.13.png

@@ -16,17 +16,17 @@ Below the set of tools we run to build the perfect notebook experience.
 Documentation on how to install them is coming soon.
 {% endhint %}
 
-### [Naas](https://hub.docker.com/r/jupyternaas/naas) 
+### [Naas](https://hub.docker.com/r/jupyternaas/naas) 
 
 The magic server who takes care of your notebooks.
 
-### [Proxy](https://hub.docker.com/r/jupyternaas/proxy) 
+### [Proxy](https://hub.docker.com/r/jupyternaas/proxy) 
 
 The proxy who expose the different URL for your notebooks.
 
 ### [Notification](https://hub.docker.com/r/jupyternaas/notifications)
 
-The server sending rich email notifications to yourself \(or your audience\).
+The server sending rich email notifications to yourself (or your audience).
 
 ### [Screenshot](https://hub.docker.com/r/jupyternaas/screenshot)
 
@@ -58,15 +58,15 @@ One of the principal challenge with Naas is that it needs to fit user demands th
 
 ### Infrastructure Diagram
 
-![](../.gitbook/assets/image%20%283%29.png)
+![](../.gitbook/assets/image.png)
 
 This design allow us to deploy infrastructure into 3 different locations at the same time. Kubernetes nodes are started as the demand is growing and stopped when single users servers are shutting down.
 
 ### Infrastructure as code
 
-We needed a solution to organise the deployment of the infrastructure, doing everything in the console was not a solution as it is very difficult to replicate the changes into different account \(dev, production, ... \).
+We needed a solution to organise the deployment of the infrastructure, doing everything in the console was not a solution as it is very difficult to replicate the changes into different account (dev, production, ... ).
 
-![](../.gitbook/assets/image%20%282%29.png)
+![](<../.gitbook/assets/image (1).png>)
 
 Terraform was definitely the way to go, we can version our infrastructure into Github and verify what modifications will happen on the infrastructure before actually applying the changes.
 
@@ -81,7 +81,7 @@ terraform apply
 
 #### EKS Configuration Example
 
-![](../.gitbook/assets/image.png)
+![](<../.gitbook/assets/image (2).png>)
 
 We are using the [kubernetes/autoscaler](https://github.com/kubernetes/autoscaler) with two different worker groups to separate the scaling and deployment of NAAS and Jupyter instances. That way Jupyter instances won't interfer with NAAS deployment.
 
@@ -91,33 +91,32 @@ We are using the [kubernetes/autoscaler](https://github.com/kubernetes/autoscale
 
 #### How hard it is to deploy EFS?
 
-![](../.gitbook/assets/image%20%281%29.png)
+![](<../.gitbook/assets/image (3).png>)
 
 ### Unleashing RDS
 
 Because databases are critical parts of almsot any infrastructure, we chose to rely on a managed service and did use [RDS](https://aws.amazon.com/rds/) for our PostgreSQL instance. Using this we can focus on building NAAS and not worry about the database anymore.
 
 ### A word about security
 
-Because all of this is sensitive, we enforce a strict password policy and the use of MFA \(for console and programatic access\) to every member of our team and we do follow the [Security Best practices in IAM](https://docs.aws.amazon.com/IAM/latest/UserGuide/best-practices.html) encouraged by AWS.
+Because all of this is sensitive, we enforce a strict password policy and the use of MFA (for console and programatic access) to every member of our team and we do follow the [Security Best practices in IAM](https://docs.aws.amazon.com/IAM/latest/UserGuide/best-practices.html) encouraged by AWS.
 
 ## 🔥 Deployments
 
 So as we saw earlier we are using Terraform to deploy the underlying infrastructure powering Naas, but what about Naas itself. We use multiple tools to manage our deployments.
 
 ### Helm
 
-[Helm](https://helm.sh/) allows use to write [charts](https://helm.sh/docs/topics/charts/) that define how our applications \(docker containers mainly\) should work in Kubernetes. It's a very powerful tool and we are deploying everything in our EKS cluster with it. This allows us to version our deployments as well and keep track of every modifications.
+[Helm](https://helm.sh) allows use to write [charts](https://helm.sh/docs/topics/charts/) that define how our applications (docker containers mainly) should work in Kubernetes. It's a very powerful tool and we are deploying everything in our EKS cluster with it. This allows us to version our deployments as well and keep track of every modifications.
 
