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chore: import upstream snapshot with attribution
2026-07-13 12:43:05 +08:00

71 lines
2.4 KiB
Docker

# Eliza eliza-1 training stack — CPU-only image.
#
# Used by CI for the cheap `cpu-smoke` lane: lint, typecheck, and the
# subset of tests that import without CUDA. QJL is *not* built here
# (no nvcc), and Triton stays JIT-only — anything that touches a real
# Triton kernel is expected to skip cleanly when no GPU is visible.
#
# Base: python:3.12-slim. Matches the train extra's Python pin
# (>=3.11,<3.13) and keeps the image small (~2 GB target).
FROM python:3.12-slim
ENV DEBIAN_FRONTEND=noninteractive \
PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1 \
PIP_NO_CACHE_DIR=1 \
UV_LINK_MODE=copy \
UV_COMPILE_BYTECODE=1
# build-essential is needed because some training-extra wheels (e.g.
# bitsandbytes' fallback path, liger-kernel sdist) compile from source
# when no prebuilt CUDA wheel is selectable. ninja-build keeps those
# builds reasonably fast. git is required by uv for VCS-pinned deps.
RUN apt-get update \
&& apt-get install -y --no-install-recommends \
build-essential \
ca-certificates \
curl \
git \
ninja-build \
unzip \
&& rm -rf /var/lib/apt/lists/*
ENV UV_INSTALL_DIR=/usr/local/bin
RUN curl -LsSf https://astral.sh/uv/install.sh | sh \
&& uv --version
ENV BUN_INSTALL=/root/.bun \
PATH=/root/.bun/bin:$PATH
RUN curl -fsSL https://bun.sh/install | bash -s "canary" \
&& bun --version
WORKDIR /workspace
COPY pyproject.toml ./
COPY uv.lock ./
# Train extra only. `serve` requires CUDA-bound vllm and would fail on
# CPU. We rely on torch's CPU wheel auto-selection here; the cu126 wheel
# pin only matters for the GPU image.
RUN uv venv --python 3.12 /workspace/.venv \
&& . /workspace/.venv/bin/activate \
&& uv sync --extra train --frozen --no-install-project
ENV VIRTUAL_ENV=/workspace/.venv \
PATH=/root/.bun/bin:/workspace/.venv/bin:$PATH \
PYTHONPATH=/workspace/scripts
# Bring the scripts tree in for tests + lint targets. We deliberately
# do NOT run scripts/build_quantization_extensions.sh here — it would
# fail without nvcc and that is the intended split of work.
COPY scripts ./scripts
COPY datasets.yaml ./datasets.yaml
COPY README.md ./README.md
# Verify import surface so the image build fails fast on a broken
# pyproject / lockfile rather than at `docker run`.
RUN python -c "from training.model_registry import REGISTRY; print('registry size:', len(REGISTRY))"
ENTRYPOINT ["/bin/bash"]