GENOPS04-BP01 Automate generative AI application lifecycle with infrastructure as code (IaC)

Implementing and managing IaC is crucial for consistent, version-controlled, and automated infrastructure deployment across environments. This practice streamlines deployment, reduces errors, and enhances team collaboration. IaC helps customers achieve efficiency, reliability, and scalability in infrastructure management, which allows for rapid iteration, straightforward rollback, and improved governance and results in secure deployments.

Desired outcome: After implementing the practice of automating the lifecycle management of generative AI workloads using IaC, customers have version control infrastructure automated through CI/CD pipelines.

Benefits of establishing this best practice: Safely automate where possible - Define your entire workload and its operations (applications, infrastructure, configuration, and procedures) as code, facilitating infrastructure level change management, infrastructure version control, and advanced paradigms such as self-healing infrastructure.

Level of risk exposed if this best practice is not established: High

Implementation guidance

Automate your application development and migration through stages using IaC principles. When selecting your tool stack, consider your team's skills and project requirements. Use tools such as AWS Cloud Development Kit (AWS CDK), AWS CloudFormation, or Terraform to define and manage the infrastructure resources required for your application. These resources may include Amazon Bedrock, Amazon API Gateway, AWS Lambda functions, and AWS Data Pipelines, all of which help you create a reproducible and version-controlled stack.

Store your IaC templates in a version control system like Git. This practice facilitates collaboration among team members, allows for tracking changes over time, and enables rolling back to previous versions if necessary.

Implement a CI/CD pipeline using AWS CodePipeline, Jenkins, or a similar tool. This pipeline should initiate on code changes, run tests on your IaC templates, and automatically deploy infrastructure changes.

Manage your IaC templates to handle multiple environments such as development, testing and staging, and production. To maintain consistency across environments, use the same templates with different parameters.

For Hyperpod, use AWS CloudFormation, AWS CDK, or Terraform to define clusters, VPCs, security groups, EKS node groups, networking policies, and Amazon SageMaker AI resources.

For Amazon EKS, describe your Kubernetes deployments, secrets management, and ML workflows in YAML or Helm charts, and then manage those using CI/CD pipelines to automatically provision and update infrastructure.

For Slurm, automate creation and scaling of compute nodes, tracker scripts, and cluster configuration using the same IaC tools.

HyperPod Recipes serve as the cornerstone for implementing operational task automation by providing pre-built automation frameworks that reduce the need for manual operational tasks in distributed training environments. These recipes deliver IaC templates that automatically provision, configure, and manage complex training workflows across both EKS and Slurm orchestrated clusters, directly addressing the core principle of reducing manual effort and minimizing human error in operational activities.

Establish practices and controls to help you maintain compliance of your resources, like using AWS Config to track resource configurations. Implement Service Catalog for standardized resource provisioning, and regularly audit your IaC templates for security best practices and compliance.

Be mindful of the time and cost involved in model training and customization when automating these activities for your workload, use historical data to determine when training and customization might be needed for your workload.

Implementation steps

Resources

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