# fish-cpp — s2.cpp (pure C++/GGML inference for Fish s2-pro GGUFs) +
# tiny FastAPI shim exposing Fish's /v1/tts contract.
#
# Two-stage build:
#   1. builder — compiles s2.cpp with CUDA backend
#   2. runtime — slim image with the s2 binary + Python shim
#
# The s2.cpp binary is the actual inference engine; the Python shim is
# just an HTTP-to-CLI bridge so this stack drops into the same fleet
# pattern as the other TTS (POST /v1/tts, Fish-shaped request body).

# ── Stage 1: build s2.cpp with CUDA ────────────────────────────────────
FROM nvidia/cuda:12.6.0-devel-ubuntu24.04 AS builder

ARG S2_CPP_SHA=e48ce8e02d8335bd9a0ba94679f605724b31d123
ENV DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y --no-install-recommends \
      git ca-certificates cmake ninja-build build-essential pkg-config \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /src
RUN git clone --recurse-submodules https://github.com/rodrigomatta/s2.cpp.git \
    && cd s2.cpp \
    && git checkout ${S2_CPP_SHA} \
    && git submodule update --init --recursive

WORKDIR /src/s2.cpp
# CUDA Driver API symbols (cuMemSetAccess, cuDeviceGet, etc.) live in
# libcuda.so which the NVIDIA driver provides at RUNTIME. Build-time
# uses the stubs library at /usr/local/cuda/lib64/stubs/. CMake's
# find_library doesn't reliably pick it up via CMAKE_LIBRARY_PATH for
# nested ggml-cuda builds — the most robust fix is symlinking the stub
# into /usr/local/lib (which ld checks unconditionally) AND providing
# libcuda.so.1 (the SONAME ggml-cuda links against). The symlinks live
# only in this build stage; the runtime image gets the real driver-
# provided libcuda.so.1 via NVIDIA's container runtime.
RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/lib/libcuda.so \
    && ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/lib/libcuda.so.1 \
    && ldconfig
RUN cmake -G Ninja -B build -DCMAKE_BUILD_TYPE=Release -DS2_CUDA=ON \
    && cmake --build build --parallel $(nproc) --target s2

# ── Stage 2: runtime — slim image with the binary + python shim ────────
FROM nvidia/cuda:12.6.0-runtime-ubuntu24.04

ENV DEBIAN_FRONTEND=noninteractive \
    PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1

RUN apt-get update && apt-get install -y --no-install-recommends \
      python3 python3-pip python3-venv tini \
      libgomp1 curl \
    && rm -rf /var/lib/apt/lists/*
# libgomp1 = GNU OpenMP runtime — required by the s2 binary.
# curl    = entrypoint uses it to wait for s2 server to bind :3030.

# Pull the shim deps into an isolated venv so we don't fight system pip.
RUN python3 -m venv /opt/venv
ENV PATH="/opt/venv/bin:${PATH}"
RUN pip install --no-cache-dir 'fastapi>=0.115' 'uvicorn[standard]>=0.30' 'pydantic>=2' 'httpx>=0.27'

# Copy the s2 binary + GGML runtime libs from the builder stage.
COPY --from=builder /src/s2.cpp/build/s2 /usr/local/bin/s2
COPY --from=builder /src/s2.cpp/build/ggml/src/libggml*.so /usr/local/lib/
COPY --from=builder /src/s2.cpp/build/ggml/src/ggml-cuda/libggml-cuda.so /usr/local/lib/
RUN ldconfig

WORKDIR /app
COPY server.py /app/server.py
COPY entrypoint.sh /app/entrypoint.sh
RUN chmod +x /app/entrypoint.sh

# Bind-mounted at runtime: weights at /weights, references at /references.
VOLUME /weights
VOLUME /references

EXPOSE 8000
# tini supervises the entrypoint script which manages both s2 server
# (bound to localhost:3030, model resident) and the uvicorn shim
# (bound to 0.0.0.0:8000, proxies /v1/tts → s2's /generate).
ENTRYPOINT ["/usr/bin/tini", "--"]
CMD ["/app/entrypoint.sh"]