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playbooks/roles/README.md

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Playbook roles planning

This document clarifies what should live under /playbooks/roles/ for host-level automation (Ansible) versus what should be delivered through Helm charts, and ensures we cover the five tiers across data platforms: data warehouse → big data → ML → DL → large models.

Scope rules

  • Ansible roles: host-coupled configuration that is not itself a cloud resource (GPU driver/runtime, OS tuning, user/SSH prep, rendering on-host config files, database bootstrapping, etc.).
  • Helm charts: anything that runs as a Kubernetes workload (operators, clusters, services running in pods).

Base roles shared across tiers (Ansible)

  • GPU driver and CUDA stack installation.
  • Docker/Containerd runtime setup.
  • System parameter tuning (kernel limits, hugepages, network stack), plus user home/SSH layout.
  • Database initialization tasks (e.g., bootstrap PostgreSQL/ClickHouse on hosts) and rendering templated configs such as ClickHouse/users.xml.

Coverage by capability tier

Tier Host-focused roles (Ansible) Kubernetes services (Helm)
Data warehouse ClickHouse host bootstrap & config render; PostgreSQL init where needed. —
Big data JVM/runtime, local disks, and OS tuning for data nodes. Spark Operator; Flink Operator; Kafka/Redpanda; MinIO.
ML GPU runtime base (drivers, container runtime), Python ML base image prep; user workspace/SSH. Ray Cluster; MLflow; JupyterHub.
DL Same GPU/system tuning plus inference node bootstrap (tensorRT/cuDNN as needed). Triton Inference Server; LMDeploy (for deployment runtimes).
Large models Secure SSH/user profiles and config templating for model storage/IO. vLLM serving; model-specific Helm releases atop Ray/K8s.

Suggested role layout under /playbooks/roles/

  • common/ (new): shared tasks for system tuning, users/SSH, and package repos for GPU/runtime support.
  • gpu/: install GPU drivers + CUDA toolkit.
  • container_runtime/: install and configure Docker/Containerd with GPU runtime integration.
  • database_init/: bootstrap on-host databases (e.g., PostgreSQL, ClickHouse), render config files (users.xml, etc.).
  • bigdata_node_prep/: OS/disk tuning for Spark/Flink/Kafka/Redpanda/MinIO hosts.
  • ml_node_prep/: Python/conda base, SSH workspace prep for ML workloads.
  • dl_inference_node/: tensorRT/cuDNN dependencies and runtime checks for Triton/LMDeploy nodes.

Helm-delivered components should live under playbooks/roles/charts/ or the repo’s Helm release structure and include Spark/Flink Operators, Kafka/Redpanda/MinIO, Ray Cluster, Triton, vLLM/LMDeploy, MLflow, and JupyterHub.