A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 13, 02:00 UTC
[NIXL][XPU] update install script of NIXL (#28778)
Signed-off-by: zhenwei-intel <zhenwei.liu@intel.com>
liuzhenwei committed
Nov 17, 2025 at 11:01 UTC
d64429bb369d4087f9f91609e7275c4901d65aea
2 files changed
+3
-1
docker/Dockerfile.xpu
+2
-1
@@ -14,6 +14,7 @@ RUN apt clean && apt-get update -y && \
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libxext6 \
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libgl1 \
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lsb-release \
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+ libaio-dev \
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numactl \
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wget \
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vim \
@@ -68,8 +69,8 @@ RUN --mount=type=cache,target=/root/.cache/pip \
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RUN python3 -m pip install -e tests/vllm_test_utils
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# install nixl from source code
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+ENV NIXL_VERSION=0.7.0
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RUN python3 /workspace/vllm/tools/install_nixl_from_source_ubuntu.py
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-ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/lib/python3.12/dist-packages/.nixl.mesonpy.libs/plugins/"
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RUN --mount=type=cache,target=/root/.cache/pip \
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pip uninstall oneccl oneccl-devel -y
tools/install_nixl_from_source_ubuntu.py
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@@ -175,6 +175,7 @@ def build_and_install_prerequisites(args):
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build_env["LD_LIBRARY_PATH"] = (
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f"{ucx_lib_path}:{ucx_plugin_path}:{existing_ld_path}".strip(":")
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)
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+ build_env["LDFLAGS"] = "-Wl,-rpath,$ORIGIN"
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print(f"--> Using LD_LIBRARY_PATH: {build_env['LD_LIBRARY_PATH']}", flush=True)
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temp_wheel_dir = os.path.join(ROOT_DIR, "temp_wheelhouse")