978fc08f5a
* Add Dockerfile (cpu/cuda), entrypoint, docs, and a GHCR release workflow Multi-stage Dockerfile with cpu and cuda targets, built from the existing build scripts' cmake invocations. The CUDA target documents and applies the two docker-build-specific gotchas we hit running this in production: CMAKE_CUDA_ARCHITECTURES needs an explicit override for GPUs older than this project's own default arch list when building without GPU device access, and the CUDA driver stub library needs an explicit -L/-lcuda at link time since ggml's VMM pool allocator needs driver-API symbols that aren't present without a real driver. Also installs make/pkg-config/libopenblas-dev (CPU build) and libgomp1 (CUDA runtime), and copies the ggml shared libraries alongside the binaries with LD_LIBRARY_PATH set, since the binaries' baked-in RPATH points at the build-tree location that doesn't exist in the final stage. Adds a GitHub Actions workflow that builds both variants on every push to master and version tag, publishing to ghcr.io/serveurpersocom/qwentts.cpp, and validates the build (without pushing) on PRs that touch the Docker files. Verified end-to-end on a real GTX 1070 (Pascal): both cpu and cuda targets build clean, run, and produce valid synthesized WAV output through tts-server's HTTP API. * Dockerfile: add a vulkan build target (AMD/Intel GPUs) Uses the LunarG Vulkan SDK apt repo for glslc (ggml-vulkan's shader compiler), which Ubuntu 22.04's own repos don't package. Runtime image ships Mesa's Vulkan drivers; NVIDIA users should prefer :cuda instead, since NVIDIA's Vulkan ICD isn't bundled. * docker workflow: bump actions to latest majors (checkout v7, buildx v4, login v4, build-push v7) * Support Pascal (sm_61) in the default CUDA architecture list Adds 61-real to CMAKE_CUDA_ARCHITECTURES' default so Pascal cards (GTX 10-series etc.) work without an explicit override -- including in the Docker cuda target, where docker build has no GPU device to autodetect against in the first place. Real-only (no virtual/PTX): Pascal is now the oldest supported card, so it doesn't need to seed forward JIT compatibility for anything older the way the 75-virtual entry does for 7.5+. Verified end-to-end on a real GTX 1070 (sm_61): built the cuda Docker target with no --build-arg override, ran it with --gpus all, and confirmed via container logs that the GPU loaded the model and served a real synthesis request producing valid WAV output. Adjusts the Dockerfile comments and docs/DOCKER.md accordingly. * docker: pass through --max-prefill-tokens, add ref_text voice cloning - entrypoint.sh: forward MAX_PREFILL_TOKENS to --max-prefill-tokens, matching the existing optional-flag passthrough pattern - entrypoint.sh: a same-stem .txt next to a voice's .wav now supplies ref_text, enabling ICL clone mode instead of the x_vector_only fallback used when no transcript is given - switch voice registration's JSON construction from raw printf to jq for safe escaping of arbitrary transcript text; base64 payloads go through jq's --rawfile (not --arg) since large files blow past ARG_MAX as a command-line argument - add jq to all three runtime image stages (cpu/cuda/vulkan) for the above - docs/DOCKER.md: document both additions Verified end-to-end on a CPU build: both the ref_text and no-ref_text registration paths log correctly (ref_text=yes / ref_text=no) and /health responds after voice registration completes. * docker: opt-in fatal worst-case warmup synthesis ggml_backend_sched grows its compute buffer to fit the largest graph it has ever built and never shrinks it back, so VRAM use can ratchet up the first time a long reply arrives on live traffic. This adds an opt-in startup warmup: when WARMUP_VOICE is set, entrypoint.sh runs one real synthesis capped at WARMUP_MAX_NEW_TOKENS (default 750) frames after voice registration, forcing that worst-case decode/ codec-decode buffer growth to happen at startup instead of mid-request. Complements --max-prefill-tokens, which only covers the input side. Off by default (WARMUP_VOICE unset skips it entirely, matching every other optional flag in this entrypoint), and fatal on failure: if the warmup synthesis fails (most likely OOM), the container kills the server and exits non-zero rather than come up healthy and fail unpredictably later -- a deployment that can't afford its own configured worst case should know that at startup, not on a live request. Verified on a CPU build: warmup runs after voice registration, hits the configured frame cap exactly, and the container stays healthy. --------- Co-authored-by: Gary <gitea@gerasch.dev>
109 lines
5.2 KiB
Docker
109 lines
5.2 KiB
Docker
# syntax=docker/dockerfile:1
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#
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# Build context must have the `ggml` submodule checked out already
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# (`git clone --recurse-submodules`, or `actions/checkout` with
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# `submodules: recursive` in CI) -- this Dockerfile does not fetch it.
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#
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# Usage:
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# docker build --target cpu -t qwentts.cpp:cpu .
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# docker build --target cuda -t qwentts.cpp:cuda .
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# docker build --target vulkan -t qwentts.cpp:vulkan .
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#
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# This project's default distributed arch list covers Pascal (sm_61) to
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# Blackwell. GPUs older than that need an explicit override, since
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# `docker build` has no GPU device to auto-detect against:
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# docker build --target cuda -t qwentts.cpp:cuda \
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# --build-arg CMAKE_CUDA_ARCHITECTURES=50 . # Maxwell
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# See docs/DOCKER.md for details.
