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multi-cloud-ai-workflow

README.md

multi-cloud-ai-workflow

This example workflow demonstrates how you can leverage AI technologies from multiple cloud vendors in a single media workflow

Requirements for running the example

  • Node.js 12 installed (version 12.19.0 or higher) and accessible in PATH. Recommended is to use a node version manager, which allows you to quickly switch between node versions (see more info at nvm-windows for windows support or nvm for Mac OS and Linux support)
  • Terraform 0.13 installed (version 0.13.2 or higher) and available in PATH. See the Terraform website
  • Java JRE or JDK 1.8 or higher to run Gradle build and deploy scripts
  • AWS account
  • Azure video indexer account, a free account can be used for testing. Follow these instructions: https://docs.microsoft.com/en-us/azure/cognitive-services/video-indexer/video-indexer-use-apis

Setup procedure

  1. Clone this repository to your local harddrive
  2. Navigate to the multi-cloud-ai-workflow folder.
  3. Create file named gradle.properties
  4. Add the following information to the created file and update the parameter values reflecting your AWS account and Azure account
# Mandatory settings

environmentName=com.your-domain.mcma
environmentType=dev

awsAccountId=<YOUR_AWS_ACCOUNT_ID>
awsAccessKey=<YOUR_AWS_ACCESS_KEY>
awsSecretKey=<YOUR_AWS_SECRET_KEY>
awsRegion=<YOUR_AWS_REGION>

# Optional settings, though without configuration some features may not work

AzureLocation=<YOUR AZURE REGION - USE "trial" FOR TESTING>
AzureAccountID=<YOUR AZURE Video Indexer Account ID> 
AzureSubscriptionKey=<YOUR AZURE SUBSCRIPTION KEY>
AzureApiUrl=<AZURE VIDEO API END[POINT DEFAULT IS: https://api.videoindexer.ai>
  1. Save the file.
  2. Open command line in multi-cloud-ai-workflow folder.
  3. Execute gradlew deploy and let it run. This can take a few minutes.
  4. If no errors have occurred until now you have successfully setup the infrastructure in your AWS cloud. Go to https://aws.amazon.com/console/ and sign in to see your cloud infrastructure. In case you do have errors it may be that your environmentName is either too long or not unique enough to guarantee unique names for your cloud resources e.g. bucket names.