Orchestrated AI Workflows on Kubernetes with AWS OAM

Question

Pulumi · Accepted Answer

Orchestrating AI workflows on Kubernetes using AWS can be a complex process, as it often involves combining Kubernetes resources with specific AWS services like SageMaker for machine learning workflows. One approach to streamline this orchestration is by utilizing the AWS Controllers for Kubernetes (ACK), which allows you to define and use AWS service resources directly from Kubernetes.

Since you're interested in AWS OAM (AWS Controllers for Kubernetes), here's a potential setup in Pulumi using Kubernetes and AWS resources. We'll focus on creating an orchestrated AI workflow that can run on Kubernetes. For this example, I will assume that a Kubernetes cluster is already running and configured.

For the AI workflow, we will use Amazon SageMaker, which allows you to build, train, and deploy machine learning models at scale. With Pulumi, we'll create the SageMaker resources necessary to achieve this, including a training job and a model endpoint that can be triggered by a Kubernetes job.

I'll also use AWS Managed Workflows for Apache Airflow (MWAA) since it's a managed orchestration service for running Apache Airflow on AWS. It's a common tool for setting up complex workflows, and it could be useful for managing machine learning pipelines.

Below is a program that sets up such an orchestrated AI workflow using Pulumi:

```python
import pulumi
import pulumi_aws as aws
import pulumi_aws_native as aws_native
import pulumi_kubernetes as k8s

# Assume AWS and Kubernetes providers are configured.

# Defining an Amazon SageMaker training job
training_job = aws_native.sagemaker.TrainingJob("aiTrainingJob",
    training_job_name="example-ai-training-job",
    algorithm_specification={
        "trainingImage": "123456789012.dkr.ecr.us-west-2.amazonaws.com/sagemaker-example:latest", # Replace with your SageMaker compatible Docker image URI
        "trainingInputMode": "File",
    },
    output_data_config={
        "s3_output_path": "s3://mybucket/prefix/"
    },
    resource_config={
        "instance_count": 1,
        "instance_type": "ml.p2.xlarge",
        "volume_size_in_gb": 50,
    },
    stopping_condition={
        "max_runtime_in_seconds": 3600,
    },
    role_arn=aws.iam.Role("aiSageMakerRole", assume_role_policy=sagemaker_role_policy).arn # Define appropriate role and policy
)

# Defining an Amazon SageMaker Model for deployment
model = aws_native.sagemaker.Model("aiModel",
    execution_role_arn=training_job.role_arn, # Reuse the role from training job
    primary_container={
        "image": "123456789012.dkr.ecr.us-west-2.amazonaws.com/sagemaker-example:latest", # Same as training job, this should be your model image
        "model_data_url": training_job.model_artifacts.s3_model_artifacts,
    }
)

# Optionally, define an endpoint configuration and endpoint if you want to deploy the model
endpoint_config = aws_native.sagemaker.EndpointConfig("aiModelEndpointConfig",
    production_variants=[{
        "variantName": "AllTraffic",
        "modelName": model.name,
        "instanceType": "ml.m4.xlarge",
    }]
)

endpoint = aws_native.sagemaker.Endpoint("aiModelEndpoint",
    endpoint_config_name=endpoint_config.endpoint_config_name
)

# Create a Kubernetes job that invokes the SageMaker model endpoint
namespace = "default" # Change this if you have a specific namespace
job_yaml_file = "my_k8s_job.yaml" # The path to your Kubernetes job YAML definition

# Load the Kubernetes job from a YAML file
k8s_job = k8s.yaml.ConfigFile("k8sJob",
    file=job_yaml_file,
    transformations=[lambda obj: obj['metadata']['namespace'] = namespace if 'metadata' in obj else {}]
)

# Export the results
pulumi.export('training_job_name', training_job.training_job_name)
pulumi.export('model_name', model.model_name)
pulumi.export('endpoint_name', endpoint.endpoint_name)
pulumi.export('k8s_job_name', k8s_job.metadata.name)
```

In this program, we first define a SageMaker training job that will train a machine learning model using a specific Docker image and input data. We then create a SageMaker model from the trained artifacts and deploy it to an endpoint.

We also define a Kubernetes job that can invoke the SageMaker model. The Kubernetes job can be further customized to trigger model retraining or re-deployment processes as needed.

Remember to replace placeholder values with actual data you intend to use, like the Docker image URIs and the IAM role.

This approach allows you to leverage the scalability of AWS services while maintaining the flexibility of Kubernetes orchestration. The above example provides a starting point that you can expand to include more sophisticated workflow logic and additional AWS services.