Merge pull request #27 from blairdrummond/master

Fixed the layout and CD

.github/workflows/publish.sh → .github/workflows/publish.yml

@@ -1,14 +1,10 @@
 name: Publish
-
 on:
   push:
     branches: [ master ]
-
 jobs:
   build:
-
     runs-on: ubuntu-latest
-
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python 3.8
@@ -22,4 +18,6 @@ jobs:
 #        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
     - name: Build and publish
       run: |
+        rm -f docs/images
+        mv images docs/images
         mkdocs gh-deploy

.gitignore

@@ -1 +1,2 @@
 site
+.theme

docs/1-Experiments/Kubeflow.md

@@ -44,7 +44,7 @@ for your team.
 
 - Navigate to the Jupyter Servers tab
 
-![Kubeflow Manages the Jupyter Servers](../images/kubeflow_ui.png)
+![Kubeflow Manages the Jupyter Servers](../images/readme/kubeflow_ui.png)
 
 - Then click **+ New Server**
 

docs/index.md

@@ -1,5 +1,109 @@
----
-template: overrides/home.html
-title: DAaaS Covid-19 Analytics Platform
----
+# Starting on the DAaaS Platform
+
+![The DAaaS Platform](images/readme/portal_ui.png)
+
+**Link to the portal [here](portal.example.ca)**
+
+We'll break down the standard tasks into three categories
+
+1. **Experimentation / Analysis**
+
+2. **Publishing**
+
+3. **Large scale production**
+
+All are important, and we will address all of them, but we will place the most
+emphasis on the first two, as these are most widely applicable. 
+
+# For Experiments
+
+<!--  ![Kubeflow](images/logo-kubeflow.png){: style="max-height:200px"} -->
+
+## Jupyter notebooks
+
+ - `R` and `Python`, along with whatever other languages you might need.
+
+ - The machines can be as big as you need. They're for large analyses.
+
+ - You can easily share your workspace with your team, and share data and
+   notebooks.
+
+![Jupyter Notebooks](images/jupyter_in_action.png)
+
+## Desktops with ML-Workspace
+
+   Notebooks are more easily shared than desktops, but we also have the ability
+   to run Desktop applications (like QGIS, or VS-Code) in a browser. 
+
+[**Learn More**](1-Experiments/1-Kubeflow)
+
+# For Publishing
+
+## R Shiny
+
+![R Shiny](images/logo-RStudio.png){: style="max-height:100px; display: block; margin-left: auto; margin-right: auto;"}
+
+The platform is designed to host any kind of open source application you want.
+We have an R-Shiny server for hosting R-Shiny apps
+
+ ![R Shiny Server](images/readme/shiny_ui.png)
+
+To create any an R-Shiny Dashboard, you just have to submit a Github Pull
+request to the [Statcan Github](https://github.com/statcan/shiny). 
+
+## NodeJS, Flask
+
+![NodeJS Dashbaords](images/readme/covid_ui.png){: style="max-height:400px; display: block; margin-left: auto; margin-right: auto;"}
+
+You can also create custom apps. For example:
+
+ - Flask 
+ - Dash
+ - NodeJS or any other Open Source app in a Docker container
+
+!!! example "See an example NodeJS app we have"
+    The NodeJS pictured above can be found
+    [here](https://covid19.example.ca). This app is in [the Statcan
+    Github Repo](https://github.com/statcan/covid19). In the same way, you can
+    submit your own custom apps and we can host them on the platform.
+
+
+To create any any of these, create an issue on the 
+[Statcan Github](https://github.com/statcan/daaas) asking for help, 
+and we will add your application.
+
+
+# For Production
+
+If an experiment turns into a product, then one of the following may be needed:
+
+ - Kubeflow pipelines for high-volume/intensity work
+ - Automation pipelines
+
+![Kubeflow Pipelines](images/readme/kubeflow_pipeline.png)
+
+!!! tip "Ask for help in production."
+    The DAaaS engineers are happy to help with production oriented use
+    cases, and they can probably save you lots of time. Don't be shy to
+    ask them for help!
+
+# How do I get data? How do I submit data?
+
+![Browse Datasets](images/readme/minio_ui.png)
+
+ - Every workspace can be equipped with its own storage.
+
+ - There are also storage buckets to publish datasets too; either for
+   internal use or for wider release.
+
+We will give an overview of the technologies here, and in the next sections
+there will be a more in-depth FAQ of each of them. 
+
+!!! example "Browse some datasets"
+    Browse some [datasets](https://datasets.example.ca) here. These
+    data sets are meant to store widely shared data. Either data that has been
+    brought it, or data to be released out as a product. **As always, ensure
+    that the data is not sensitive.**
+
+
 

mkdocs.yml

@@ -35,11 +35,12 @@ repo_url: https://github.com/statcan/daaas
 
 # Page tree
 nav:
-  - Home: index.md
-  - Getting Started: Getting-Started.md
+  - Getting Started: index.md
   - Analysis:
     - Kubeflow: 1-Experiments/Kubeflow.md
+    - Jupyter: 1-Experiments/Jupyter.md
     - ML-Workspaces: 1-Experiments/ML-Workspaces.md
+    - DataBricks: 1-Experiments/Databricks.md
   - Publishing:
     - R Shiny: 2-Publishing/R-Shiny.md
   - Exploring: