v3.8 model serving 部署成功

src/mvp/images/argus-ray-node/Dockerfile

@@ -6,6 +6,15 @@ SHELL ["/bin/bash", "-lc"]
 # Install supervisord (prefer pip to avoid relying on distro package manager).
 RUN python3 -m pip install --no-cache-dir supervisor
 
+# v3.8: Ray Serve LLM deps (keep Ray version pinned to what's already in the base image).
+# NOTE: base image already includes Ray; we only add extras.
+RUN RAY_VER="$(python3 -c 'import ray; print(ray.__version__)')" && \
+    python3 -m pip install --no-cache-dir "ray[serve,llm]==${RAY_VER}"
+# Ray Serve LLM's import chain currently pulls in ray.rllib which requires extra deps.
+# Install them explicitly to make `from ray.serve.llm import ...` work reliably.
+RUN python3 -m pip install --no-cache-dir gymnasium dm-tree && \
+    python3 -c "from ray.serve.llm import LLMConfig, build_openai_app; print('ray_serve_llm_ok')"
+
 RUN mkdir -p /opt/argus/py/argus/ray
 
 # Minimal embedded code for stateless pool (API code is intentionally excluded).

src/mvp/images/argus-ray-node/argus-head-ray.sh

@@ -16,9 +16,8 @@ exec ray start \
   --port="${ray_port}" \
   --dashboard-host=0.0.0.0 \
   --dashboard-port="${dashboard_port}" \
-  --num-cpus=0 \
+  --num-cpus="${ARGUS_HEAD_NUM_CPUS:-1}" \
   --num-gpus=0 \
   --disable-usage-stats \
   --block \
   ${ARGUS_RAY_EXTRA_ARGS:-}
-