kuberay Docker & Self-Hosted Deployment

GitHub

A toolkit to run Ray applications on Kubernetes

2,520 stars Go Go 1 file · ~7,676 tokens #apache#deep-learning#kubernetes#machine-learning#ray
1. Pull Image (DOCKERHUB) docker pull ray-project/kuberay:latest
2. Docker Run (1-Click Launch) docker run -d --name kuberay --restart always ray-project/kuberay:latest
3. Podman Run podman run -d --name kuberay --restart always ray-project/kuberay:latest
4. Skopeo Inspect (Zero-Pull Manifest) skopeo inspect docker://docker.io/ray-project/kuberay:latest
5. ORAS Pull (OCI Artifact) oras pull ray-project/kuberay:latest
apiserver/Dockerfile 
# Build the backend service
FROM golang:1.26-bookworm AS builder

WORKDIR /workspace

# Copy the Go Modules manifests
COPY go.mod go.sum ./
COPY ray-operator/go.mod ray-operator/go.mod
COPY ray-operator/go.sum ray-operator/go.sum

# cache deps before building and copying source so that we don't need to re-download as much
# and so that source changes don't invalidate our downloaded layer
RUN go mod download

# Copy the go source
COPY proto/ proto/
COPY ray-operator/ ray-operator/
COPY apiserver/ apiserver/
COPY apiserversdk/ apiserversdk/

WORKDIR /workspace/apiserver


# Build
USER root
WORKDIR /workspace/apiserver
RUN CGO_ENABLED=0 GOOS=linux GOARCH=amd64 go build -a -o kuberay-apiserver cmd/main.go

FROM scratch
WORKDIR /workspace
COPY --from=builder /workspace/apiserver/kuberay-apiserver apiserver/
# Support serving swagger files
COPY proto/ proto/
USER 65532:65532

ENTRYPOINT ["/workspace/apiserver/kuberay-apiserver"]