1. I want a vpc with two public and two private subnets in us-west-2 with redundancy in two availability zones. Then add two web servers in the public subnets and two api servers in the private subnets and a postgres rds instance with access to the private subnets

    Python

    Below is a Pulumi Python program that creates a VPC with two public and two private subnets spread across two availability zones in the us-west-2 region. It includes deploying two web servers in the public subnets and two API servers in the private subnets. Additionally, a PostgreSQL RDS instance is created with access limited to the private subnets.

    import pulumi import pulumi_aws as aws from pulumi_aws import ec2 # Create a VPC vpc = ec2.Vpc("custom-vpc", cidr_block="10.0.0.0/16", enable_dns_hostnames=True, enable_dns_support=True, tags={"Name": "custom-vpc"} ) # Create Internet Gateway for the VPC igw = ec2.InternetGateway("vpc-igw", vpc_id=vpc.id) # Create subnets across two availability zones for redundancy availability_zones = aws.get_availability_zones(state="available", filters=[{"name": "opt-in-status", "values": ["opt-in-not-required"]}]) subnet_pub_1 = ec2.Subnet("subnet-pub-1", vpc_id=vpc.id, cidr_block="10.0.1.0/24", availability_zone=availability_zones.names[0], map_public_ip_on_launch=True, tags={"Name": "public-subnet-az1"} ) subnet_pub_2 = ec2.Subnet("subnet-pub-2", vpc_id=vpc.id, cidr_block="10.0.2.0/24", availability_zone=availability_zones.names[1], map_public_ip_on_launch=True, tags={"Name": "public-subnet-az2"} ) subnet_priv_1 = ec2.Subnet("subnet-priv-1", vpc_id=vpc.id, cidr_block="10.0.3.0/24", availability_zone=availability_zones.names[0], tags={"Name": "private-subnet-az1"} ) subnet_priv_2 = ec2.Subnet("subnet-priv-2", vpc_id=vpc.id, cidr_block="10.0.4.0/24", availability_zone=availability_zones.names[1], tags={"Name": "private-subnet-az2"} ) # Create a route table for public subnets to allow internet access route_table_public = ec2.RouteTable("route-table-public", vpc_id=vpc.id, routes=[ec2.RouteTableRouteArgs( cidr_block="0.0.0.0/0", gateway_id=igw.id )], tags={"Name": "public-route-table"} ) # Associate the public route table with the public subnets ec2.RouteTableAssociation("route-table-association-pub-1", subnet_id=subnet_pub_1.id, route_table_id=route_table_public.id) ec2.RouteTableAssociation("route-table-association-pub-2", subnet_id=subnet_pub_2.id, route_table_id=route_table_public.id) # Create a NAT Gateway for private subnet instances to access the internet elastic_ip = ec2.Eip("nat-gateway-eip", vpc=True) nat_gateway = ec2.NatGateway("nat-gateway", allocation_id=elastic_ip.id, subnet_id=subnet_pub_1.id, tags={"Name": "nat-gateway"} ) # Create a route table for private subnets with a default route through the NAT gateway route_table_private = ec2.RouteTable("route-table-private", vpc_id=vpc.id, routes=[ec2.RouteTableRouteArgs( cidr_block="0.0.0.0/0", nat_gateway_id=nat_gateway.id )], tags={"Name": "private-route-table"} ) # Associate the private route table with the private subnets ec2.RouteTableAssociation("route-table-association-priv-1", subnet_id=subnet_priv_1.id, route_table_id=route_table_private.id) ec2.RouteTableAssociation("route-table-association-priv-2", subnet_id=subnet_priv_2.id, route_table_id=route_table_private.id) # Security groups for web servers and API servers sg_web = ec2.SecurityGroup("sg-web", vpc_id=vpc.id, description="Allow web traffic", ingress=[ec2.SecurityGroupIngressArgs( protocol="tcp", from_port=80, to_port=80, cidr_blocks=["0.0.0.0/0"] )] ) sg_api = ec2.SecurityGroup("sg-api", vpc_id=vpc.id, description="Allow API traffic", ingress=[ec2.SecurityGroupIngressArgs( protocol="tcp", from_port=8080, to_port=8080, cidr_blocks=["10.0.3.0/24", "10.0.4.0/24"] )] ) # Launch two web server instances in public subnets for i in range(2): ec2.Instance(f"web-server-{i+1}", instance_type="t2.micro", vpc_security_group_ids=[sg_web.id], ami="ami-0c55b159cbfafe1f0", # Update to an AMI ID for your region subnet_id=subnet_pub_1.id if i % 2 == 0 else subnet_pub_2.id, tags={"Name": f"web-server-{i+1}"} ) # Launch two API server instances in private subnets for i in range(2): ec2.Instance(f"api-server-{i+1}", instance_type="t2.micro", vpc_security_group_ids=[sg_api.id], ami="ami-0c55b159cbfafe1f0", # Update to an AMI ID for your region subnet_id=subnet_priv_1.id if i % 2 == 0 else subnet_priv_2.id, tags={"Name": f"api-server-{i+1}"} ) # RDS DB Instance (PostgreSQL) in the private subnets rds_subnet_group = aws.rds.SubnetGroup("db-subnet-group", subnet_ids=[subnet_priv_1.id, subnet_priv_2.id]) db_instance = aws.rds.Instance("db-instance", allocated_storage=20, instance_class="db.t2.micro", engine="postgres", engine_version="12", db_subnet_group_name=rds_subnet_group.name, vpc_security_group_ids=[sg_api.id], username="postgres", password="changeMe1234!", # Use a secure method to pass the password skip_final_snapshot=True, tags={"Name": "db-instance"} ) # Export the VPC ID and database address pulumi.export('vpc_id', vpc.id) pulumi.export('db_instance_address', db_instance.address)

    Before running this code, ensure you replace the AMI ID with the appropriate one for your region and instance type. Also, you should handle the database password securely, perhaps by using the Pulumi config system or another secret management service.


  2. add https access with cloudfront

    Python

    To add HTTPS access with CloudFront for a web application, you will need to create a CloudFront distribution that points to your application, and configure it to serve content over HTTPS. You can choose to use the default CloudFront SSL certificate or provision a custom SSL certificate through ACM (AWS Certificate Manager) for a custom domain.

    Below is a Pulumi program that sets up a CloudFront distribution with HTTPS access. This program assumes that you have an S3 bucket that serves your web application content, and it configures a CloudFront distribution to act as a CDN with an SSL-enabled custom domain.

    import pulumi import pulumi_aws as aws # Specify your S3 bucket's ARN and name here web_bucket_arn = 'arn:aws:s3:::my-bucket' web_bucket_name = 'my-bucket' # Create an Origin Access Identity for your CloudFront distribution oai = aws.cloudfront.OriginAccessIdentity("oai") # Attach a policy to your S3 bucket that allows CloudFront to access it bucket_policy = aws.s3.BucketPolicy("bucketPolicy", bucket=web_bucket_name, policy=pulumi.Output.all(oai.cloudfront_access_identity_path, web_bucket_arn).apply(lambda args: f"""{{ "Version": "2012-10-17", "Statement": [{{ "Effect": "Allow", "Principal": {{ "AWS": "arn:aws:iam::cloudfront:user/CloudFront Origin Access Identity {args[0]}" }}, "Action": "s3:GetObject", "Resource": "{args[1]}/*" }}] }}""" ) ) # Create a CloudFront distribution for the S3 bucket distribution = aws.cloudfront.Distribution("distribution", origins=[aws.cloudfront.DistributionOriginArgs( domain_name=web_bucket_name + ".s3.amazonaws.com", origin_id=web_bucket_name, s3_origin_config=aws.cloudfront.DistributionOriginS3OriginConfigArgs( origin_access_identity=oai.cloudfront_access_identity_path ), )], enabled=True, default_root_object="index.html", default_cache_behavior=aws.cloudfront.DistributionDefaultCacheBehaviorArgs( allowed_methods=["GET", "HEAD"], cached_methods=["GET", "HEAD"], target_origin_id=web_bucket_name, forwarded_values=aws.cloudfront.DistributionDefaultCacheBehaviorForwardedValuesArgs( query_string=False, cookies=aws.cloudfront.DistributionDefaultCacheBehaviorForwardedValuesCookiesArgs( forward="none", ), ), viewer_protocol_policy="redirect-to-https", # Redirect HTTP requests to HTTPS min_ttl=0, default_ttl=3600, max_ttl=86400, ), price_class="PriceClass_100", custom_error_responses=[ aws.cloudfront.DistributionCustomErrorResponseArgs( error_code=404, response_page_path="/index.html", response_code=200, error_caching_min_ttl=300, ), ], restrictions=aws.cloudfront.DistributionRestrictionsArgs( geo_restriction=aws.cloudfront.DistributionRestrictionsGeoRestrictionArgs( restriction_type="none", ), ), viewer_certificate=aws.cloudfront.DistributionViewerCertificateArgs( cloudfront_default_certificate=True, # Use CloudFront's default certificate # Alternatively, use a custom ACM certificate: # acm_certificate_arn="arn:aws:acm:REGION:ACCOUNT-ID:certificate/CERTIFICATE-ID", # ssl_support_method="sni-only", ), ) # The CloudFront distribution's URL pulumi.export('distribution_url', distribution.domain_name)

    This program will create a CloudFront distribution that serves content over HTTPS using CloudFront's default SSL certificate. Replace web_bucket_arn, web_bucket_name, and any other placeholders with your actual resource values. If you want to use a custom domain name and SSL certificate, you would need to specify the acm_certificate_arn and the appropriate ssl_support_method within the viewer_certificate argument.


  3. add an application load balancer with https access to the web servers

    Python

    To add an Application Load Balancer (ALB) with HTTPS access to web servers using AWS, you can create an ALB, a listener for HTTPS, and a default target group. The application load balancer will distribute incoming application traffic across multiple targets, such as Amazon EC2 instances. For HTTPS access, you will need an SSL certificate, which can be specified in the HTTPS listener. Here's a Pulumi program that demonstrates how to set up an ALB with an HTTPS listener:

    import pulumi import pulumi_aws as aws # Create a VPC to host our ALB (if you don't have an existing one) vpc = aws.ec2.Vpc("app-vpc", cidr_block="10.100.0.0/16") # Create subnets for the VPC (adjust as necessary) subnet_a = aws.ec2.Subnet("app-subnet-a", vpc_id=vpc.id, cidr_block="10.100.1.0/24", availability_zone="us-west-2a") subnet_b = aws.ec2.Subnet("app-subnet-b", vpc_id=vpc.id, cidr_block="10.100.2.0/24", availability_zone="us-west-2b") # Create an Application Load Balancer alb = aws.lb.LoadBalancer("app-lb", subnets=[subnet_a.id, subnet_b.id], security_groups=[], internal=False, load_balancer_type="application") # Create a target group target_group = aws.lb.TargetGroup("app-tg", vpc_id=vpc.id, port=80, protocol="HTTP", target_type="ip") # Create an HTTPS listener for the Application Load Balancer. # This requires an SSL certificate from AWS Certificate Manager (ACM) https_listener = aws.lb.Listener("https-listener", load_balancer_arn=alb.arn, port=443, protocol="HTTPS", ssl_policy="ELBSecurityPolicy-2016-08", certificate_arn="arn:aws:acm:us-west-2:123456789012:certificate/your-cert-id", # Replace with your cert ID default_actions=[{ "type": "forward", "target_group_arn": target_group.arn }]) # Export the DNS name of the load balancer pulumi.export('alb_dns', alb.dns_name)

    Make sure to provide your ACM SSL certificate ARN for the certificate_arn parameter in the https_listener resource. If you need a new certificate, you can request one through AWS Certificate Manager.


