Deploy CloudFormation Template from AWS CLI
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Overview
You can deploy a stack using the aws cloudformation create-stack
or aws cloudformation deploy
commands from the AWS CLI. The example in this section uses the create-stack command. See AWS CLI Command Reference documentation for the syntax differences between the create-stack
and deploy
commands.
Before you start
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Install and configure AWS CLI. See Get started with the AWS CLI.
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Make sure you have:
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Required AWS credentials.
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Necessary IAM permissions to create and manage resources. If you do not have the necessary permissions, contact your organization administrator to create all the specified roles.
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Required parameter files and CloudFormation templates. You can download the files from the AI Unlimited GitHub repository.
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Create a stack
Run the following command on the AWS CLI:
aws cloudformation create-stack --stack-name all-in-one \
--template-body file://all-in-one.yaml \
--parameters file://test_parameters/all-in-one.json \
--tags Key=ThisIsAKey,Value=AndThisIsAValue \
--capabilities CAPABILITY_IAM CAPABILITY_NAMED_IAM
NOTE:
-
CAPABILITY_IAM is required if IamRole is set to New.
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CAPABILITY_NAMED_IAM is required if IamRole is set to New and IamRoleName is given a value.
To use an existing role, see Control AWS Access and Permissions using Permissions and Policies.
Delete a stack
Run the following command on the AWS CLI:
aws cloudformation delete-stack --stack-name <stackname>
Get stack information
Run the following command on the AWS CLI:
aws cloudformation delete-stack --stack-name <stackname>
aws cloudformation describe-stacks --stack-name <stackname>
aws cloudformation describe-stack-events --stack-name <stackname>
aws cloudformation describe-stack-instance --stack-name <stackname>
aws cloudformation describe-stack-resource --stack-name <stackname>
aws cloudformation describe-stack-resources --stack-name <stackname>
Get stack outputs
Run the following command on the AWS CLI:
aws cloudformation describe-stacks --stack-name <stackname> --query 'Stacks[0].Outputs' --output table
Next steps
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Get started with Teradata AI Unlimited by running a simple workflow. See Run a Sample Workload in JupyterLab Using Teradata AI Unlimited.
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Interested in learning how Teradata AI Unlimited can help you with real-life use cases? Coming soon! Keep watching this space for the GitHub link.
If you have any questions or need further assistance, please visit our community forum where you can get support and interact with other community members. |