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Niko1221
afc33c260e
- The ready-made engine: with the default --prebuilt, setup looks at releases/download/v<version>/ first (the version CMakeLists.txt says, source_version()) and falls back to releases/latest with a message when that release is not published (yet). An explicit --prebuilt / STRATA_PREBUILT_URL is used as before; an installed engine new enough is kept without asking the network. - Hugging Face: every model family's shards and vision encoder come from a fixed commit (HF_REVISIONS, the repos' `sha` on 2026-09-30), tools/mtp_fetch.py reads the BF16 checkpoint at its commit (STRATA_MTP_REVISION overrides). A revision a repository no longer has falls back to its current files, with a message. Files already downloaded keep their names and .done marks, so nothing is downloaded again. - Python: requirements.txt pins the packages and their dependencies (numpy per Python range, 3.10+), used by setup's pip step and the Dockerfile. A venv set up before this (bare names in .strata-pip.json) is left as it is; only packages it never had are installed, pinned. Tests: tools/test_setup_pins.py (mocked network: the tag URL first, latest as the fallback, an installed engine kept, a gone revision falling back to main in download() and mtp_fetch, the pip step for fresh, old and bumped installs).
116 lines
5.4 KiB
Docker
116 lines
5.4 KiB
Docker
# syntax=docker/dockerfile:1
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#
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# Strata: Qwen3.8-Flash-Next on NVIDIA GPUs (RTX 30/40/50, 12+ GB VRAM; two or
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# three cards can share one model, 8 GB each - docs/MULTI_GPU.md).
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#
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# The engine is compiled during docker build, so the first container start only
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# downloads the model (~70 GB) and starts the server. docker build has no GPU,
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# so the CUDA architectures are fixed here instead of read from nvidia-smi: the
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# engine is a fat binary with a cubin per listed arch, and the runtime picks the
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# one matching your card. Narrow CUDA_ARCHITECTURES to your card for a faster
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# build; a card outside the set needs a rebuild with its own arch.
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#
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# Build:
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# docker build -t strata .
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# docker build -t strata --build-arg CUDA_ARCHITECTURES=89 . # RTX 40 only
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#
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# Run (host needs an NVIDIA driver >= 580 and nvidia-container-toolkit):
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# docker run --rm --gpus all \
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# -p 8080:8080 \
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# --ulimit memlock=-1 \
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# -v strata-data:/data \
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# -e MODEL=IQ2_XS \
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# strata
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#
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# Setup choices are env vars, read by docker-entrypoint.sh: FAMILY, MODEL, CONTEXT,
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# VISION (no | yes | cpu), KV (int8 | q4_0 | k8v4), GPU (one card) or GPUS ("0,2"
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# or "all", with LAYER_SPLIT), LOW_RAM (auto | on | off), HOST, PORT, API_KEY.
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#
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# Only the model files, the prepared pack, the MTP layer and the install config
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# live in the /data volume; the engine is part of the image. Strata loads 32-62 GB
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# into RAM, so a capped container needs -e LOW_RAM=on: setup.py reads the RAM from
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# /proc/meminfo, which here is the host's total, not the container's limit. Add an
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# API key before exposing the port to a network: -e API_KEY=<secret>. Pass
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# -e REINSTALL=1 to change the model settings later.
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#
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# --gpus all on a host with two usable cards: setup takes both (the layer split is
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# its recommended default). Pin one card with -e GPU=0, or name them with
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# -e GPUS=0,2. A volume set up for one card switches to the pair on its first start
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# on a two-card host unless GPU or GPUS pins it. LOW_RAM=on runs on one card.
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FROM nvidia/cuda:13.0.0-devel-ubuntu24.04
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# STRATA_EXECV=1: setup.py replaces itself with the server, so the server is PID 1
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# and docker stop's SIGTERM reaches it (see setup.start). Normal Linux starts, which
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# don't set it, keep spawning the server as a child.
