Tessferrandez/fix more doc issues (#1013)

* fix post workspace-template screenshot
* fix json in workspace creation sample
* add guacamole image tag to docs
* clean up register bundles

docs/tre-admins/registering-templates.md

@@ -4,39 +4,45 @@ To enable users to deploy Workspaces, Workspace Services or User Resources, we n
 
 ## Porter Bundles
 
-Templates are encapsulated in [Porter](https://porter.sh) bundles. Porter bundles can either be registered interactively using the Swagger UI or automatically using the utility script (useful in CI/CD scenarios). The script is provided at `/devops/scripts/publish_register_bundle.sh`.
+Templates are encapsulated in [Porter](https://porter.sh) bundles. Porter bundles can either be registered interactively using the Swagger UI or automatically using the `/devops/scripts/publish_register_bundle.sh` script (useful in CI/CD scenarios).
 
-The script can also be used to generate the payload required by the API without actually calling the API. The script carries out the following actions:
+This script can also be used to generate the payload required by the API without actually calling the API.
+
+It carries out the following actions:
 
 1. Publishes the bundle to the Azure Container Registry specified.
 1. Extracts the parameters from the bundle using `porter explain`.
 
 ### Registration using Swagger UI
 
-1. We will use the utility script to generate the payload. The script needs to be executed from within the bundle directory, for example `/templates/workspaces/base/`. This script can be used as follows:
+1. Build the porter bundle
+
+   ```cmd
+   porter build
+   ```
+
+1. Use the utility script to generate the payload. The script needs to be executed from within the bundle directory, for example `/templates/workspaces/base/`
 
    ```cmd
    ../../../devops/scripts/publish_register_bundle.sh -r <acr_name> -i -t workspace
    ```
 
-   Copy the resulting payload json.
+   Copy the resulting JSON payload.
 
-1. Navigate to the Swagger UI at `/docs`
-1. Log into the Swagger UI by clicking `Authorize`, then `Authorize` again. You will be redirected to the login page.
-1. Once logged in. Click `Try it out` on the `POST` `/api/workspace-templates` operation:
+1. Navigate to the Swagger UI at `/api/docs`
+1. Log into the Swagger UI using `Authorize`
+1. Click `Try it out` on the `POST` `/api/workspace-templates` operation:
 
-  ![Post Workspace Template](../assets/post-template.png)
+    ![Post Workspace Template](../assets/post-template.png)
 
 1. Paste the payload json generated earlier into the `Request body` field, then click `Execute`. Review the server response.
-1. To verify registration of the template do `GET` operation on `/api/workspace-templates`. The name of the template should now be listed.
+1. Verify the template registration using the `GET` operation on `/api/workspace-templates`. The name of the template should now be listed.
 
 ### Registration using script
 
-To use the script to automatically register the template, a user that does not require an interactive login must be created as per the [e2e test user documentation here](../tre-admins/auth.md#tre-e2e-test).
+To use the script to automatically register the template, you must create a user that does not require an interactive login per the [e2e test user documentation here](../tre-admins/auth.md#tre-e2e-test).
 
-The script needs to be executed from within the bundle directory, for example `/templates/workspaces/base/`.
-
-This script can be used as follows:
+The script needs to be executed from within the bundle directory, for example `/templates/workspaces/base/`
 
 ```cmd
 Usage: ../../../devops/scripts/publish_register_bundle.sh [-u --tre_url]  [-c --current] [-i --insecure]

docs/tre-admins/setup-instructions/installing-base-workspace.md

@@ -37,15 +37,11 @@ Go to ``azure_tre_fqdn/docs`` and use POST /api/workspaces with the sample body
 
