CD added; no custom theme

.github/workflows/publish.sh

@@ -0,0 +1,37 @@
+name: Publish
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+#     strategy:
+#       matrix:
+#         python-version: [3.5, 3.6, 3.7, 3.8]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.8
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8 mkdocs mkdocs-material
+#        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+#     - name: Lint with flake8
+#       run: |
+#         # stop the build if there are Python syntax errors or undefined names
+#         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+#         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+#         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Build and publish
+      run: |
+        mkdocs gh-deploy
+

.replace-hostname.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+
+TRUE="not-telling-you-the-hostname.ca"
+FAKE="example.ca"
+
+find . -type f -name '*.md' | tee >(xargs sed -i "s/$TRUE/$FAKE/g")

docs/1-Experiments/Jupyter.md

@@ -1,5 +1,66 @@
 # Jupyter
 
+## Friendly R and Python experience
+
+Jupyter gives you **notebooks** to write your code and make visualizations. You
+can quickly iterate, visualize, and sahre your analyses. Because it's running on
+a server (that you set up in the last section) you can do really big analyses on
+centralized hardware! Adding as much horsepower as you need! And because it's on
+the cloud, you can share it with your colleagues too.
+
+![Embed your visualizations](../images/jupyter_scikit.png)
+
+
+## Get started with the examples
+
+When you started your server, it got loaded with a bunch of example notebooks.
+Great notebooks to start with are `R/01-R-Notebook-Demo.ipynb`, or the notebooks
+in `scikitlearn`. `pytorch` and `tensorflow` are great if you are familiar with
+machine learning. The `mapreduce-pipeline` and `ai-pipeline` are more advanced.
+
+??? danger "Some notebooks only work in certain server versions"
+    For instance, `gdal` is only in the geomatics image. So if you use another
+    image then a notebook using gdal might not work.
+
+## Adding software
+
+You do not have `sudo` in Jupyter, but you can use 
+
+```sh
+conda install --use-local your_package_name
+```
+
+or
+
+```sh
+pip install --user your_package_name
+```
+
+**Don't forget to restart your jupyter kernel afterwards, to make new packages
+available.****
+
+??? tip "Make sure to restart the Jupyter kernel after installing new software"
+    If you install software in a terminal, but your jupyter kernel was already
+    running, then it will not be updated.
+
+??? tip "Is there something that you can't install?"
+    If you need something installed, reach us or [open a github issue](https://github.com/StatCan/kubeflow-containers).
+    We can add it to the default software.
+
+
+# Getting Data in and out of Jupyter
+
+You can upload and download data to/from Jupyterhub directly in the menu. There
+is an upload button at the top, and you can right-click most files or folders to
+download them.
+
+# TODO S3 browser?
+
+
+
+
+
+
 
 !!! warning "Phasellus posuere in sem ut cursus"
     Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nulla et euismod

docs/1-Experiments/Kubeflow.md

@@ -1,8 +1,8 @@
-# Experiments with Kubeflow 
+# Getting started with Kubeflow
 
 ## What does Kubeflow do?
 
-Kubeflow runs your workspaces. You can have notebook servers (called Jupyter
+Kubeflow runs your **workspaces**. You can have notebook servers (called Jupyter
 Servers), and in them you can create analyses in R and Python with interactive
 visuals. You can save & upload data, download it, and create shared workspaces
 for your team.
@@ -11,9 +11,9 @@ for your team.
 
 **Let's get started!**
 
-## Create a Server
+# Create a Server
 
-### Log into Kubeflow
+## Log into Kubeflow
 
 - Log into [the azure portal](https://portal.azure.com) **using your
   cloud.statcan credentials**.
@@ -24,7 +24,7 @@ for your team.
     ![Azure Portal: Choose the `@cloud.statcan.ca` address](../images/azure-login.png)
 
 
-- After logging into Azure, log into [kubeflow](https://kubeflow.covid.cloud.statcan.ca)
+- After logging into Azure, log into [kubeflow](https://kubeflow.example.ca)
 
 
 ??? failure "Why am I getting "Missing url parameter: code"?"
@@ -48,7 +48,7 @@ for your team.
 
 - Then click **+ New Server**
 
-### Configuring your server
+## Configuring your server
 
 - You will get a template to create your notebook server.
   **Note:** the name must be lowercase letters with hypens. **No spaces, and no underscores.**
@@ -65,7 +65,7 @@ for your team.
 - If you want to use a gpu, check if the image says **cpu** or **gpu**.
 
 
-### CPU and Memory 
+## CPU and Memory 
 
 - At the time of writing (April 21, 2020) there are two types of computers in
   the cluster
@@ -82,7 +82,7 @@ for your team.
 !!! note "Use GPU machines responsibly"
     There are fewer GPU machines than CPU machines, so use them responsibly.
 
-### Storing your data
+## Storing your data
 
 - You'll want to create a data volume! You'll be able to save your work here,
   and if you shut down your server, you'll be able to just remount your old data
@@ -98,7 +98,7 @@ for your team.
     to find your old disk, but it takes time.)
 
 
-### And... Create!!!
+## And... Create!!!
 
 - If you're satisfied with the settings, you can now create the server! It may
   take a few minutes to spin up depending on the resources you asked for. (GPUs
@@ -107,5 +107,26 @@ for your team.
 !!! success "Your server is running"
     If all goes well, your server should be running!!! You will now have the
     option to connect, and [try out Jupyter!](/1-Experiments/Jupyter)
+
 
-
+# Share your workspace
+
+In kubeflow every user has a **namespace**. Your namespace belongs to you, and
+it's where all your resources live. If you want to collaborate with someone you
+need to share a namespace. So you can do that either by sharing your own
+namespace, or more preferably, **by creating a team namespace**. 
+
+## Create a new shared namespace
+
+The link to create a new namespace is here
+- TODO THERE IS NO LINK YET.
+
+## Manage contributors
+
+You can add or remove people from a namespace you own through the **Manage
+Contributors** menu in kubeflow.
+
+![Contributors Menu](../images/kubeflow_contributors.png)
+
+!!! success "Now you and your colleagues can share access to a server!"
+    Now you can share a server with colleagues! Try it out!

docs/Getting-Started.md

@@ -61,7 +61,7 @@ You can also create custom apps. For example:
 
 !!! example "See an example NodeJS app we have"
     The NodeJS pictured above can be found
-    [here](https://covid19.covid.cloud.statcan.ca). This app is in [the Statcan
+    [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.
 
@@ -98,7 +98,7 @@ 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.covid.cloud.statcan.ca) here. These
+    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

@@ -4,7 +4,6 @@ site_description: User guide for the DAaaS Covid-19 Platform
 copyright: Copyright © 2020 Statistics Canada
 theme:
   name: material
-  custom_dir: .theme
   #features:
   #  - tabs
   palette: