chore: import upstream snapshot with attribution

docker/ray-llm/Dockerfile

@@ -0,0 +1,108 @@
+# syntax=docker/dockerfile:1.3-labs
+
+ARG BASE_IMAGE
+FROM "$BASE_IMAGE"
+
+COPY python/deplocks/llm/rayllm_*.lock ./
+COPY python/requirements/llm/patches/vllm-device-aware-compile-cache.patch ./
+
+# vLLM version tag to use for EP kernel and DeepGEMM install scripts
+# Keep in sync with vllm version in python/requirements/llm/llm-requirements.txt
+ARG VLLM_SCRIPTS_REF="v0.24.0"
+
+# Pin DeepEP to the V1 (NVSHMEM) commit that vLLM's own release image uses. The
+# install script's default drifted to DeepEP V2 ("NCCL Gin"), which needs
+# NCCL >= 2.30.4, but our image has nvidia-nccl-cu13==2.28.9 (via torch) so V2
+# fails to build. Keep in sync with DEEPEP_COMMIT_HASH in vllm's docker/Dockerfile.
+ARG DEEPEP_COMMIT_HASH="73b6ea4"
+
+RUN <<EOF
+#!/bin/bash
+
+set -euo pipefail
+
+PYTHON_CODE="$(python -c "import sys; v=sys.version_info; print(f'py{v.major}{v.minor}')")"
+
+if [[ "${PYTHON_CODE}" == "py312" ]]; then
+    CUDA_CODE=cu130
+    # Use nvshmem 3.3.24 which is the default for vLLM and compatible with CUDA 13
+    # https://github.com/vllm-project/vllm/blob/64ac1395e8d52e3e38910a62c7eb8524126730d8/tools/ep_kernels/install_python_libraries.sh#L14
+    NVSHMEM_VER=3.3.24
+else
+    echo "ray-llm supports Python 3.12 only (this image is ${PYTHON_CODE})."
+    exit 1
+fi
+
+# Hash verification is disabled because uv pip compile generates hashes from
+# PyPI, but unsafe-best-match may download from the CUDA index which serves
+# different builds of some packages (e.g. triton). The lock file still pins
+# exact versions, so integrity is maintained through version pinning.
+uv pip install --system --no-cache-dir --no-deps \
+    --index-strategy unsafe-best-match \
+    --no-verify-hashes \
+    -r "rayllm_${PYTHON_CODE}_${CUDA_CODE}.lock"
+
+# Include the CUDA device index in vLLM's compile cache paths so a worker never
+# reloads a torch.compile artifact built for a different physical GPU.
+# TODO (jeffreywang): Remove this patch once https://github.com/vllm-project/vllm/pull/38962 lands.
+VLLM_DEVICE_AWARE_COMPILE_CACHE_PATCH="$(pwd)/vllm-device-aware-compile-cache.patch"
+VLLM_SITE_PACKAGES="$(python - <<'PY'
+import site
+import sysconfig
+from pathlib import Path
+
+candidate_dirs = [
+    Path(sysconfig.get_paths()["purelib"]),
+    Path(sysconfig.get_paths()["platlib"]),
+    *(Path(path) for path in site.getsitepackages()),
+]
+
+for base_dir in dict.fromkeys(candidate_dirs):
+    import_utils = base_dir / "vllm" / "utils" / "import_utils.py"
+    if import_utils.exists():
+        print(base_dir)
+        break
+else:
+    raise SystemExit("vLLM import_utils.py not found")
+PY
+)"
+(
+    cd "${VLLM_SITE_PACKAGES}"
+    git apply "${VLLM_DEVICE_AWARE_COMPILE_CACHE_PATCH}"
+)
+
+sudo apt-get update -y && sudo apt-get install -y curl kmod pkg-config librdmacm-dev cmake
+
+# Fetch and run vLLM install scripts at pinned commit
+VLLM_RAW="https://raw.githubusercontent.com/vllm-project/vllm/${VLLM_SCRIPTS_REF}"
+
+# Tell uv to use system Python since the vLLM scripts use uv
+export UV_SYSTEM_PYTHON=1
+
+# Set CUDA architectures for building EP kernels
+# EP kernels + DeepGEMM require Hopper+ features (matches vLLM Dockerfile)
+export TORCH_CUDA_ARCH_LIST="9.0a 10.0a"
+
+# Install EP kernels (PPLX, DeepEP, and NVSHMEM)
+# Pin --deepep-ref to the V1 NVSHMEM-backend commit (see DEEPEP_COMMIT_HASH above);
+# the script's own default is the V2 NCCL-Gin backend that fails to compile here.
+curl -fsSL "${VLLM_RAW}/tools/ep_kernels/install_python_libraries.sh" | \
+    bash -s -- --workspace /home/ray/llm_ep_support --nvshmem-ver ${NVSHMEM_VER} --deepep-ref ${DEEPEP_COMMIT_HASH}
+
+# Install DeepGEMM
+curl -fsSL "${VLLM_RAW}/tools/install_deepgemm.sh" | bash
+
+# Export installed packages
+$HOME/anaconda3/bin/pip freeze > /home/ray/pip-freeze.txt
+
+sudo rm -rf /var/lib/apt/lists/*
+sudo apt-get clean
+
+EOF
+
+
+# vLLM 0.21.0 selects the FlashInfer top-k/top-p sampler during engine initialization
+# instead of the previous PyTorch-native/Triton sampling path. The FlashInfer sampler
+# introduces longer adds a large one-time engine initialization cost. To avoid performance
+# surprises, we disable the FlashInfer sampler by default.
+ENV VLLM_USE_FLASHINFER_SAMPLER=0

docker/ray-llm/cuda.wanda.yaml

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+name: "ray-llm-py$PYTHON_VERSION-cu$CUDA_VERSION-base"
+froms: ["cr.ray.io/rayproject/ray-py$PYTHON_VERSION-cu$CUDA_VERSION-base"]
+dockerfile: docker/ray-llm/Dockerfile
+srcs:
+  - python/requirements.txt
+  - python/deplocks/llm/rayllm_py312_cu130.lock
+  - python/requirements/llm/patches/vllm-device-aware-compile-cache.patch
+build_args:
+  - BASE_IMAGE=cr.ray.io/rayproject/ray-py$PYTHON_VERSION-cu$CUDA_VERSION-base
+tags:
+  - cr.ray.io/rayproject/ray-llm-py$PYTHON_VERSION-cu$CUDA_VERSION-base