新增本地 K8s Ray Daft 音频处理环境

.dockerignore

@@ -1,10 +1,18 @@
 .git
+.claude
 .idea
 __pycache__
 *.pyc
 .venv
+.benchmarks
+.pytest_cache
+.ruff_cache
+.coverage
 .env
-data/
+# K8s 音频冒烟镜像只需要仓库自带的少量种子音频；其他本地数据不能进入构建上下文。
+data/*
+!data/audio/
+!data/audio/**
 *.db
 *.sqlite
 *.sqlite3

deploy/k8s/Dockerfile.audio

@@ -0,0 +1,31 @@
+# 本地 K8s 音频链路的完整 CPU runtime。
+# 此镜像与项目 uv.lock 保持一致，包含真实 SenseVoice 所需依赖和 system codec；
+# Kubernetes 资源声明不申请 GPU，CUDA 相关依赖即使存在也不会被使用。
+FROM python:3.12-slim
+
+COPY --from=ghcr.io/astral-sh/uv:0.9.17 /uv /uvx /bin/
+
+WORKDIR /app
+ENV UV_COMPILE_BYTECODE=1 \
+    UV_PROJECT_ENVIRONMENT=/opt/venv \
+    # PyPI 上的 Linux ARM64 Torch 依赖包含数百 MiB 的 CUDA wheel；本地网络较慢时，
+    # 解压阶段可能超过默认超时。保留有限下载并发，并把单个请求/解压的超时提高到 15 分钟。
+    UV_HTTP_TIMEOUT=900 \
+    UV_CONCURRENT_DOWNLOADS=2 \
+    PATH=/opt/venv/bin:$PATH \
+    CUDA_VISIBLE_DEVICES=""
+
+RUN apt-get update \
+    && apt-get install --yes --no-install-recommends \
+        build-essential ffmpeg libsndfile1 wget curl git \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY pyproject.toml uv.lock README.md ./
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv sync --frozen --no-install-project --no-dev
+COPY . .
+RUN --mount=type=cache,target=/root/.cache/uv uv sync --frozen --no-dev
+# 项目锁定 Ray 本体；最终 `uv sync` 会移除不在 lock 中的额外包，故必须在其之后
+# 安装 KubeRay health check 和 Ray Dashboard 需要的 default extra。
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip install --python /opt/venv/bin/python "ray[default]==2.55.1"

deploy/k8s/minio.yaml

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+# 本地专用 MinIO，不读取宿主机或现有对象存储的任何数据。
+apiVersion: v1
+kind: Secret
+metadata:
+  name: minio-credentials
+  namespace: daft-ray
+type: Opaque
+stringData:
+  MINIO_ROOT_USER: minioadmin
+  MINIO_ROOT_PASSWORD: minioadmin
+---
+apiVersion: v1
+kind: PersistentVolumeClaim
+metadata:
+  name: minio-data
+  namespace: daft-ray
+spec:
+  accessModes: [ReadWriteOnce]
+  resources:
+    requests:
+      storage: 4Gi
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: minio
+  namespace: daft-ray
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: minio
+  template:
+    metadata:
+      labels:
+        app: minio
+    spec:
+      containers:
+        - name: minio
+          image: minio/minio:RELEASE.2025-07-23T15-54-02Z
+          args: [server, /data, --console-address, ":9001"]
+          envFrom:
+            - secretRef:
+                name: minio-credentials
+          ports:
+            - containerPort: 9000
+              name: api
+            - containerPort: 9001
+              name: console
+          resources:
+            requests:
+              cpu: 250m
+              memory: 512Mi
+            limits:
+              cpu: "1"
+              memory: 1Gi
+          volumeMounts:
+            - name: data
+              mountPath: /data
+      volumes:
+        - name: data
+          persistentVolumeClaim:
+            claimName: minio-data
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: minio
+  namespace: daft-ray
+spec:
+  selector:
+    app: minio
+  ports:
+    - name: api
+      port: 9000
+      targetPort: api
+    - name: console
+      port: 9001
+      targetPort: console
+---
+# 创建 benchmark bucket；Job 可安全重复执行，现有 bucket 不会被删除或清空。
+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: minio-init
+  namespace: daft-ray
+spec:
+  backoffLimit: 20
+  template:
+    spec:
+      restartPolicy: OnFailure
+      containers:
+        - name: mc
+          image: minio/mc:RELEASE.2025-07-21T05-28-08Z
+          envFrom:
+            - secretRef:
+                name: minio-credentials
+          command: [/bin/sh, -ec]
+          args:
+            - >-
+              until mc alias set local http://minio:9000 "$MINIO_ROOT_USER" "$MINIO_ROOT_PASSWORD";
+              do sleep 2; done;
+              mc mb --ignore-existing local/benchmark