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ARG CUDA_BUILD_IMAGE=nvidia/cuda:12.4.1-devel-ubuntu22.04
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ARG CUDA_RUNTIME_IMAGE=nvidia/cuda:12.4.1-runtime-ubuntu22.04
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# ---------------------------------------------------------------- CPU build
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FROM ubuntu:22.04 AS build-cpu
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RUN apt-get update -qq && apt-get install -y -qq --no-install-recommends \
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git ca-certificates cmake g++ make pkg-config libopenblas-dev \
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> /dev/null && rm -rf /var/lib/apt/lists/*
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WORKDIR /build
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COPY . .
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RUN cmake -B build -DGGML_BLAS=ON -DCMAKE_BUILD_TYPE=Release && \
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cmake --build build --config Release -j"$(nproc)"
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FROM ubuntu:22.04 AS cpu
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RUN apt-get update -qq && apt-get install -y -qq --no-install-recommends \
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libgomp1 libopenblas0 curl ca-certificates jq \
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> /dev/null && rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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COPY --from=build-cpu /build/build/tts-server /build/build/qwen-tts /build/build/qwen-codec /build/build/*.so* ./
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COPY docker/entrypoint.sh ./entrypoint.sh
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RUN chmod +x ./entrypoint.sh
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# Binaries are copied out of the build tree their RPATH points at, so the
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# ggml shared libraries (copied alongside, above) need an explicit search path.
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ENV LD_LIBRARY_PATH=/app
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ENTRYPOINT ["./entrypoint.sh"]
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# --------------------------------------------------------------- CUDA build
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FROM ${CUDA_BUILD_IMAGE} AS build-cuda
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ARG CMAKE_CUDA_ARCHITECTURES
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RUN apt-get update -qq && apt-get install -y -qq --no-install-recommends \
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git ca-certificates cmake g++ make \
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> /dev/null && rm -rf /var/lib/apt/lists/*
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WORKDIR /build
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COPY . .
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# `docker build` never has GPU device access, unlike `docker run --gpus`, so:
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# - CMAKE_CUDA_ARCHITECTURES must be set explicitly when targeting a GPU
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# generation outside this project's own default arch list (see
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# docs/DOCKER.md); when unset here, CMake's own project default
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# (Pascal and newer) is used unchanged.
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# - ggml's CUDA VMM pool allocator needs driver-API symbols (cuMemCreate,
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# cuMemMap, ...) at link time. The real libcuda.so isn't present without
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# a GPU, but the devel image ships a link-time-only stub at
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# lib64/stubs/libcuda.so for exactly this case; it isn't on the default
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# linker search path so both -L and -lcuda are needed explicitly.
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RUN cmake -B build -DGGML_CUDA=ON \
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-DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \
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${CMAKE_CUDA_ARCHITECTURES:+-DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES}} \
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-DCMAKE_EXE_LINKER_FLAGS="-L/usr/local/cuda/lib64/stubs -lcuda" \
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-DCMAKE_SHARED_LINKER_FLAGS="-L/usr/local/cuda/lib64/stubs -lcuda" \
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-DCMAKE_BUILD_TYPE=Release && \
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cmake --build build --config Release -j"$(nproc)"
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FROM ${CUDA_RUNTIME_IMAGE} AS cuda
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RUN apt-get update -qq && apt-get install -y -qq --no-install-recommends \
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libgomp1 curl ca-certificates jq \
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> /dev/null && rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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COPY --from=build-cuda /build/build/tts-server /build/build/qwen-tts /build/build/qwen-codec /build/build/*.so* ./
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COPY docker/entrypoint.sh ./entrypoint.sh
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RUN chmod +x ./entrypoint.sh
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ENV LD_LIBRARY_PATH=/app
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ENTRYPOINT ["./entrypoint.sh"]
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# ------------------------------------------------------------- Vulkan build
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# AMD/Intel GPUs (and NVIDIA via its Vulkan ICD). glslc (shader compiler) is
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# only packaged by the LunarG SDK repo on Ubuntu 22.04, not apt's universe.
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FROM ubuntu:22.04 AS build-vulkan
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RUN apt-get update -qq && apt-get install -y -qq --no-install-recommends \
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git ca-certificates cmake g++ make wget gnupg \
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> /dev/null && rm -rf /var/lib/apt/lists/*
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RUN wget -qO- https://packages.lunarg.com/lunarg-signing-key-pub.asc | gpg --dearmor -o /usr/share/keyrings/lunarg.gpg && \
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echo "deb [signed-by=/usr/share/keyrings/lunarg.gpg] https://packages.lunarg.com/vulkan/1.3.296 jammy main" \
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> /etc/apt/sources.list.d/lunarg-vulkan.list && \
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apt-get update -qq && apt-get install -y -qq --no-install-recommends vulkan-sdk \
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> /dev/null && rm -rf /var/lib/apt/lists/*
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WORKDIR /build
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COPY . .
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RUN cmake -B build -DGGML_VULKAN=ON -DCMAKE_BUILD_TYPE=Release && \
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cmake --build build --config Release -j"$(nproc)"
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FROM ubuntu:22.04 AS vulkan
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RUN apt-get update -qq && apt-get install -y -qq --no-install-recommends \
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libgomp1 libvulkan1 mesa-vulkan-drivers curl ca-certificates jq \
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> /dev/null && rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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COPY --from=build-vulkan /build/build/tts-server /build/build/qwen-tts /build/build/qwen-codec /build/build/*.so* ./
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COPY docker/entrypoint.sh ./entrypoint.sh
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RUN chmod +x ./entrypoint.sh
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ENV LD_LIBRARY_PATH=/app
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ENTRYPOINT ["./entrypoint.sh"]
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