Building Custom AI Inference Server Images with AWS Image Builder

Question

Pulumi · Accepted Answer

AWS Image Builder is a service that enables you to automate the creation of virtual machine or container images. When building custom AI Inference Server images, you typically start with a base OS image and then customize it by installing your AI models, inference code, and any additional dependencies needed.

To create custom AI Inference Server images using AWS Image Builder, you would generally follow these steps:

1. Define a **Component** that specifies the steps to install and configure your AI inference software.
2. Create an **Image Recipe** that references the base image (e.g., Amazon Linux 2) and the component(s) to install on top of the base image.
3. Define an **Infrastructure Configuration** that specifies the AWS infrastructure to use for building the image (like instance type, key pair, networking info).
4. Optionally, define a **Distribution Configuration** if you want to distribute the built image to multiple AWS regions or accounts.
5. Create an **Image Pipeline** (if you want to rebuild the image regularly with updated components).

I'll provide you with a Pulumi program in Python that sets up an Image Builder pipeline to create a custom AI Inference Server image. The code will include:

- A **Component** to install the necessary software for the AI inference server.
- An **Image Recipe** specifying the base image and components.
- An **Infrastructure Configuration** to define the infrastructure requirements.
- A **Distribution Configuration** to distribute the image.
- An **Image Pipeline** to automate the creation and management of the images.

Let's begin with the code:

```python
import pulumi
import pulumi_aws as aws

# Define a component for the AI inference software installation.
ai_inference_component = aws.imagebuilder.Component("aiInferenceComponent",
    name="ai-inference-component", 
    platform="Linux",
    version="1.0.0",
    data="""{
        "schemaVersion": "1.0",
        "phases": [{
            "name": "build",
            "steps": [{
                "name": "InstallPython3",
                "action": "ExecuteBash",
                "inputs": {
                    "commands": [
                        "sudo yum install -y python3"
                    ]
                }
            },
            {
                "name": "AddInferenceCode",
                "action": "ExecuteBash",
                "inputs": {
                    "commands": [
                        # Replace the following URL with the location of your AI inference server code.
                        "curl -o inference_server.py https://example.com/inference_server.py",
                        "python3 -m pip install -r requirements.txt"
                    ]
                }
            }]
        }]
    }"""
)

# Create an Image Recipe for the AI inference server.
ai_image_recipe = aws.imagebuilder.ImageRecipe("aiImageRecipe",
    name="ai-inference-image-recipe",
    version="1.0.0",
    parent_image="arn:aws:imagebuilder:us-east-1:aws:image/amazon-linux-2-x86/...",
    components=[{
        "componentArn": ai_inference_component.arn
    }]
)

# Define the infrastructure configuration for building the image.
infrastructure_configuration = aws.imagebuilder.InfrastructureConfiguration("aiInfraConfig",
    name="ai-inference-infra-config",
    instance_types=["t2.micro"],  # Choose an appropriate instance type
    instance_profile_name="EC2InstanceProfileWithImageBuilderPermissions"
)

# Define a distribution configuration if you want to distribute the image.
distribution_configuration = aws.imagebuilder.DistributionConfiguration("aiDistConfig",
    name="ai-inference-dist-config",
    distributions=[{
        "region": "us-east-1",
        "ami_distribution_configuration": {
            "name": "ai_inference_image_{{ imagebuilder:buildDate }}",
            # Add additional distribution settings as needed
        },
    }]
)

# Finally, create an Image Pipeline.
ai_image_pipeline = aws.imagebuilder.ImagePipeline("aiImagePipeline",
    name="ai-inference-image-pipeline",
    image_recipe_arn=ai_image_recipe.arn,
    infrastructure_configuration_arn=infrastructure_configuration.arn,
    distribution_configuration_arn=distribution_configuration.arn,
    image_tests_configuration={
        "image_tests_enabled": False,  # Set to True if you want to run tests on the image
    }
)
```

This is a basic setup for using Pulumi to orchestrate AWS Image Builder resources:

- The `aws.imagebuilder.Component` resource defines the component used to customize the AI inference server image, which includes installing Python and your inference server application.

- The `aws.imagebuilder.ImageRecipe` resource references a base image (Amazon Linux 2 in the example) and includes the component defined above.

- The `aws.imagebuilder.InfrastructureConfiguration` resource specifies the infrastructure required for the AWS Image Builder environment to build the image.

- The `aws.imagebuilder.DistributionConfiguration` resource allows you to define how and where the built image should be distributed.

- The `aws.imagebuilder.ImagePipeline` resource creates a pipeline that runs the processes (build and tests) defined in the recipe and infrastructure configuration, optionally on a schedule.

Please replace placeholders such as `https://example.com/inference_server.py` with your actual locations for AI inference server code and resource values based on your requirements.

Run this program with the Pulumi CLI by installing Pulumi, setting up your AWS credentials, and running `pulumi up` in the same directory as this script. Pulumi will then provision the AWS resources as defined.