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chore(docker): reduce size between docker builds #7571
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@@ -13,52 +13,62 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \ | |
git | ||
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# Install `uv` for package management | ||
COPY --from=ghcr.io/astral-sh/uv:0.5.5 /uv /uvx /bin/ | ||
COPY --from=ghcr.io/astral-sh/uv:0.5.21 /uv /uvx /bin/ | ||
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ENV VIRTUAL_ENV=/opt/venv | ||
ENV PATH="$VIRTUAL_ENV/bin:$PATH" | ||
ENV INVOKEAI_SRC=/opt/invokeai | ||
ENV PYTHON_VERSION=3.11 | ||
ENV UV_PYTHON=3.11 | ||
ENV UV_COMPILE_BYTECODE=1 | ||
ENV UV_LINK_MODE=copy | ||
ENV UV_INDEX="https://download.pytorch.org/whl/cu124" | ||
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ARG GPU_DRIVER=cuda | ||
ARG TARGETPLATFORM="linux/amd64" | ||
# unused but available | ||
ARG BUILDPLATFORM | ||
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# Switch to the `ubuntu` user to work around dependency issues with uv-installed python | ||
RUN mkdir -p ${VIRTUAL_ENV} && \ | ||
mkdir -p ${INVOKEAI_SRC} && \ | ||
RUN mkdir -p ${INVOKEAI_SRC} && \ | ||
chmod -R a+w /opt | ||
USER ubuntu | ||
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# Install python and create the venv | ||
RUN uv python install ${PYTHON_VERSION} && \ | ||
uv venv --relocatable --prompt "invoke" --python ${PYTHON_VERSION} ${VIRTUAL_ENV} | ||
# Install python | ||
RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \ | ||
uv python install ${PYTHON_VERSION} | ||
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WORKDIR ${INVOKEAI_SRC} | ||
COPY invokeai ./invokeai | ||
COPY pyproject.toml ./ | ||
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# Editable mode helps use the same image for development: | ||
# the local working copy can be bind-mounted into the image | ||
# at path defined by ${INVOKEAI_SRC} | ||
# Install project's dependencies as a separate layer so they aren't rebuilt every commit. | ||
# bind-mount instead of copy to defer adding sources to the image until next layer. | ||
# | ||
# NOTE: there are no pytorch builds for arm64 + cuda, only cpu | ||
# x86_64/CUDA is the default | ||
RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \ | ||
--mount=type=bind,source=pyproject.toml,target=pyproject.toml \ | ||
--mount=type=bind,source=invokeai,target=invokeai \ | ||
if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \ | ||
UV_INDEX="https://download.pytorch.org/whl/cpu"; \ | ||
elif [ "$GPU_DRIVER" = "rocm" ]; then \ | ||
UV_INDEX="https://download.pytorch.org/whl/rocm6.1"; \ | ||
fi && \ | ||
uv sync --no-install-project | ||
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# Now that the bulk of the dependencies have been installed, copy in the project files that change more frequently. | ||
COPY invokeai invokeai | ||
COPY pyproject.toml . | ||
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RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \ | ||
--mount=type=bind,source=pyproject.toml,target=pyproject.toml \ | ||
if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \ | ||
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \ | ||
UV_INDEX="https://download.pytorch.org/whl/cpu"; \ | ||
elif [ "$GPU_DRIVER" = "rocm" ]; then \ | ||
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm6.1"; \ | ||
else \ | ||
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu124"; \ | ||
UV_INDEX="https://download.pytorch.org/whl/rocm6.1"; \ | ||
fi && \ | ||
uv pip install --python ${PYTHON_VERSION} $extra_index_url_arg -e "." | ||
uv sync | ||
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#### Build the Web UI ------------------------------------ | ||
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FROM node:20-slim AS web-builder | ||
FROM docker.io/node:20-slim AS web-builder | ||
ENV PNPM_HOME="/pnpm" | ||
ENV PATH="$PNPM_HOME:$PATH" | ||
RUN corepack use [email protected] | ||
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@@ -97,26 +107,24 @@ RUN apt update && apt install -y --no-install-recommends \ | |
apt-get clean && apt-get autoclean | ||
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ENV INVOKEAI_SRC=/opt/invokeai | ||
ENV VIRTUAL_ENV=/opt/venv | ||
ENV PYTHON_VERSION=3.11 | ||
ENV INVOKEAI_ROOT=/invokeai | ||
ENV INVOKEAI_HOST=0.0.0.0 | ||
ENV INVOKEAI_PORT=9090 | ||
ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH" | ||
ENV PATH="$INVOKEAI_SRC/.venv/bin:$INVOKEAI_SRC:$PATH" | ||
ENV CONTAINER_UID=${CONTAINER_UID:-1000} | ||
ENV CONTAINER_GID=${CONTAINER_GID:-1000} | ||
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# Install `uv` for package management | ||
# and install python for the ubuntu user (expected to exist on ubuntu >=24.x) | ||
# this is too tiny to optimize with multi-stage builds, but maybe we'll come back to it | ||
COPY --from=ghcr.io/astral-sh/uv:0.5.5 /uv /uvx /bin/ | ||
COPY --from=ghcr.io/astral-sh/uv:0.5.21 /uv /uvx /bin/ | ||
USER ubuntu | ||
RUN uv python install ${PYTHON_VERSION} | ||
USER root | ||
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# --link requires buldkit w/ dockerfile syntax 1.4 | ||
COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC} | ||
COPY --link --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV} | ||
COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist | ||
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# Link amdgpu.ids for ROCm builds | ||
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@@ -101,8 +101,7 @@ dependencies = [ | |
"xformers" = [ | ||
# Core generation dependencies, pinned for reproducible builds. | ||
"xformers>=0.0.28.post1; sys_platform!='darwin'", | ||
# Auxiliary dependencies, pinned only if necessary. | ||
"triton; sys_platform=='linux'", | ||
Comment on lines
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. As far as I can tell, CUDA builds of torch 2.4.1 have a dependency on triton, which has two consequences:
If we had an exact dependency on However, since the torch version is left ambiguous, it's possible for the resolver to decide that non-CUDA torch==2.4.1 is a better solution since that doesn't conflict with the latest version of xformers. 😖 There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. We'll hopefully be bumping torch soon to support the new Nvidia GPUs, so we'll address it then. There's also an additional quirk of requiring a different torch pin for AMD, so the rabbit hole will go even deeper. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. yeah, and uv has a documentation page about torch specifically: https://docs.astral.sh/uv/guides/integration/pytorch/#configuring-accelerators-with-optional-dependencies I did make an attempt at implementing that here, but its scope quickly grew far past what I wanted to deal with in this docker-focused PR. |
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# torch 2.4+cu carries its own triton dependency | ||
] | ||
"onnx" = ["onnxruntime"] | ||
"onnx-cuda" = ["onnxruntime-gpu"] | ||
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Keeping the
.venv
out of the source dir enables using this image for development on non-Linux systems. E.g. someone on a Mac can mount their INVOKEAI_SRC into the image while avoiding compatibility issues with their local macOS-specific .venv. We should keep it as-isThere was a problem hiding this comment.
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alright, I believe I've resolved this so it's back to using /opt/venv