Make links to Cloud Console open in a new page (#476)

* integrate custom docs with new UI

* more edits

* use website wording for intro

* fix numbering in table

* rename and some edits

* rename manage_repo file, per Bo

* Merge.

* formatting edits

* updates per Keyur's feedback

* Fix typos

* fix nav order

* fix link to API key request form

* update form link

* update key request form and output dir env var

* Revert to gerund

Though the style guide says to just use imperatives, "get started" just sounds weird. Also this is more consistent with "troubleshooting"

* new troubleshooting entry

* fix typo

* new data container procedures

* more work

* more work

* complete data draft

* more changes

* more changes

* more revisions

* update troubleshooting doc etc.

* new version of diagrams

* remove data loading problems troubleshooting entry; can't reproduce

* revert title change

* add example for not mixing entity types

* changes from Keyur

* add screenshots for GCP, and related changes

* fixed one image

* added screenshots for Cloud Run service

* resize images

* more changes from Keyur

* fix a tiny error

* delete unused images

* fix missing dash

* update build file

* adjust build command

* Revert "adjust build command"

This reverts commit 4ce0fb9.

* update docker file

* more fixes

* one last fix

* make links to Cloud Console open in a new page

custom_dc/data_cloud.md

@@ -23,7 +23,7 @@ Alternatively, if you have a very large data set, you may find it faster to stor
 
 ## Prerequisites
 
-- A [GCP](https://console.cloud.google.com/welcome) billing account and project.
+- A [GCP](https://console.cloud.google.com/welcome){: target="_blank"} billing account and project.
 - Optional: Install the [gcloud CLI](https://cloud.google.com/sdk/docs/install-sdk).
 
 ## One-time setup steps {#setup}
@@ -36,7 +36,7 @@ While you are testing, you can start with a single Google Cloud region; to be cl
 
 This stores the CSV and JSON files that you will upload whenever your data changes. It also stores generated files files in a `datacommons` subdirectory when you run the data management job.
 
-1. Go to [https://console.cloud.google.com/storage/browser](https://console.cloud.google.com/storage/browser) for your project.
+1. Go to [https://console.cloud.google.com/storage/browser](https://console.cloud.google.com/storage/browser){: target="_blank"} for your project.
 1. Next to **Buckets**, click **Create**.
 1. Enter a name for this bucket.
 1. For the **Location type**, choose the same regional options as for Cloud SQL above.
@@ -51,7 +51,7 @@ This stores the CSV and JSON files that you will upload whenever your data chang
 
 This stores the data that will be served at run time. The Data Commons data management job will create the SQL tables and populate them when you start the job.
 
-1. Go to https://console.cloud.google.com/sql/instances for your project.
+1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/sql/instances){: target="_blank"} for your project.
 1. Next to **Instances**, click **Create Instance**.
 1. Click **Choose MySQL.**
 1. If necessary, enable APIs as directed.
@@ -69,11 +69,11 @@ This stores the data that will be served at run time. The Data Commons data mana
 
 ### Step 4: Create a Google Cloud Run job
 
-Since you won't need to customize the data management container, you can simply run an instance of the released container provided by Data Commons team, at https://console.cloud.google.com.google.com/gcr/images/datcom-ci/global/datacommons-data.
+Since you won't need to customize the data management container, you can simply run an instance of the released container provided by Data Commons team, at [https://console.cloud.google.com/gcr/images/datcom-ci/global/datacommons-data](https://console.cloud.google.com/gcr/images/datcom-ci/global/datacommons-data){: target="_blank"}.
 
 See also the [Cloud Run](https://cloud.google.com/run/docs/create-jobs) documentation for links to more information on all the options you may set on your jobs.
 
-1. Go to https://console.cloud.google.com/run/ for your project.
+1. Go to [https://console.cloud.google.com/run/](https://console.cloud.google.com/run/){: target="_blank"} for your project.
 1. Click **Create job**.
 1. In the **Container image URL** field, click **Select** to open the **Select container image** window.
 1. Click the **Container Registry** tab.
@@ -116,7 +116,7 @@ Now set environment variables:
 
 ### Step 1: Upload data files to Google Cloud Storage
 
-1. Go to https://console.cloud.google.com/storage/browse and select your custom Data Commons bucket.
+1. Go to [https://console.cloud.google.com/storage/browse](https://console.cloud.google.com/storage/browse){: target="_blank"} and select your custom Data Commons bucket.
 1. Navigate to the folder you created in the earlier step.
 1. Click **Upload Files**, and select all your CSV files and `config.json`.
 
