mirror of
https://github.com/oqyude/nixos.git
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68 lines
2.9 KiB
Docker
68 lines
2.9 KiB
Docker
# Fully qualified on purpose: NixOS ships a podman registries.conf without
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# unqualified-search-registries, so a bare "python:3.12-slim-bookworm" fails to
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# resolve before the build even starts.
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FROM docker.io/library/python:3.12-slim-bookworm
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# Pinned, not "main": a rebuild that only touched the Nix module must not
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# silently pick up different weights. Bump these deliberately.
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ARG KOKORO_RU_REPO=zaakirio/kokoro-ru
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ARG KOKORO_RU_REVISION=d649c57b239b18c4c384378127cbf01dba039bc1
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# Trim to "sveta" to halve the image: masha shares her checkpoint and dima is
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# a second 327 MB one.
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ARG KOKORO_RU_VOICES=sveta,masha,dima
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# Thread counts, not a guess: see app.py THREADS. 12 was the measured plateau on
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# a 24-logical-core host, and 24 was ~2x worse. Must stay equal to the Nix
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# module's environment.environment, which wins over this ENV.
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ENV PYTHONUNBUFFERED=1 \
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PIP_NO_CACHE_DIR=1 \
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PIP_DISABLE_PIP_VERSION_CHECK=1 \
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HF_HUB_DISABLE_TELEMETRY=1 \
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HF_HUB_DISABLE_SYMLINKS_WARNING=1 \
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KOKORO_RU_REPO=${KOKORO_RU_REPO} \
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KOKORO_RU_REVISION=${KOKORO_RU_REVISION} \
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KOKORO_RU_VOICES=${KOKORO_RU_VOICES} \
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KOKORO_MODEL_DIR=/app/kokoro-ru \
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KOKORO_THREADS=12 \
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OMP_NUM_THREADS=12 \
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MKL_NUM_THREADS=12 \
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TZ=Europe/Moscow
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WORKDIR /app
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# libgomp1 is torch's OpenMP runtime. espeak-ng comes from the espeakng-loader
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# wheel rather than the distro package because the model needs its own
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# recompiled ru_dict, and libsndfile is absent because WAV/PCM are written with
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# stdlib `wave` while every other format goes through imageio-ffmpeg.
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RUN apt-get update \
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&& apt-get install -y --no-install-recommends libgomp1 \
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&& rm -rf /var/lib/apt/lists/*
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# CPU-only torch from its own index: the default PyPI wheel drags in ~2.5 GB of
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# CUDA libraries for a machine that has no GPU.
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RUN pip install --index-url https://download.pytorch.org/whl/cpu torch
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COPY requirements.txt ./
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RUN pip install -r requirements.txt
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# fetch_assets.py is copied on its own and app.py only after the snapshot, never
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# as one COPY. A single COPY would tie the 639 MB download to the application
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# source: any edit to app.py would invalidate this layer and refetch every
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# checkpoint as hundreds of anonymous, rate-limited requests.
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COPY fetch_assets.py ./
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# Bakes the checkpoints, the acute-aware espeak data and ruaccent's ONNX models
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# into the layer, which is what lets the container start with no network and no
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# writable volume.
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RUN python fetch_assets.py
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COPY app.py ./
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EXPOSE 8000
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HEALTHCHECK --interval=30s --timeout=5s --start-period=180s --retries=3 \
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CMD ["python", "-c", "import urllib.request; urllib.request.urlopen('http://127.0.0.1:8000/healthz', timeout=4)"]
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# No workers: the model is a shared in-process singleton, so a second worker
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# would only mean a second copy of ~2 GB of weights.
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "1"] |