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>
153 lines
6.2 KiB
YAML
153 lines
6.2 KiB
YAML
# ──────────────────────────────────────────────────────────────
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# OmniVoice Studio — Docker Compose
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#
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# Quick start:
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# docker compose -f deploy/docker-compose.yml --profile cpu up # CPU mode
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# docker compose -f deploy/docker-compose.yml --profile gpu up # NVIDIA GPU
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# docker compose -f deploy/docker-compose.yml --profile rocm up # AMD GPU (ROCm)
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#
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# All services bind to port 3900, so they MUST be opt-in via profiles —
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# otherwise `compose up` would race them and one would fail to bind.
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#
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# First run downloads ~4 GB of models. Progress is shown in logs.
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# Open http://localhost:3900 once the health check passes.
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#
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# SECURITY: The port is bound to 127.0.0.1 by default — only this
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# machine can reach the API. To expose OmniVoice on your LAN (or
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# through a reverse proxy / tunnel), change the port mapping to
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# "0.0.0.0:3900:3900" or "3900:3900". OmniVoice itself ships no
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# authentication — if you expose it, put it behind a reverse proxy
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# with auth (Caddy basic_auth, nginx + htpasswd, Tailscale, etc.).
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# ──────────────────────────────────────────────────────────────
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services:
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# ── CPU mode — activate with: docker compose --profile cpu up
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omnivoice:
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image: ghcr.io/debpalash/omnivoice-studio:latest
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# To build from source instead of pulling, comment out `image:` and
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# uncomment the two lines below:
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build:
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context: ..
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dockerfile: deploy/Dockerfile
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container_name: omnivoice-studio
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profiles: ["cpu"]
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ports:
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- "127.0.0.1:3900:3900"
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volumes:
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- omnivoice-data:/app/omnivoice_data
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environment:
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- HF_HOME=/app/omnivoice_data/huggingface
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- HF_TOKEN=${HF_TOKEN:-}
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- OMNIVOICE_DATA_DIR=/app/omnivoice_data
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- PYTHONPATH=/app/backend
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- PYTHONUNBUFFERED=1
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# Bind uvicorn to 0.0.0.0 *inside* the container so the host-side port
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# mapping above can forward traffic in. The 127.0.0.1 prefix on the
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# `ports:` mapping is what enforces loopback-only on the host —
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# OMNIVOICE_BIND_HOST=0.0.0.0 here only opens the container's own
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# interface. The backend default is 127.0.0.1 (see backend/main.py).
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- OMNIVOICE_BIND_HOST=0.0.0.0
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# Headless server: relax the desktop-only loopback origin gate so the
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# web UI's /system/* and /api/settings/* routes work through Docker's
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# NAT (issue #261). Already baked into the image; shown here so it's
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# discoverable. If you front the container with your own auth proxy on
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# loopback, set this to 0 to re-enable the strict gate.
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- OMNIVOICE_SERVER_MODE=1
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healthcheck:
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test: ["CMD", "curl", "-sf", "http://localhost:3900/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 120s
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restart: unless-stopped
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# ── GPU mode — activate with: docker compose --profile gpu up
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omnivoice-gpu:
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image: ghcr.io/debpalash/omnivoice-studio:latest
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build:
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context: ..
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dockerfile: deploy/Dockerfile
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container_name: omnivoice-studio-gpu
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profiles: ["gpu"]
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ports:
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- "127.0.0.1:3900:3900"
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volumes:
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- omnivoice-data:/app/omnivoice_data
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environment:
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- HF_HOME=/app/omnivoice_data/huggingface
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- HF_TOKEN=${HF_TOKEN:-}
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- OMNIVOICE_DATA_DIR=/app/omnivoice_data
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- PYTHONPATH=/app/backend
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- PYTHONUNBUFFERED=1
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# Bind uvicorn to 0.0.0.0 *inside* the container — same as the CPU
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# service above. The host-side `127.0.0.1:3900:3900` mapping keeps
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# LAN reachability off by default.
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- OMNIVOICE_BIND_HOST=0.0.0.0
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# See the CPU service above — relaxes the loopback origin gate for the
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# headless Docker deployment (issue #261). Set to 0 to re-enable it.
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- OMNIVOICE_SERVER_MODE=1
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healthcheck:
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test: ["CMD", "curl", "-sf", "http://localhost:3900/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 180s
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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restart: unless-stopped
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# ── AMD GPU (ROCm) mode — activate with: docker compose --profile rocm up
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# Uses the dedicated `:rocm` image variant (#1165). The GPU is passed
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# through as plain device nodes — no toolkit needed, the host only needs
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# the amdgpu kernel driver (the ROCm userspace ships inside the image).
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# Podman works with the same two --device flags (Quadlet: AddDevice=).
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omnivoice-rocm:
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image: ghcr.io/debpalash/omnivoice-studio:rocm
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# To build from source instead of pulling, comment out `image:` and
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# uncomment the lines below. BASE_IMAGE/GPU_FLAVOR are required — the
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# Dockerfile's defaults build the CUDA variant.
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# build:
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# context: ..
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# dockerfile: deploy/Dockerfile
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# args:
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# BASE_IMAGE: rocm/pytorch:rocm7.2.4_ubuntu24.04_py3.12_pytorch_release_2.8.0
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# GPU_FLAVOR: rocm
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container_name: omnivoice-studio-rocm
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profiles: ["rocm"]
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ports:
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- "127.0.0.1:3900:3900"
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devices:
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- /dev/kfd
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- /dev/dri
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volumes:
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- omnivoice-data:/app/omnivoice_data
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environment:
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- HF_HOME=/app/omnivoice_data/huggingface
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- HF_TOKEN=${HF_TOKEN:-}
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- OMNIVOICE_DATA_DIR=/app/omnivoice_data
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- PYTHONPATH=/app/backend
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- PYTHONUNBUFFERED=1
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# See the CPU service above — container-internal bind + relaxed
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# loopback origin gate for the headless Docker deployment.
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- OMNIVOICE_BIND_HOST=0.0.0.0
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- OMNIVOICE_SERVER_MODE=1
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# RDNA3 consumer cards (RX 7900 XTX/XT and friends, gfx1100): if the
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# GPU is not detected, uncomment the override below. The backend
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# auto-sets it for known consumer GFX IDs, so try without it first.
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# - HSA_OVERRIDE_GFX_VERSION=11.0.0
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healthcheck:
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test: ["CMD", "curl", "-sf", "http://localhost:3900/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 180s
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restart: unless-stopped
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volumes:
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omnivoice-data:
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