KubeRay Worker Images

GPU-specific Ray worker images for the DaviesTechLabs AI/ML platform.

Features

• BuildKit optimized: Cache mounts for apt and pip speed up rebuilds
• OCI compliant: Standard image labels (org.opencontainers.image.*)
• Health checks: Built-in HEALTHCHECK for container orchestration
• Non-root execution: Ray runs as unprivileged ray user
• Retry logic: Entrypoint waits for Ray head with exponential backoff

Images

Image	GPU Target	Workloads	Base
ray-worker-nvidia	NVIDIA CUDA 12.1 (RTX 2070)	Whisper STT, XTTS TTS	rayproject/ray-ml:2.53.0-py310-cu121
ray-worker-rdna2	AMD ROCm 6.4 (Radeon 680M)	BGE Embeddings	rocm/pytorch:rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.6.0
ray-worker-strixhalo	AMD ROCm 7.1 (Strix Halo)	vLLM, BGE	rocm/pytorch:rocm7.1_ubuntu24.04_py3.12_pytorch_release_2.8.0
ray-worker-intel	Intel XPU (Arc)	BGE Reranker	rayproject/ray-ml:2.53.0-py310

Building Locally

# Lint Dockerfiles (requires hadolint)
make lint

# Build all images
make build-all

# Build specific image
make build-nvidia
make build-rdna2
make build-strixhalo
make build-intel

# Push to Gitea registry (requires login)
make login
make push-all

# Release with version tag
make VERSION=v1.0.0 release

CI/CD

Images are automatically built and pushed to git.daviestechlabs.io package registry on:

• Push to main branch
• Git tag creation (e.g., v1.0.0)

Gitea Actions Secrets Required

Add these secrets in Gitea repo settings → Actions → Secrets:

Secret	Description
REGISTRY_USER	Gitea username
REGISTRY_TOKEN	Gitea access token with package:write scope

Directory Structure

kuberay-images/
├── dockerfiles/
│   ├── Dockerfile.ray-worker-nvidia
│   ├── Dockerfile.ray-worker-rdna2
│   ├── Dockerfile.ray-worker-strixhalo
│   ├── Dockerfile.ray-worker-intel
│   └── ray-entrypoint.sh
├── ray-serve/
│   ├── serve_embeddings.py
│   ├── serve_whisper.py
│   ├── serve_tts.py
│   ├── serve_llm.py
│   ├── serve_reranker.py
│   └── requirements.txt
├── .gitea/workflows/
│   └── build-push.yaml
├── Makefile
└── README.md

Environment Variables

Variable	Description	Default
RAY_HEAD_SVC	Ray head service name	ai-inference-raycluster-head-svc
GPU_RESOURCE	Custom Ray resource name	gpu_nvidia, gpu_amd, etc.
NUM_GPUS	Number of GPUs to expose	1

Node Allocation

Node	Image	GPU	Memory
elminster	ray-worker-nvidia	RTX 2070	8GB VRAM
khelben	ray-worker-strixhalo	Strix Halo	64GB Unified
drizzt	ray-worker-rdna2	Radeon 680M	12GB VRAM
danilo	ray-worker-intel	Intel Arc	16GB Shared

Related

• homelab-design - Architecture documentation
• homelab-k8s2 - Kubernetes manifests