Managed AI Workload Rollouts Using Elastic Beanstalk

Question

Pulumi · Accepted Answer

To manage AI workload rollouts using AWS Elastic Beanstalk, we can orchestrate the necessary AWS resources using Pulumi. AWS Elastic Beanstalk simplifies the process of deploying and scaling web applications and services by automating the details of infrastructure management, such as provisioning servers, setting up load balancing, and scaling.

To accomplish this task, we will use the following AWS Pulumi resources:
- `aws.elasticbeanstalk.Application`: Represents your Elastic Beanstalk application (A collection of elements that you run as a solution, such as a web application).
- `aws.elasticbeanstalk.Environment`: Defines the environment in which your application will be deployed (e.g., development, staging, production).

Here's how you can use Pulumi to deploy an AI application using Elastic Beanstalk:

1. **Define an Elastic Beanstalk Application**: This is the logical representation of your application in AWS. This doesn't include any actual deployment.
2. **Create an Application Version**: This specifies a particular version of the code or build that you want to deploy.
3. **Provision an Environment for the Application**: This is where your application's code runs. It provisions the necessary AWS resources like EC2 instances, databases, etc., and configures them according to the specified environment options.

Below is a program written in Python that uses Pulumi to create a managed AI workload on Elastic Beanstalk:

```python
import pulumi
import pulumi_aws as aws

# Create an Elastic Beanstalk application.
app = aws.elasticbeanstalk.Application("my-ai-app",
    description="My AI application")

# Define the application version based on a ZIP file in S3.
app_version = aws.elasticbeanstalk.ApplicationVersion("v1",
    application=app.name,
    source_bundle={
        "bucket": "my-app-bucket",
        "key": "v1.zip",
    })

# Provision an environment to run the application version.
# This will create all the necessary resources, such as EC2 instances,
# to run your application.
env = aws.elasticbeanstalk.Environment("env",
    application=app.name,
    solution_stack_name="64bit Amazon Linux 2 v3.3.6 running Python 3.8",
    settings=[
        {
            "namespace": "aws:elasticbeanstalk:application:environment",
            "name": "ENV_TYPE",
            "value": "production",  # Indicate the environment type (e.g., development, staging, production).
        },
        # You can add more environment configuration options here as needed.
    ],
    version_label=app_version.id)

# Export the DNS name for the Elastic Beanstalk environment to access your application.
pulumi.export("app_url", env.cname)
```

This Pulumi program sets up an Elastic Beanstalk environment for an AI application. The application code is assumed to come from an S3 bucket (`my-app-bucket`) where the specific version `v1.zip` is stored.

Here's a brief walkthrough of what each section does:

- The `aws.elasticbeanstalk.Application` resource defines a new Elastic Beanstalk application named "my-ai-app".
- `aws.elasticbeanstalk.ApplicationVersion` uploads the source bundle from an S3 bucket to create a new application version. This version points to a `.zip` file containing the AI application's code and dependencies.
- `aws.elasticbeanstalk.Environment` launches the application version on an AWS infrastructure defined by the specified 'solution stack name', which represents a configuration for a language running on an Amazon Linux 2 (`Python 3.8` in this example).
- Environment settings are configured to specify the environment type through custom environment variables (e.g., `ENV_TYPE` set to `production`).
- Finally, we export the URL of the AI application using `pulumi.export`, which allows us to access the deployed application's environment URL outside of Pulumi.

When you update your AI application, you can create new versions using `aws.elasticbeanstalk.ApplicationVersion` and update the `version_label` in `aws.elasticbeanstalk.Environment` to deploy it.

Please note that for your AI application to successfully deploy and run, you will need to ensure that your ZIP file contains all the necessary code and a valid Elastic Beanstalk configuration file (typically named `elasticbeanstalk.config`). Additionally, the S3 bucket and the ZIP file must be accessible by your AWS account and Elastic Beanstalk environment.