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| # Generating AWS and GitHub access tokens | ||
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| On this page... |
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| # Docs for AWS-EKS-EC2-ASG-CDK Workflow Stack | ||
| ## AWS-EKS-EC2-ASG-CDK Workflow Stack Documentation | ||
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| ### Set Up your Account on CTO.ai | ||
| This repo contains a complete, functional PaaS workflow with integrated GitOps and ChatOps features. Designed for deployment to AWS infrastructure, this workflow supports EKS on EC2, Aurora, SQS, Redis, and Autoscaling—all orchestrated by CDK. | ||
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| Before you can deploy this Workflow, you need to [Setup your account on CTO.ai](https://cto.ai/docs/setup-flow) | ||
| ## Prerequisites | ||
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| ### Create Secrets from Settings | ||
| ### Add environment variables to Secrets Store | ||
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| Secrets are encrypted environment variables that CTO.ai utilizes within your workflow to build and run your application and deployments. [Follow this guide to create secrets from settings.](https://cto.ai/docs/aws-eks-ec2#create-secret-from-settings), and also [generate your Github token](https://cto.ai/docs/aws-eks-ec2#generate-github-token) | ||
| > Resource: [Using Secrets and Configs via Dashboard](https://cto.ai/docs/secrets-and-configs-dashboard) | ||
| ### View Auto Scaling groups | ||
| Create new environment variables via your Secrets Store on the CTO.ai Dashboard, one for each of the following keys: | ||
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| Schedule the scaling [actions that proactively increases and decreases capacity to match the load forecast](https://cto.ai/docs/aws-eks-ec2#view-auto-scaling-groups) | ||
| - `AWS_ACCESS_KEY_ID` | ||
| - `AWS_SECRET_ACCESS_KEY` | ||
| - `AWS_ACCOUNT_NUMBER` | ||
| - `GITHUB_TOKEN` | ||
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| Follow the following steps below to configure and deploy your AWS EC2-Elastic Kubernetes Service Workflow | ||
| This wiki has more information on [generating your AWS and GitHub credentials](https://github.com/workflows-sh/aws-eks-ec2-asg-cdk/wiki/Generating-access-tokens). | ||
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| ### Build and Run Pipelines | ||
| ### Install the repo locally | ||
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| - [Build Pipelines locally with the Workflow CLI](https://cto.ai/docs/aws-eks-ec2#build-pipelines-locally-with-the-ctoai-cli) | ||
| Clone the GitHub repository for this workflow stack locally, then change in to the directory: | ||
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| - [Run Pipelines locally with the Workflow CLI](https://cto.ai/docs/aws-eks-ec2#run-pipelines-locally-with-the-ctoai-cli) | ||
| ```bash | ||
| git clone https://github.com/workflows-sh/aws-eks-ec2-asg-cdk.git | ||
| cd aws-eks-ec2-asg-cdk | ||
| ``` | ||
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| ### Run and Setup your Infrastructure | ||
| ## Usage | ||
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| In your AWS EKS EC2 workflow, [build and set up your infrastructure using the `ops run -b .` command. This will provision your AWS-EKS EC2 stacks using Cloud Formation](https://cto.ai/docs/aws-eks-ec2#run-and-setup-your-infrastructure) | ||
| ### Build Pipelines locally with CTO.ai CLI | ||
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| ### View changes on CloudFormation | ||
| Run the following CLI command to build the repo's Pipelines workflow locally: | ||
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| - [Get a detailed overview of your entire Stack on CloudFormation](https://cto.ai/docs/aws-eks-ec2#view-changes-on-aws-cloudformation) | ||
| ```bash | ||
| ops build . | ||
| ``` | ||
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| ### View EC2 instances on AWS | ||
| Then, when prompted, select the `sample-app-pipeline` workflow from the list. This will build the workflow locally (for use or sharing) and build the workflow's Docker image from its `Dockerfile`. | ||
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| Get insights to your EC2 instances on AWS. [View EC2 instances on AWS](https://cto.ai/docs/aws-eks-ec2#view-ec2-instances-on-aws) | ||
| The resulting Docker image will be tagged with the name and version specified in the workflow's `ops.yml` file. | ||
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| ### Run Pipelines locally with CTO.ai CLI | ||
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| ### View Elastic Kubernetes Service Cluster | ||
| Run the following CLI command to run the repo's Pipelines workflow locally: | ||
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| See your [EKS Cluster resources, deployments, and authentication](https://cto.ai/docs/aws-eks-ec2#view-elastic-kubernetes-service-cluster) | ||
| ```bash | ||
| ops run . | ||
| ``` | ||
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| Then, when prompted, select the `sample-app-pipeline` workflow from the list. This will run the workflow locally, using the Docker image built in the previous step. |