  4. auto scale the web servers and api servers so it will scale up when the cpu load is over 80% and scale down when the cpu load is below 50%

    Python

    To auto-scale your web servers and API servers based on CPU load, you can use AWS Auto Scaling Groups with an attached scaling policy. Below is a Pulumi program in Python that sets up an Auto Scaling Group with a TargetTrackingScaling policy to handle the scaling based on the specified CPU load.

    The Auto Scaling Group will adjust the number of EC2 instances automatically - scaling up when the average CPU utilization exceeds 80% and down when it falls below 50%.

    import pulumi import pulumi_aws as aws # Specify the desired capacity and min/max capacity for your Auto Scaling Group desired_capacity = 2 min_size = 1 max_size = 5 # Define a launch configuration for your EC2 instances that will be part of the Auto Scaling Group launch_config = aws.ec2.LaunchConfiguration("web-api-launch-configuration", image_id="ami-0c55b159cbfafe1f0", # Replace with the AMI ID of your choice instance_type="t2.medium", # Choose the instance type that fits your needs ) # Create an Auto Scaling Group using the launch configuration autoscaling_group = aws.autoscaling.Group("web-api-autoscaling-group", launch_configuration=launch_config.id, min_size=min_size, max_size=max_size, desired_capacity=desired_capacity, vpc_zone_identifiers=[subnet.id for subnet in [subnet_a, subnet_b]], # Add your subnet IDs tag={ "key": "Name", "value": "web-api-server", "propagate_at_launch": True, } ) # Define a Target Tracking Scaling policy for the Auto Scaling Group scaling_policy = aws.autoscaling.Policy("cpu-utilization-target-tracking", autoscaling_group_name=autoscaling_group.name, policy_type="TargetTrackingScaling", target_tracking_configuration={ "predefinedMetricSpecification": { "predefinedMetricType": "ASGAverageCPUUtilization", }, "targetValue": 80.0, } ) # Another policy for scaling down when the CPU utilization falls below 50% down_scaling_policy = aws.autoscaling.Policy("cpu-utilization-target-tracking-down", autoscaling_group_name=autoscaling_group.name, policy_type="TargetTrackingScaling", target_tracking_configuration={ "predefinedMetricSpecification": { "predefinedMetricType": "ASGAverageCPUUtilization", }, "targetValue": 50.0, "disableScaleIn": False, # Enable scale-in } ) # Export the Auto Scaling Group name pulumi.export('autoscaling_group_name', autoscaling_group.name)

    Replace the image_id, instance_type, and vpc_zone_identifiers with appropriate values for your setup. The tag in the Auto Scaling Group is optional, but it's a good practice to tag your resources for better management.

    This script sets up a scaling policy that increases the number of instances in the Auto Scaling Group when the average CPU utilization climbs above 80%. A similar downscaling policy is also set up to decrease the number of instances when the CPU usage drops below 50%.

    Make sure your instances are set to send CPU utilization metrics to CloudWatch, which is where the Auto Scaling Group retrieves the metrics to make scaling decisions.

    You can adjust the desired_capacity, min_size, and max_size variables according to how many instances you want at the start and the minimum and maximum number of instances the Auto Scaling Group should maintain.


  5. add elaticache service accessible to the web and api servers

    Python

    To add an Amazon ElastiCache service that is accessible to both the web and API servers in your environment, you should define an ElastiCache Cluster or a ServerlessCache, depending on your requirements. Below is the Pulumi program to set up an ElastiCache Cluster which can be accessed by the specified web and API servers:

    1. Set up a security group for the ElastiCache cluster that allows inbound access on the appropriate port (normally 6379 for Redis, or 11211 for Memcached) from the security groups associated with the web and API servers.
    2. Create an ElastiCache subnet group with the subnets your web and API servers are in.
    3. Create the ElastiCache cluster within this subnet group and with the created security group.

