Dockerfile

# Base image with CUDA support
FROM nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04

# Set environment variables
ENV PYTHONUNBUFFERED=1 \
    DEBIAN_FRONTEND=noninteractive \
    CUDA_HOME=/usr/local/cuda \
    PATH=/usr/local/cuda/bin:$PATH \
    LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    python3 \
    python3-pip \
    python3-dev \
    build-essential \
    curl \
    ffmpeg \
    libsndfile1 \
    && rm -rf /var/lib/apt/lists/*

# Upgrade pip and install build tools
RUN python3 -m pip install --no-cache-dir --upgrade pip setuptools wheel

# Install PyTorch separately first
RUN pip3 install --no-cache-dir \
    torch==2.4.1 \
    torchaudio==2.4.1 \
    torchvision==0.19.1 \
    --extra-index-url https://download.pytorch.org/whl/cu121

# Set up working directory
WORKDIR /app

# Copy requirements first to leverage Docker cache
COPY requirements.txt .

# Modify requirements to remove PyTorch packages
RUN sed -i '/^torch==/d; /^torchaudio==/d; /^torchvision==/d; /^torchdata==/d' requirements.txt

# Install remaining Python dependencies
RUN pip3 install --no-cache-dir -r requirements.txt

# Copy the rest of your application
COPY . .

# Verify GPU setup
RUN python3 -c "import torch; assert torch.cuda.is_available(), 'CUDA not available'"

# Expose port
EXPOSE 8000

# Start the application
CMD ["python3", "server.py"]