software-engineering/spark-vllm-docker
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Improved caching

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This commit is contained in:
eugr
2025-12-13 21:34:57 -08:00
parent 76a8e92c86
commit cc3e73feb1
1 changed files with 14 additions and 9 deletions
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@@ -1,5 +1,7 @@
# syntax=docker/dockerfile:1.6
# =========================================================
# STAGE 1: Builder (Heavy image with compiler toolchain)
# STAGE 1: Builder (Builds vLLM from Source)
# =========================================================
FROM nvidia/cuda:13.0.2-devel-ubuntu24.04 AS builder
@@ -44,14 +46,17 @@ ARG CACHEBUST_DEPS=1
# Using --mount=type=cache ensures that even if this layer invalidates,
# pip reuses previously downloaded wheels.
RUN --mount=type=cache,target=/root/.cache/pip \
# Set pip cache directory
ENV PIP_CACHE_DIR=/root/.cache/pip
RUN --mount=type=cache,id=pip-cache,target=/root/.cache/pip \
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu130
# You can add termplotlib to the list below if you want to visualize text graphs in vllm bench serve
RUN --mount=type=cache,target=/root/.cache/pip \
RUN --mount=type=cache,id=pip-cache,target=/root/.cache/pip \
pip install xgrammar triton fastsafetensors
RUN --mount=type=cache,target=/root/.cache/pip \
RUN --mount=type=cache,id=pip-cache,target=/root/.cache/pip \
pip install flashinfer-python --no-deps --index-url https://flashinfer.ai/whl && \
pip install flashinfer-cubin --index-url https://flashinfer.ai/whl && \
pip install flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu130 && \
@@ -64,7 +69,7 @@ ARG CACHEBUST_VLLM=1
# 4. Smart Git Clone (Fetch changes instead of full re-clone)
# We mount a cache at /repo-cache. This directory persists on your host machine.
RUN --mount=type=cache,target=/repo-cache \
RUN --mount=type=cache,id=repo-cache,target=/repo-cache \
# 1. Go into the persistent cache directory
cd /repo-cache && \
# 2. Logic: Clone if missing, otherwise Fetch & Reset
@@ -85,7 +90,7 @@ RUN --mount=type=cache,target=/repo-cache \
WORKDIR $VLLM_BASE_DIR/vllm
# Prepare build requirements
RUN --mount=type=cache,target=/root/.cache/pip \
RUN --mount=type=cache,id=pip-cache,target=/root/.cache/pip \
python3 use_existing_torch.py && \
sed -i "/flashinfer/d" requirements/cuda.txt && \
pip install -r requirements/build.txt
@@ -98,13 +103,13 @@ RUN patch -p1 < fastsafetensors.patch
# Final Compilation
# We mount the ccache directory here. Ideally, map this to a host volume for persistence
# across totally separate `docker build` invocations.
RUN --mount=type=cache,target=/root/.ccache \
--mount=type=cache,target=/root/.cache/pip \
RUN --mount=type=cache,id=ccache,target=/root/.ccache \
--mount=type=cache,id=pip-cache,target=/root/.cache/pip \
pip install --no-build-isolation . -v
# =========================================================
# STAGE 2: Runner (Lightweight Runtime Image)
# STAGE 2: Runner (Transfers only necessary artifacts)
# =========================================================
FROM nvidia/cuda:13.0.2-devel-ubuntu24.04 AS runner