1. Elastic Beanstalk for Zero-Downtime AI Deployments

    Python

    Elastic Beanstalk is an AWS service that allows you to deploy and scale web applications and services developed with Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker on familiar servers such as Apache, Nginx, Passenger, and IIS. For deploying an AI model, a common approach is to wrap the model in a web service so that it can process HTTP requests. This service can then be deployed as an application in Elastic Beanstalk.

    When using Elastic Beanstalk for zero-downtime deployments, you would typically:

    1. Create an application version that points to an S3 bucket with your new code.
    2. Update the existing Elastic Beanstalk environment to use this new version.

    AWS Elastic Beanstalk supports blue/green or rolling deployments to help you update your web application with zero downtime. Applying these strategies involves a careful orchestration of environment updates, and in some cases, environment swapping.

    Below is the Pulumi program written in Python that shows how you can create an Elastic Beanstalk environment and application, which you could use for AI deployments. This example assumes you've already packaged your AI application and uploaded it to an S3 bucket.

    import pulumi import pulumi_aws as aws # Create a new Elastic Beanstalk application. app = aws.elasticbeanstalk.Application("aiApplication", description="AI Application") # Elastic Beanstalk Application Versions correspond to a specific version of your application's code and configuration. # This version points to an S3 bucket where the code lives. app_version = aws.elasticbeanstalk.ApplicationVersion("aiApplicationVersion", application=app.name, source_bundle=aws.elasticbeanstalk.ApplicationVersionSourceBundleArgs( s3_bucket="my-ai-application-bucket", s3_key="versions/example-ai-app-v1.zip", ), description="Version 1.0 of the AI application") # Define the Elastic Beanstalk solution stack (platform) you want to use. # The solution stack name can specify the operating system, platform, and platform version. solution_stack_name = "64bit Amazon Linux 2 v3.3.6 running Python 3.8" # Create a new Elastic Beanstalk Environment that runs the application version. env = aws.elasticbeanstalk.Environment("aiEnvironment", application=app.name, solution_stack_name=solution_stack_name, settings=[ aws.elasticbeanstalk.EnvironmentSettingArgs( namespace="aws:autoscaling:asg", name="MinSize", value="1", ), aws.elasticbeanstalk.EnvironmentSettingArgs( namespace="aws:autoscaling:asg", name="MaxSize", value="2", ), # ... additional options here ... ], version_label=app_version.label, tags={"Name": "AI Application Environment"}) # Export the DNS name of the Elastic Beanstalk Environment so you can access it. pulumi.export('elastic_beanstalk_env_url', env.endpoint_url)

    In the code above:

    • We create an Elastic Beanstalk application using aws.elasticbeanstalk.Application.
    • We create an application version that points to an S3 bucket containing your packaged AI application using aws.elasticbeanstalk.ApplicationVersion.
    • We specify the solution stack name to let AWS know which platform configuration we want to use for our environment.
    • We create an environment with the specified application version and other settings, like the min and max size of autoscaling groups using aws.elasticbeanstalk.Environment. These settings can be adjusted to handle the load as per your requirements.
    • Finally, we export the URL endpoint of the Elastic Beanstalk environment so you can interact with your deployed AI application.

    Make sure to replace "my-ai-application-bucket" and "versions/example-ai-app-v1.zip" with your actual S3 bucket name and application zip file path. This example also assumes you have extended the Elastic Beanstalk environment with additional configuration details necessary for your AI application.

    To ensure zero-downtime deployments, you can set up and use such configurations like Health check paths, Scaling policies, Rolling updates, and more within the settings of your Elastic Beanstalk environment.

    Please adapt the solution_stack_name and other configurations as needed for your application specifics. For more details about Elastic Beanstalk resources in Pulumi, you can visit the Elastic Beanstalk Application, Elastic Beanstalk Application Version, and Elastic Beanstalk Environment docs.


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