ColorWave/DockerFile at main · sharma-0311/ColorWave · GitHub

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# Production-Ready Dockerfile for SAR Image Colorization
# Base image with CUDA 11.8 support
FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu20.04

# Set environment variables
ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
ENV CUDA_VISIBLE_DEVICES=0
ENV PYTHONPATH=/app:$PYTHONPATH
ENV PATH=/home/appuser/.local/bin:$PATH

# Install system dependencies
RUN apt-get update && apt-get install -y \
    python3 \
    python3-pip \
    python3-dev \
    build-essential \
    cmake \
    git \
    wget \
    curl \
    libgdal-dev \
    gdal-bin \
    libproj-dev \
    libgeos-dev \
    libspatialite-dev \
    libhdf5-dev \
    libnetcdf-dev \
    libopenjp2-7-dev \
    libtiff5-dev \
    libjpeg-dev \
    libpng-dev \
    libwebp-dev \
    libx264-dev \
    libx265-dev \
    libvpx-dev \
    libavcodec-dev \
    libavformat-dev \
    libavutil-dev \
    libswscale-dev \
    libavresample-dev \
    libavfilter-dev \
    libavdevice-dev \
    libpostproc-dev \
    libswresample-dev \
    libfreetype6-dev \
    libfontconfig1-dev \
    libxrender1 \
    libgl1-mesa-glx \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    libgomp1 \
    && rm -rf /var/lib/apt/lists/*

# Create symbolic link for python
RUN ln -s /usr/bin/python3 /usr/bin/python

# Upgrade pip
RUN pip install --upgrade pip setuptools wheel

# Install Python dependencies
COPY requirements.txt /tmp/requirements.txt
RUN pip install --no-cache-dir -r /tmp/requirements.txt

# Install additional scientific packages
RUN pip install --no-cache-dir \
    opencv-python-headless \
    rasterio \
    geopandas \
    fiona \
    shapely \
    pyproj \
    cartopy \
    earthpy \
    folium \
    plotly \
    dash \
    dash-bootstrap-components

# Install PyTorch with CUDA support
RUN pip install --no-cache-dir \
    torch==1.13.1+cu118 \
    torchvision==0.14.1+cu118 \
    torchaudio==0.13.1+cu118 \
    --extra-index-url https://download.pytorch.org/whl/cu118

# Install additional ML packages
RUN pip install --no-cache-dir \
    lpips \
    kornia \
    albumentations \
    timm \
    efficientnet-pytorch \
    pretrainedmodels \
    segmentation-models-pytorch

# Create application directory
WORKDIR /app

# Copy application code
COPY . /app/

# Create necessary directories
RUN mkdir -p /app/data/raw /app/data/processed /app/experiments/logs /app/experiments/checkpoints /app/experiments/outputs

# Set permissions
RUN chmod -R 755 /app

# Create non-root user
RUN useradd -m -u 1000 appuser && \
    chown -R appuser:appuser /app
USER appuser

# Expose ports
EXPOSE 8501 8000

# Health check (Streamlit root responds with 200 when ready)
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD curl -f http://localhost:8501/ || exit 1

# Default command
CMD ["streamlit", "run", "webapp/app.py", "--server.port=8501", "--server.address=0.0.0.0"]