    Here is the Pulumi program in Python to accomplish this:

    import pulumi import pulumi_aws as aws # Assume that web_sg_id and api_sg_id are the IDs of the existing security groups # for your web and API servers respectively # ElastiCache Cluster Security Group that allows access from Web and API servers elasticache_sg = aws.ec2.SecurityGroup('elasticache-sg', description='ElastiCache security group', ingress=[ { 'protocol': 'tcp', 'from_port': 6379, # ElastiCache Redis default port 'to_port': 6379, 'security_groups': [web_sg_id, api_sg_id], }, ], ) # ElastiCache Subnet Group using the subnets the instances are deployed in elasticache_subnet_group = aws.elasticache.SubnetGroup('elasticache-subnet-group', description='ElastiCache subnet group', subnet_ids=['subnet-xxxxxxxxxx', 'subnet-yyyyyyyyyy'], # Replace with actual subnet IDs ) # ElastiCache Redis Cluster redis_cluster = aws.elasticache.Cluster('redis-cluster', engine='redis', node_type='cache.m4.large', num_cache_nodes=1, parameter_group_name='default.redis5.0', snapshot_retention_limit=5, snapshot_window='04:00-05:00', security_group_ids=[ elasticache_sg.id, ], subnet_group_name=elasticache_subnet_group.name, ) # Export the ElastiCache cluster endpoint pulumi.export('redis_endpoint', redis_cluster.cache_nodes[0]['address'])

    Remember to replace 'subnet-xxxxxxxxxx', 'subnet-yyyyyyyyyy' with the actual subnet IDs where your web and API servers reside, as well as the web_sg_id and api_sg_id with the actual security group IDs for your server instances.


  6. add framework to allow the pulimi to run in dev stg and prd environments

    Python

    To run a Pulumi program in multiple environments such as development (dev), staging (stg), and production (prd), you can utilize Pulumi stacks. Each stack can represent an environment and you can set stack-specific configurations for each one.

    Here is a Pulumi program that outlines how to create resources with environment-specific configurations:

    import pulumi # Assuming we have a custom resource, for example, an S3 Bucket. # The following program sets up an S3 bucket with environment-specific settings. class MyBucket(pulumi.ComponentResource): def __init__(self, name: str, tags: dict, opts: pulumi.ResourceOptions=None): super().__init__('my:module:MyBucket', name, None, opts) # Resource creation logic using environment-specific tags. self.bucket = aws.s3.Bucket(name, tags=tags, opts=pulumi.ResourceOptions(parent=self)) self.register_outputs({ 'bucket_name': self.bucket.id }) # You can switch between stacks using the Pulumi CLI commands: # pulumi stack select dev # pulumi stack select stg # pulumi stack select prd # The following code gets the current stack (environment) # and applies specific configurations based on the stack name. stack = pulumi.get_stack() # Define the tags based on the environment. tags = { 'dev': {'Environment': 'Development'}, 'stg': {'Environment': 'Staging'}, 'prd': {'Environment': 'Production'} } # Create an S3 bucket with the given tags for the current environment. bucket = MyBucket(f'my-bucket-{stack}', tags.get(stack, {})) # Export the name of the bucket pulumi.export('bucket_name', bucket.bucket_name)

    To manage these environments, you would run pulumi up within the context of the stack you want to apply. You would typically have separate stack configuration files for each environment like Pulumi.dev.yaml, Pulumi.stg.yaml, and Pulumi.prd.yaml. Each of these files would have specific values for their environment which your program could read using pulumi.Config.

    When you want to move code from dev to staging or to production, you would use pulumi stack select stg or pulumi stack select prd respectively and then run pulumi up to apply the changes in those environments.


  7. add a custom userdata script to the launch configuration of the web and api servers

    Python

    To add a custom user data script to the launch configuration for both web and API servers, you can use the aws.ec2.LaunchTemplate resource. This resource allows you to specify the user data as a string that can be run on instance initialization. Below is a program that creates two launch templates, one for web servers and one for API servers, with a custom user data script for each.