 ```json
 {
-  "displayName": "manual-from-swagger",
-  "description": "workspace for team X",
   "workspaceType": "tre-workspace-base",
-  "parameters": {},
-  "authConfig": {
-    "provider": "AAD",
-    "data": {
-      "app_id": "app id created above"
-    }
+  "properties": {
+    "display_name": "manual-from-swagger",
+    "description": "workspace for team X",
+    "app_id": "app id created above"
   }
 }
 ```

docs/tre-templates/workspace-services/guacamole.md

@@ -22,6 +22,7 @@ Service Tags:
   | ------------------------- | ----------- |
   | `ID` | A GUID to identify the workspace service. The last 4 characters of this `ID` can be found in the resource names of the workspace service resources. |
   | `WORKSPACE_ID` | The GUID identifier used when deploying the base workspace bundle. |
+  | `GUACAMOLE_IMAGE_TAG` | The tag of the Guacamole Image to use - the tag will be the version (you can find the version in `templates\workspace\services\guacamole\version.txt`) |
 
 1. Build and install the Guacamole Service bundle
 

docs/tre-templates/workspace-services/inner-eye.md

@@ -24,39 +24,39 @@ URLs:
 
 1. Create a copy of `templates/workspace_services/innereye_deeplearning/.env.sample` with the name `.env` and update the variables with the appropriate values.
 
-  | Environment variable name | Description |
-  | ------------------------- | ----------- |
-  | `ID` | A GUID to identify the workspace service. The last 4 characters of this `ID` can be found in the resource names of the workspace service resources. |
-  | `WORKSPACE_ID` | The GUID identifier used when deploying the base workspace bundle. |
-  | `INFERENCE_SP_CLIENT_ID` | Service principal client ID used by the inference service to connect to Azure ML. Use the output from the step above. |
-  | `INFERENCE_SP_CLIENT_SECRET` | Service principal client secret used by the inference service to connect to Azure ML. Use the output from the step above. |
+    | Environment variable name | Description |
+    | ------------------------- | ----------- |
+    | `ID` | A GUID to identify the workspace service. The last 4 characters of this `ID` can be found in the resource names of the workspace service resources. |
+    | `WORKSPACE_ID` | The GUID identifier used when deploying the base workspace bundle. |
+    | `INFERENCE_SP_CLIENT_ID` | Service principal client ID used by the inference service to connect to Azure ML. Use the output from the step above. |
+    | `INFERENCE_SP_CLIENT_SECRET` | Service principal client secret used by the inference service to connect to Azure ML. Use the output from the step above. |
 
 1. Build and install the InnerEye Deep Learning Service bundle
 
-  ```cmd
-  make porter-build DIR=./templates/workspace_services/innereye
-  make porter-publish DIR=./templates/workspace_services/innereye
-  make porter-install DIR=./templates/workspace_services/innereye
-  ```
+    ```cmd
+    make porter-build DIR=./templates/workspace_services/innereye
+    make porter-publish DIR=./templates/workspace_services/innereye
+    make porter-install DIR=./templates/workspace_services/innereye
+    ```
 
 ## Running the InnerEye HelloWorld on AML Compute Cluster
 
 1. Log onto a VM in the workspace, open PowerShell and run:
 
-  ```cmd
-  git clone https://github.com/microsoft/InnerEye-DeepLearning
-  cd InnerEye-DeepLearning
-  git lfs install
-  git lfs pull
-  conda init
-  conda env create --file environment.yml
-  ```
+    ```cmd
+    git clone https://github.com/microsoft/InnerEye-DeepLearning
+    cd InnerEye-DeepLearning
+    git lfs install
+    git lfs pull
+    conda init
+    conda env create --file environment.yml
+    ```
 
 1. Restart PowerShell and navigate to the "InnerEye-DeepLearning" folder
 
-  ```cmd
-  conda activate InnerEye
-  ```
+    ```cmd
+    conda activate InnerEye
+    ```
 
 1. Open Azure Storage Explorer and connect to your Storage Account using name and access key
 1. On the storage account create a container with name ```datasets``` and a folder named ```hello_world```
@@ -79,11 +79,11 @@ The workspace service provisions an App Service Plan and an App Service for host
 
 1. Log onto a VM in the workspace and run:
 
-  ```cmd
-  git clone https://github.com/microsoft/InnerEye-Inference
-  cd InnerEye-Inference
-  az webapp up --name <inference-app-name> -g <resource-group-name>
-  ```
+    ```cmd
+    git clone https://github.com/microsoft/InnerEye-Inference
+    cd InnerEye-Inference
+    az webapp up --name <inference-app-name> -g <resource-group-name>
+    ```
 
 1. Create a new container in your storage account for storing inference images called `inferencedatastore`.
 1. Create a new folder in that container called `imagedata`.