Files
VoiceStudio/deploy/Dockerfile
T
99e01610bb feat(docker): publish ROCm/AMD GPU image variant (#1165) (#1166)
The Docker image was CUDA-only, so AMD GPUs (e.g. RX 7900 XTX under
Podman) silently ran on CPU. Every preview and release now also ships a
ROCm variant built from the same Dockerfile:

- deploy/Dockerfile: parameterize the runtime base with a BASE_IMAGE
  build-arg (default unchanged: pytorch/pytorch 2.8.0 CUDA). Add
  PIP/UV_BREAK_SYSTEM_PACKAGES for the ROCm base's PEP-668-marked
  Ubuntu 24.04 Python (no-op on the conda CUDA base), and a build-time
  GPU_FLAVOR guard asserting the dependency install did not clobber the
  base image's GPU torch/torchaudio — a future dep bump that forces a
  torch reinstall now fails the build instead of shipping a CPU-only
  "ROCm" image.
- .github/workflows/docker.yml: new build-and-push-rocm job (separate
  job for runner disk — the ROCm base is ~25 GB unpacked, so it frees
  the preinstalled toolchains first). Tags mirror the CUDA semantics
  with a -rocm suffix (:rocm rolling preview, :stable-rocm, :X.Y.Z-rocm,
  :X.Y-rocm, :sha-xxxx-rocm) on both GHCR and Docker Hub, same secret
  gating. flavor latest=false so release tags can't clobber :latest.
  No cache-to: the ROCm layers would blow the 10 GB GHA cache budget.
- deploy/docker-compose.yml: new opt-in 'rocm' profile passing the GPU
  through via /dev/kfd + /dev/dri, with HSA_OVERRIDE_GFX_VERSION=11.0.0
  documented (user-set, not baked in — backend auto-sets it for known
  consumer GFX IDs).
- Docs-sync: docker.md (ROCm quick start incl. Podman/Quadlet, tag
  table, troubleshooting), dockerhub-overview.md, README AMD note,
  linux.md ROCm section cross-link, CHANGELOG [Unreleased].

Base image: rocm/pytorch:rocm7.2.4_ubuntu24.04_py3.12_pytorch_release_2.8.0
— torch 2.8.0 exactly matches the CUDA image (identical resolution, so
uv keeps it), py3.12 satisfies requires-python >=3.11 (the ubuntu22.04
variants are py3.10 and do not).

Closes #1165

Co-authored-by: mergetest <nizam4103@gmail.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-07-16 15:34:28 +05:30