    import pulumi import pulumi_aws as aws # Custom user data script for web servers web_user_data = """#!/bin/bash echo "Web server setup" # Your setup commands here """ # Custom user data script for API servers api_user_data = """#!/bin/bash echo "API server setup" # Your setup commands here """ # Create a launch template for web servers web_launch_template = aws.ec2.LaunchTemplate("webLaunchTemplate", name_prefix="web-server-", user_data=web_user_data, # Base64 encoding is automatically handled by Pulumi image_id="ami-0abcdef1234567890", # Replace with your AMI ID instance_type="t2.micro", # Replace with your instance type key_name="my-key-pair", # Replace with your key pair name ) # Create a launch template for API servers api_launch_template = aws.ec2.LaunchTemplate("apiLaunchTemplate", name_prefix="api-server-", user_data=api_user_data, # Base64 encoding is automatically handled by Pulumi image_id="ami-0987654321fedcba0", # Replace with your AMI ID instance_type="t2.small", # Replace with your instance type key_name="my-key-pair", # Replace with your key pair name ) # Export the launch template IDs pulumi.export("web_launch_template_id", web_launch_template.id) pulumi.export("api_launch_template_id", api_launch_template.id)

    Make sure to replace the ami-0abcdef1234567890 and ami-0987654321fedcba0 with the actual AMI IDs of the web and API server images you are using, as well as the instance_type and key_name with the appropriate values for your use case.

    The user_data for each launch template contains the shell script that initializes either the web or API server. These scripts will execute when an EC2 instance is launched from these templates.


  8. add codedeploy from a github git repository

    Python

    To set up AWS CodeDeploy to deploy from a GitHub repository, you need to complete a few main tasks:

    1. Create a GitHub repository and note down the GitHub token.
    2. Create an IAM Service Role for CodeDeploy.
    3. Create a CodeDeploy Application.
    4. Create a Deployment Group.
    5. Optionally, set up a CodeBuild project if you need to build the code before deploying.

    Below is a Pulumi program that demonstrates how to create an AWS CodeDeploy Application and Deployment Group that references a GitHub repository:

    import pulumi import pulumi_aws as aws # Note: You need to create a GitHub token beforehand and store it securely. # The GitHub token should have the necessary permissions to access the repository. github_token = 'your_github_token' repository_name = 'your_github_repository_name' repository_owner = 'your_github_username_or_organization' # Create an IAM service role for CodeDeploy. codedeploy_role = aws.iam.Role("CodeDeployServiceRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "codedeploy.amazonaws.com" }, "Action": "sts:AssumeRole" } ] }""") # Attach AWS managed policy for CodeDeploy to the role created above. aws.iam.RolePolicyAttachment("CodeDeployRolePolicyAttachment", role=codedeploy_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSCodeDeployRole") # Create a CodeDeploy application. codedeploy_app = aws.codedeploy.Application("MyCodeDeployApp", compute_platform="Server") # Set to 'Lambda' or 'ECS' depending on the compute platform # Create a deployment group with GitHub location. codedeploy_group = aws.codedeploy.DeploymentGroup("MyDeploymentGroup", app_name=codedeploy_app.name, service_role_arn=codedeploy_role.arn, deployment_config_name="CodeDeployDefault.OneAtATime", # Specify another configuration if needed deployment_group_name="my-deployment-group", auto_rollback_configuration={ "enabled": True, "events": ["DEPLOYMENT_FAILURE"], }, trigger_configurations=[{ "trigger_events": ["DeploymentFailure", "DeploymentSuccess"], "trigger_name": "MyDeploymentTrigger", "trigger_target_arn": "arn:aws:sns:us-west-2:123456789012:MySNSTopic", # Update with your SNS topic ARN }], # Assuming the repository is public or the token has access to private repo # For private repos, you need to use the GitHub Token to grant access github_location={ "repository": f"{repository_owner}/{repository_name}", "commit_id": "head/master", # Update to a specific commit if needed }, depends_on=[codedeploy_role]) # Output the Deployment Group ID pulumi.export('deployment_group_id', codedeploy_group.id)

    Make sure to replace your_github_token, your_github_repository_name, and your_github_username_or_organization with your actual GitHub access token, repository name, and username or organization. You must also have created the GitHub token with permissions to access repositories.

