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>
8 lines
49 B
Plaintext
8 lines
49 B
Plaintext
.git
|
|
build
|
|
checkpoints
|
|
models
|
|
docs
|
|
examples
|
|
*.md
|