116 lines
5.0 KiB
Docker

# Base image for the Python/PyTorch runtime stage. The default builds the
# CUDA variant; CI also builds a ROCm/AMD variant (issue #1165) by overriding:
# BASE_IMAGE=rocm/pytorch:rocm7.2.4_ubuntu24.04_py3.12_pytorch_release_2.8.0
# GPU_FLAVOR=rocm
# Both bases ship torch/torchaudio 2.8.0 preinstalled; the dependency install
# below deliberately preserves them (see the GPU_FLAVOR guard).
ARG BASE_IMAGE=pytorch/pytorch:2.8.0-cuda12.8-cudnn9-runtime
# ==========================================
# Builder Stage: Compile React Frontend
# ==========================================
FROM oven/bun:1-alpine AS frontend-builder
WORKDIR /app
# Monorepo — bun workspace with lockfile at repo root. Copy manifests first
# so `bun install` caches independently of source edits.
COPY package.json bun.lock ./
COPY frontend/package.json ./frontend/
RUN bun install --frozen-lockfile
# Build static files (output lands in /app/frontend/dist)
COPY frontend/ ./frontend/
RUN bun run --cwd frontend build
# ==========================================
# Runtime Stage: Python & PyTorch Backend
# ==========================================
FROM ${BASE_IMAGE} AS runtime
WORKDIR /app
# Enable unbuffered logs and optimizations
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
ENV UV_SYSTEM_PYTHON=1
ENV HF_HOME=/app/omnivoice_data/huggingface
# Allow bare imports (from core.config, from services.*, etc.) when
# uvicorn is started as `backend.main:app` from WORKDIR /app.
ENV PYTHONPATH=/app/backend
# Headless server deployment: relax the desktop-only loopback origin gate.
# Docker's network NAT rewrites the client host to the bridge gateway, so the
# gate would otherwise 403 the operator out of /system/* and /api/settings/*
# ("Loopback origin required", issue #261). Exposure is governed by the
# operator's `-p` port mapping plus the optional share PIN. Desktop builds
# never set this, so their loopback boundary is unchanged.
ENV OMNIVOICE_SERVER_MODE=1
# Install system dependencies (FFmpeg is critical for torchaudio/scene splitting)
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
ffmpeg \
libsndfile1 \
curl \
&& rm -rf /var/lib/apt/lists/*
# PEP 668: the ROCm base (Ubuntu 24.04) marks its system Python
# EXTERNALLY-MANAGED, which would refuse `pip install` / `uv pip install
# --system`. Inside a single-purpose container image installing into the
# base env is exactly what we want. No-ops on the conda-based CUDA image.
ENV PIP_BREAK_SYSTEM_PACKAGES=1
ENV UV_BREAK_SYSTEM_PACKAGES=1
# Install `uv` for blazing-fast reliable pip resolution
# (`python3 -m pip` — not every base symlinks a bare `pip` onto PATH)
RUN python3 -m pip install --no-cache-dir uv
# Copy python packaging specs (README.md required by hatchling metadata)
COPY pyproject.toml uv.lock README.md ./
# Install the project (non-editable — no need for -e in containers).
# Uses `uv` for exponentially faster resolution than plain pip.
#
# NOTE: `uv pip install` (without --upgrade) keeps already-installed packages
# that satisfy the requirements, so the base image's GPU-built torch/torchaudio
# (2.8.0, satisfying our `torch>=2.4`) survive this step instead of being
# clobbered by PyPI's CUDA-default wheels. That property is what makes the
# ROCm variant possible at all — the guard below pins it down.
RUN uv pip install --system --no-cache .
# Guard (fails the build, not the user at runtime): assert the dependency
# install did NOT replace the base image's GPU torch. A future dep bump that
# forces a different torch version would otherwise silently ship a CUDA build
# in the ROCm image (= CPU-only for AMD users) — catch it here instead.
ARG GPU_FLAVOR=cuda
RUN python3 -c "import os, torch, torchaudio; \
flavor = os.environ['GPU_FLAVOR']; \
accel = torch.version.hip if flavor == 'rocm' else torch.version.cuda; \
print(f'torch={torch.__version__} torchaudio={torchaudio.__version__} {flavor}={accel}'); \
assert accel, f'base image {flavor} torch was clobbered (now {torch.__version__})'"
# Copy application source
COPY backend/ ./backend/
COPY omnivoice/ ./omnivoice/
# Alembic config so schema migrations run natively on existing volumes
# (without it the backend fell back to the additive-column self-heal —
# functional, but the real migration chain is the first-class path).
COPY alembic.ini ./
# Copy the pre-built React frontend from the builder stage
COPY --from=frontend-builder /app/frontend/dist ./frontend/dist
# Expose the single unified API and UI port
EXPOSE 3900
# Image-level health probe (compose files define their own; this covers plain
# `docker run`). Generous start period: first boot creates the venv-less
# schema + may pull model metadata before /health answers.
HEALTHCHECK --interval=30s --timeout=5s --start-period=120s --retries=5 \
CMD curl -fsS http://127.0.0.1:3900/health || exit 1
# Mount points for persistent data (sqlite db, user voices, huggingface cache)
VOLUME ["/app/omnivoice_data"]
# Bind to 0.0.0.0 for external access
ENTRYPOINT ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "3900"]