@@ -132,7 +132,7 @@ Every time you upload new input CSV or JSON files to Google Cloud Storage, you w
 
 To run the job:
 
-1. Go to https://console.cloud.google.com/run/jobs for your project.
+1. Go to [https://console.cloud.google.com/run/jobs](https://console.cloud.google.com/run/jobs){: target="_blank"} for your project.
 1. From the list of jobs, click the link of the "datacommons-data" job you created above.
 1. Click **Execute**. It will take several minutes for the job to run. You can click the **Logs** tab to view the progress. 
 
@@ -142,7 +142,7 @@ When it completes, to verify that the data has been loaded correctly, see the ne
 
 To view information about the created tables:
 
-1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/sql/instances) for your project and select the instance you created earlier.
+1. Go to [https://console.cloud.google.com/sql/instances](https://console.cloud.google.com/sql/instances){: target="_blank"} for your project and select the instance you created earlier.
 1. In the left panel, select **Cloud SQL Studio**.
 1. In the **Sign in to SQL Studio** page, from the Database field, select the database you created earlier, e.g. `datacommons`.
 1. Enter the user name and password and click **Authenticate**.

custom_dc/deploy_cloud.md

@@ -23,7 +23,7 @@ You push a locally built Docker image to the [Google Cloud Artifact Registry](ht
 
 ## One-time setup: Create a Google Artifact Registry repository
 
-1. Go to [https://console.cloud.google.com/artifacts](https://console.cloud.google.com/artifacts) for your project.
+1. Go to [https://console.cloud.google.com/artifacts](https://console.cloud.google.com/artifacts){: target="_blank"} for your project.
 1. Click **Create Repository**.
 1. Specify a name for the repository.
 1. In the **Format** option, select **Docker**.
@@ -72,7 +72,7 @@ This will take several minutes to upload.
 
 When it completes, verify that the container has been uploaded in the Cloud Console:
 
-1. Go to [https://console.cloud.google.com/artifacts](https://console.cloud.google.com/artifacts) for your project.
+1. Go to [https://console.cloud.google.com/artifacts](https://console.cloud.google.com/artifacts){: target="_blank"} for your project.
 1. In the list of repositories, click on the one you created above. Under **Repository Details**, you should see the Docker image listed.
 
 ## One-time setup: Create a Google Run service
@@ -81,7 +81,7 @@ This procedure shows you how to create a service and set environment variables u
 
 See also [Deploying to Cloud Run](https://cloud.google.com/run/docs/deploying) for more information on all the options you may set on your service.
 
-1. Go to https://console.cloud.google.com/run/ for your project.
+1. Go to [https://console.cloud.google.com/run/](https://console.cloud.google.com/run/){: target="_blank"} for your project.
 1. Click **Create service**.
 1. Keep the default **Deploy one revision from an existing container image** enabled.
 1. In the **Container image URL** field, click **Select** to open the **Select container image** window.
@@ -124,5 +124,5 @@ Click **Create** to kick off the deployment.  Click the **Logs** tab to see the
 
 Every time you make changes to the code and release a new Docker artifact, or rerun the [data management job](/custom_dc/data_cloud.html#run-job), you need to restart the service as well. To do so:
 
-1. Go to the https://console.cloud.google.com/run/ page, click on the service you created above, and click **Edit & Deploy Revision**. 
+1. Go to the [https://console.cloud.google.com/run/](https://console.cloud.google.com/run/){: target="_blank"} page, click on the service you created above, and click **Edit & Deploy Revision**. 
 1. Select a new container and click **Deploy**.

custom_dc/troubleshooting.md

@@ -80,7 +80,7 @@ In general, whenever you encounter problems with any Google Cloud Run service, c
 ### "403 Forbidden: Your client does not have permission to get URL / from this server"
 
 This error indicates that your application requires authenticated requests but you have not provided an authentication token. If your site is intended to be public, first check to see that the Cloud Run service is not set up to require authentication:
-1. Go to the [Google Cloud Console Cloud Run](https://console.cloud.google.com?run) page for your project.
+1. Go to the [Google Cloud Console Cloud Run](https://console.cloud.google.com?run){: target="_blank"} page for your project.
 1. From the list of services, select the relevant service and select the **Security** tab.
 1. Ensure that you have enabled **Allow unauthenticated invocations** and restart the Cloud Run service.