    Please note that handling sensitive information, such as your GitHub token, should be done through secure means such as secret management systems. The token is hardcoded here for demonstration purposes only.


  9. please get the github token from secrets manager for the previous example

    Python

    To retrieve the GitHub token from AWS Secrets Manager to use in the Pulumi program, you can use the aws.secretsmanager.Secret and aws.secretsmanager.SecretVersion resources to access the stored secret. Here is a program that fetches the GitHub token secret and uses it for the CodeDeploy setup:

    import pulumi import pulumi_aws as aws # Assuming you have stored the GitHub token in AWS Secrets Manager under a secret named 'github-token' github_secret = aws.secretsmanager.Secret("githubToken") # Retrieve the latest version of the secret github_secret_version = aws.secretsmanager.SecretVersion("githubTokenVersion", secret_id=github_secret.id) # Rest of the CodeDeploy setup remains the same, but uses the retrieved GitHub token repository_name = 'your_github_repository_name' repository_owner = 'your_github_username_or_organization' # ... (rest of the CodeDeploy setup such as IAM role and application) # Now create the CodeDeploy deployment group with the GitHub location using the secret codedeploy_group = aws.codedeploy.DeploymentGroup("MyDeploymentGroup", # ... (other configuration details) # Use the GitHub token from Secrets Manager for the GitHub location github_location={ "repository": f"{repository_owner}/{repository_name}", "commit_id": "head/master", # Update to a specific commit if needed }, # Pass the actual token string to the configuration # WARNING: Make sure not to expose secret values in plaintext in your configurations # or output them to console/logs. oauth_token=github_secret_version.secret_string, # ... (rest of the deployment group configuration) depends_on=[codedeploy_role]) # It's important to export the token for reference, but not the actual value of the token for security reasons. pulumi.export('github_secret_name', github_secret.name) # Do NOT export `github_secret_version.secret_string` as it is sensitive data

    Make sure to replace 'your_github_repository_name' and 'your_github_username_or_organization' with your actual repository name and owner. The github_secret_version.secret_string is the actual token that will be used in the GitHub location configuration. Be careful not to output or expose the secret token in plaintext to maintain security best practices.

    The depends_on parameter ensures that the deployment group is created only after the IAM role is available. The secret's name is exported, not the value, as it's sensitive information.


  10. create a gitlab cicd pipeline to build, test and deploy the project

    Python

    To create a GitLab CI/CD pipeline with Pulumi, you'll want to define a .gitlab-ci.yml file in your project's repository, which GitLab uses to manage the pipeline's stages and jobs. Here's a simple Pulumi Python program that illustrates how to create a GitLab project and then you would add a .gitlab-ci.yml within that project's repo to build, test, and deploy your application.

    import pulumi import pulumi_gitlab as gitlab # Create a new GitLab project project = gitlab.Project("my-project", description="Project to demonstrate GitLab CI/CD pipeline", visibility_level="public") # pulumi.export to output the project details, such as web URL, which you can visit pulumi.export('project_name', project.name) pulumi.export('project_web_url', project.web_url)

    The .gitlab-ci.yml file is not managed directly through Pulumi but is part of your source code. This file should be placed at the root of your repository and might look something like this:

    stages: - build - test - deploy variables: PULUMI_ACCESS_TOKEN: $PULUMI_ACCESS_TOKEN before_script: - echo "Starting CI/CD pipeline" build_job: stage: build script: - echo "Building the project" # Add commands here to build your project, e.g., compile code, build Docker images. test_job: stage: test script: - echo "Running tests" # Add commands here to test your project, e.g., run unit tests. deploy_job: stage: deploy script: - echo "Deploying the project" - pulumi up --yes only: - master

    Make sure to create the PULUMI_ACCESS_TOKEN as a GitLab CI/CD variable containing the Pulumi access token to authenticate the CLI in your GitLab pipeline.

    After committing pulumi_gitlab.py and .gitlab-ci.yml to your repository, GitLab will pick up the changes and start running your CI/CD pipeline based on the configurations made in the .gitlab-ci.yml.


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