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@@ -54,10 +54,44 @@ Then, log into the site using the credentials:
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> Alternatively, you can provide your own `--account-name`, `--account-email`, and `--account-pass`.
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## Updating configuration
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## Updating Drupal site configuration
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This site uses a full configuration export, and to update the site's configuration, you can run (from within the docroot):
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drush --uri=local.d8pix.com cex -y
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This should update the configuration as stored in `config/default`. Commit this new config, then test the configuration by reinstalling the site (to make sure the config works on a fresh install).
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## AWS setup
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To allow AWS Lambda to call back to your Drupal site (so faces and labels can be integrated with your media entities), you must have Drupal running on a publicly-accessible URL. Therefore before any of the AWS integration for Rekognition can be tested, make sure you're running an installation of this site on a server with a publicly-accessible URL.
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After that, make sure you create a Drupal user account with permission to create and update nodes and taxonomy terms, then store that account's credentials for use by the Rekognition AWS Lambda function.
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After you have a Drupal site installed, and the API user created, do the following to prepare your local workstation and AWS account for the Rekognition resources:
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1. Install AWS CLI.
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1. Generate a 'programmatic access' AWS access key for an AWS IAM User with admin rights.
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1. Store the access key ID and secret in a location suitable for use with the AWS CLI.
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1. Create an S3 bucket named `drupal-lambda`, and upload a .zip archive containing the file `web/modules/contrib/rekognition_api/lambda/index.js`, after renaming the .zip archive to `drupal-media-rekognition.zip` (so the full S3 path is `s3://drupal-lambda/drupal-media-rekognition.zip`)
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Now that you're workstation is ready, and the lambda code is in place, run the following command to deploy the required AWS resources via AWS CloudFormation:
If you don't see that message, take a look in the AWS Console in the CloudFormation section, or use the AWS CLI to view detailed logs of what caused any issues.
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