Benoit
6a55de3299
Architecture en 2 images: - Image base (audio-classifier-base): deps système + Python (~15min, 1x/semaine) - Image app (audio-classifier-backend): code uniquement (~30s-2min, chaque commit) Fichiers ajoutés: - backend/Dockerfile.base: Image de base avec toutes les dépendances - .gitea/workflows/docker-base.yml: CI pour build de l'image de base - backend/DOCKER_BUILD.md: Documentation complète Fichiers modifiés: - backend/Dockerfile: Utilise l'image de base (FROM audio-classifier-base) - .gitea/workflows/docker.yml: Passe BASE_IMAGE en build-arg Gains de performance: - Build normal: 15-25min → 30s-2min (90-95% plus rapide) - Trigger auto du build base: quand requirements.txt change - Trigger manuel: via interface Gitea Actions 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
60 lines
1.7 KiB
Docker
60 lines
1.7 KiB
Docker
# Base image for Audio Classifier Backend
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# This image contains all system dependencies and Python packages
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# Build this image only when dependencies change (requirements.txt updates)
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# Use amd64 platform for better Essentia compatibility
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FROM --platform=linux/amd64 python:3.9-slim
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LABEL maintainer="benoit.schw@gmail.com"
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LABEL description="Base image with all dependencies for Audio Classifier Backend"
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# Install system dependencies
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RUN apt-get update && apt-get install -y \
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ffmpeg \
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libsndfile1 \
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libsndfile1-dev \
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gcc \
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g++ \
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gfortran \
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libopenblas-dev \
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liblapack-dev \
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pkg-config \
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curl \
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build-essential \
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libyaml-dev \
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libfftw3-dev \
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libavcodec-dev \
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libavformat-dev \
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libavutil-dev \
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libswresample-dev \
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libsamplerate0-dev \
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libtag1-dev \
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libchromaprint-dev \
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&& rm -rf /var/lib/apt/lists/*
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# Set working directory
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WORKDIR /app
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# Upgrade pip, setuptools, wheel
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RUN pip install --no-cache-dir --upgrade pip setuptools wheel
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# Copy requirements
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COPY requirements.txt .
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# Install Python dependencies in stages for better caching
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# Using versions compatible with Python 3.9
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RUN pip install --no-cache-dir numpy==1.24.3
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RUN pip install --no-cache-dir scipy==1.11.4
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# Install Essentia-TensorFlow - Python 3.9 AMD64 support
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RUN pip install --no-cache-dir essentia-tensorflow
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# Install remaining dependencies
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RUN pip install --no-cache-dir -r requirements.txt
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# Verify installations
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RUN python -c "import essentia.standard; import numpy; import scipy; import fastapi; print('All dependencies installed successfully')"
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# This image is meant to be used as a base
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# The application code will be copied in the derived Dockerfile
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