 ### Skaffold
 
-As we have many charts, it can become complicated to deploy them, but also difficult to share values and secrets between charts. [Skaffold](https://skaffold.dev/) allow us to easily create [profiles](https://skaffold.dev/docs/references/yaml/#profiles) for every chart and referencce `values.yaml` and `secrets.yaml`.
+As we have many charts, it can become complicated to deploy them, but also difficult to share values and secrets between charts. [Skaffold](https://skaffold.dev) allow us to easily create [profiles](https://skaffold.dev/docs/references/yaml/#profiles) for every chart and referencce `values.yaml` and `secrets.yaml`.
 
 For example if we need to deploy a new version of our Jupyter deployment we simply need to run `skaffold deploy -p jupyter` and skaffold will handle everything.
 
 ### SOPS
 
-Because storing and deploying secrets can be a difficult part \(I mean if you really want to do it properly and stay safe\) we chose to use [SOPS](https://github.com/mozilla/sops) wrapped by [helm-secrets](https://github.com/zendesk/helm-secrets). SOPS allow us to store our secrets in YAML files and encrypt its content using a [KMS](https://aws.amazon.com/kms/) key in AWS. The wonderful thing about all this is that we simply need to add the right to `encrypt` /`decrypt` to a user to be able to see the secret and modify them. If we want to limit this right to a certain list of users for example we can definitely do it and still store the yams file containing the secrets in the repository, only users with propers right will be able to see its content.
+Because storing and deploying secrets can be a difficult part (I mean if you really want to do it properly and stay safe) we chose to use [SOPS](https://github.com/mozilla/sops) wrapped by [helm-secrets](https://github.com/zendesk/helm-secrets). SOPS allow us to store our secrets in YAML files and encrypt its content using a [KMS](https://aws.amazon.com/kms/) key in AWS. The wonderful thing about all this is that we simply need to add the right to `encrypt` /`decrypt` to a user to be able to see the secret and modify them. If we want to limit this right to a certain list of users for example we can definitely do it and still store the yams file containing the secrets in the repository, only users with propers right will be able to see its content.
 
 In addition to that, Skaffold is able to handle helm-secrets which means that when we provide a `secrets.yaml` to a Skaffold profile, it will know what to do to decrypt its content.
-

.gitbook/assets/Screenshot 2021-03-02 at 14.38.47.png

@@ -4,20 +4,20 @@ After few months of using our own product and with the help of all of you we hav
 
 Use Mainly Naas as ETL :
 
-{% embed url="https://en.wikipedia.org/wiki/Extract,\_transform,\_load" %}
+{% embed url="https://en.wikipedia.org/wiki/Extract,_transform,_load" %}
 
 Not a crypto miner or any other Fancy idea, it will work as his best when :
 
 Create a folder in your Naas by project, ideally, in this project, you should find:
 
 * **Input** folder where you put every file you use as dependency to your notebook.
-* **Output** folder ****where you save all results of your notebook, intermediary or final
-* **Script** folder where you put your notebooks, all with a clear name of what they do.
-* **deploy.ipynb** who is there to list and send all your sandbox files in production, it will prevent you to send your work in production by restarting a kernel!
+* **Output **folder** **where you save all results of your notebook, intermediary or final
+* **Script **folder where you put your notebooks, all with a clear name of what they do.
+* **deploy.ipynb **who is there to list and send all your sandbox files in production, it will prevent you to send your work in production by restarting a kernel!
 
-Use **real Database** for storing purposes, CSVs are great but please use it as debug, not for storing every move from excel loses all its meaning! 
+Use **real Database** for storing purposes, CSVs are great but please use it as debug, not for storing every move from excel loses all its meaning! 
 
-To do so, use our Mongo connector and a **free instance** from Mongo Atlas, you have **512MB** a good point to start  :
+To do so, use our Mongo connector and a **free instance** from Mongo Atlas, you have **512MB **a good point to start  :
 
 {% embed url="https://www.mongodb.com/pricing" %}
 
@@ -27,5 +27,4 @@ To do so, use our Mongo connector and a **free instance** from Mongo Atlas, you
 
 * Don't use emoji, space, or weird characters this will lead to errors just for a name, do you really what to find that the name \`$t0️⃣ck of W/-\ll $street.csv\` was causing your bug after 10 hours?
 * Don't create your own scheduler within the scheduler, like schedule every minute a script to choose which action to do. It will create a ton of output file, max you file storage very fast and debug with be a crazy hell!
-* Don't put password or sensitive information in your notebook, for many reason this is not the rigth place to store them,  use our secret system instead, it store them encoded on your machine, not perfect but much better !  
-
+* Don't put password or sensitive information in your notebook, for many reason this is not the rigth place to store them,  use our secret system instead, it store them encoded on your machine, not perfect but much better !