deploy/k8s/mock-llm.yaml

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+# 使用项目内 OpenAI-compatible Mock LLM，避免本地 E2E 依赖外部模型服务或密钥。
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: mock-llm
+  namespace: daft-ray
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: mock-llm
+  template:
+    metadata:
+      labels:
+        app: mock-llm
+    spec:
+      containers:
+        - name: mock-llm
+          image: multimodal-toolkit:audio-local
+          imagePullPolicy: IfNotPresent
+          command: [python, -m, benchmark.audio, serve-mock, --host, 0.0.0.0, --port, "8010", --profile, fast]
+          ports:
+            - containerPort: 8010
+              name: http
+          resources:
+            requests:
+              cpu: 100m
+              memory: 256Mi
+            limits:
+              cpu: 500m
+              memory: 512Mi
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: mock-llm
+  namespace: daft-ray
+spec:
+  selector:
+    app: mock-llm
+  ports:
+    - name: http
+      port: 8010
+      targetPort: http

deploy/k8s/namespace.yaml

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+# 本地 Daft/Ray 演示环境的所有资源都隔离在此命名空间，便于一键清理。
+apiVersion: v1
+kind: Namespace
+metadata:
+  name: daft-ray

deploy/k8s/prometheus.yaml

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+# 轻量、单副本 Prometheus：只保存本地 Ray 集群的指标，避免本地开发机负担过重。
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: prometheus-config
+  namespace: daft-ray
+data:
+  prometheus.yml: |
+    global:
+      scrape_interval: 5s
+      evaluation_interval: 5s
+    scrape_configs:
+      - job_name: ray
+        kubernetes_sd_configs:
+          # Pod discovery 配合 Ray 节点标签，避免 Endpoints 元数据在不同
+          # Prometheus/Kubernetes 版本间的端口标签差异。
+          - role: pod
+            namespaces:
+              names: [daft-ray]
+        relabel_configs:
+          - source_labels: [__meta_kubernetes_pod_label_ray_io_is_ray_node]
+            action: keep
+            # KubeRay 将 ray.io/is-ray-node 标签的值设为 "yes"。
+            regex: "yes"
+          - source_labels: [__meta_kubernetes_pod_container_port_name]
+            action: keep
+            regex: metrics
+---
+ # Prometheus 通过 Kubernetes 服务发现读取 Service、Endpoints 和 Pod；默认账号没有
+ # 这些读取权限，会导致 UI 中没有任何 active target。
+apiVersion: v1
+kind: ServiceAccount
+metadata:
+  name: prometheus
+  namespace: daft-ray
+---
+apiVersion: rbac.authorization.k8s.io/v1
+kind: Role
+metadata:
+  name: prometheus-discovery
+  namespace: daft-ray
+rules:
+  - apiGroups: [""]
+    resources: ["services", "endpoints", "pods"]
+    verbs: ["get", "list", "watch"]
+---
+apiVersion: rbac.authorization.k8s.io/v1
+kind: RoleBinding
+metadata:
+  name: prometheus-discovery
+  namespace: daft-ray
+subjects:
+  - kind: ServiceAccount
+    name: prometheus
+    namespace: daft-ray
+roleRef:
+  apiGroup: rbac.authorization.k8s.io
+  kind: Role
+  name: prometheus-discovery
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: prometheus
+  namespace: daft-ray
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: prometheus
+  template:
+    metadata:
+      labels:
+        app: prometheus
+    spec:
+      serviceAccountName: prometheus
+      containers:
+        - name: prometheus
+          image: prom/prometheus:v3.5.0
+          args:
+            - --config.file=/etc/prometheus/prometheus.yml
+            - --storage.tsdb.path=/prometheus
+            - --storage.tsdb.retention.time=2d
+          ports:
+            - containerPort: 9090
+              name: web
+          resources:
+            requests:
+              cpu: 100m
+              memory: 256Mi
+            limits:
+              cpu: 500m
+              memory: 512Mi
+          volumeMounts:
+            - name: config
+              mountPath: /etc/prometheus
+      volumes:
+        - name: config
+          configMap:
+            name: prometheus-config
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: prometheus
+  namespace: daft-ray
+spec:
+  selector:
+    app: prometheus
+  ports:
+    - name: web
+      port: 9090
+      targetPort: web

deploy/k8s/ray-metrics-service.yaml

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+# Prometheus 集群内抓取所有 Ray Pod 的 OpenMetrics endpoint（head 与 worker）。
+apiVersion: v1
+kind: Service
+metadata:
+  name: daft-ray-metrics
+  namespace: daft-ray
+  labels:
+    app.kubernetes.io/name: daft-ray
+spec:
+  selector:
+    ray.io/cluster: daft-ray
+  ports:
+    - name: metrics
+      port: 8080
+      targetPort: 8080