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The following repository is self-contained in regard to enabling this pattern: GitLab HA Scaling Runner Vending Machine for AWS EC2 ASG. EKS cluster provisioning best practicesĮKS Cluster Provisioning Patterns - considerations for setting up EKS cluster for runners and for integrating. GitLab SRE Considerations for AWS - important information and known issues for planning, implementing, upgrading and long term management of GitLab instances and runners on AWS. GitLab Site Reliability Engineering (SRE) for AWS Manual instructions from which you may build out a GitLab instance or create your own Infrastructure as Code (IaC). Omnibus GitLab on AWS EC2 (HA) - instructions for installing GitLab on EC2 instances. GitLab Cloud Native Hybrid is the supported way to put as much of GitLab as possible into Kubernetes. It also includes Bill of Materials listings and links to Infrastructure as Code. This document includes instructions, patterns, and automation for installing GitLab Cloud Native Hybrid on AWS EKS. Provision GitLab Cloud Native Hybrid on AWS EKS (HA). Implementation patterns information Install GitLab Cloud Native Hybrid on AWS EKS (HA) Implementation patterns are built on the foundational information and testing done for Reference Architectures and allow architects and implementers at GitLab, GitLab Customers, and GitLab Partners to build out deployments with less experimentation and a higher degree of confidence that the results will perform as expected. This is what enables Reference Architectures to be adaptable to the broadest number of supported implementations. This generally means they have a highly-granular "machine" to "server role" specification and focus on system elements that impact performance. Reference Architectures are purpose-designed to be non-implementation specific so they can be extrapolated to as many environments as possible. GitLab Reference Architectures give qualified and tested guidance on the recommended ways GitLab can be configured to meet the performance requirements of various workloads.