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ENV DEBIAN_FRONTEND=noninteractive PYTHONUNBUFFERED=1 LANG=C.UTF-8 STRATA_EXECV=1
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RUN apt-get update && apt-get install -y --no-install-recommends \
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build-essential ca-certificates curl git libatomic1 libgomp1 \
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python3 python3-pip python3-venv unzip \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /opt/strata
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COPY . .
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# RTX 20 (75), RTX 30 (86), RTX 40 (89), RTX 50 (120), plus 80 for A-series. CMakeLists
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# refuses anything below 75. BUILD_VISION=0 skips the image encoder build.
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ARG CUDA_ARCHITECTURES=75;80;86;89;120
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ARG BUILD_VISION=1
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RUN python3 -m venv .venv \
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&& .venv/bin/pip install --no-cache-dir --upgrade pip \
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&& .venv/bin/pip install --no-cache-dir -r requirements.txt \
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&& chmod +x setup.sh docker-entrypoint.sh
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# llama.cpp at the pinned commit, then the engine and the image encoder, built
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# exactly the way setup.py builds them. BUILD.json is what setup.py reads to
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# decide whether an engine is current: source=local with a matching src hash
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# means the first start reuses it instead of recompiling.
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RUN .venv/bin/python - <<'PYEOF'
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import json, os, pathlib, shutil
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import setup
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llama = setup.get_llama_cpp()
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nvcc, _ = setup.find_nvcc()
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arch = os.environ.get("CUDA_ARCHITECTURES", "75;80;86;89;120").strip().strip('"').replace(",", ";")
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vision = "gpu" if os.environ.get("BUILD_VISION", "1") == "1" else "none"
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setup.cmake_build(setup.ROOT, setup.ROOT / "build", "strata",
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["-DSTRATA_ENABLE_CUDA=ON", "-DSTRATA_BUILD_TESTS=OFF",
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f"-DCMAKE_CUDA_ARCHITECTURES={arch}", f"-DCMAKE_CUDA_COMPILER={nvcc}",
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f"-DSTRATA_GGML_DIR={llama}"], None, "build-strata.bat")
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if vision != "none":
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setup.cmake_build(setup.ROOT / "tools" / "vision", setup.ROOT / "build-vision", "strata-vision",
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[f"-DLLAMA_DIR={llama}", "-DSTRATA_VISION_CUDA=ON",
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f"-DCMAKE_CUDA_ARCHITECTURES={arch}", f"-DCMAKE_CUDA_COMPILER={nvcc}"], None, "build-vision.bat")
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eng = setup.ROOT / "engine"
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eng.mkdir(exist_ok=True)
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shutil.copy2(setup.ROOT / "build" / setup.EXE, eng / setup.EXE)
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if vision != "none":
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shutil.copy2(setup.ROOT / "build-vision" / "bin" / setup.VEXE, eng / setup.VEXE)
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bindir = pathlib.Path(nvcc).parent
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meta = {"source": "local", "version": setup.source_version(),
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"archs": [int(a.split("-")[0]) for a in arch.split(";") if a.split("-")[0].isdigit()], "vision": vision,
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"cuda_dirs": [str(d) for d in (bindir, bindir / "x64", bindir.parent / "lib64") if d.is_dir()],
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"src": setup.source_hash(setup.ENGINE_SOURCES),
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"vision_src": setup.source_hash(setup.VISION_SOURCES) if vision != "none" else None}
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(eng / "BUILD.json").write_text(json.dumps(meta, indent=1))
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PYEOF
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# the cmake trees are build-time only; the engine itself is what the container needs
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RUN rm -rf build build-vision
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VOLUME ["/data"]
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EXPOSE 8080
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# /health is answered before the API key gate, so it works with or without one.
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# The port only opens after the model loads (1-3 minutes, longer on a first run),
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# so the start period is generous: a too short one marks a still-loading container
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# unhealthy and a restart policy would kill it mid-download.
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HEALTHCHECK --interval=30s --timeout=5s --start-period=600s --retries=3 \
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CMD curl -fs "http://127.0.0.1:${PORT:-8080}/health" || exit 1
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ENTRYPOINT ["./docker-entrypoint.sh"]
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