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FROM nvidia/cuda:12.1.1-devel-ubuntu22.04
# Set environment variables
ENV PYTHONUNBUFFERED=1 \
PYTHONDONTWRITEBYTECODE=1 \
DEBIAN_FRONTEND=noninteractive
# Install system dependencies
RUN apt-get update && apt-get install -y \
build-essential \
python3-pip \
python3-dev \
git \
wget \
libcairo2-dev \
pkg-config \
libgl1 \
libglib2.0-0 \
libsm6 \
libxrender1 \
libxext6 \
ffmpeg \
&& rm -rf /var/lib/apt/lists/*
RUN useradd -m -u 1000 user
USER user
ENV HOME=/home/user \
PATH=/home/user/.local/bin:$PATH
WORKDIR $HOME/app
# Copy requirements first to leverage Docker cache
COPY --chown=user requirements.txt $HOME/app
# Install Python dependencies
RUN pip install --no-cache-dir --upgrade pip && \
pip install --no-cache-dir -r requirements.txt && \
pip install --no-cache-dir 'tensorflow[and-cuda]' && \
pip install --no-cache-dir git+https://github.com/openai/CLIP.git
# Copy the whole application
COPY --chown=user . $HOME/app
# Install and build star-vector if it exists
# COPY star-vector/ ./star-vector/
# RUN if [ -d "star-vector" ]; then cd star-vector && pip install -e . && cd ..; fi
# Set environment variables for GPU usage
ENV NVIDIA_VISIBLE_DEVICES=all \
NVIDIA_DRIVER_CAPABILITIES=compute,utility
# Expose port for Streamlit
EXPOSE 8501
# Create a healthcheck
HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health || exit 1
# Set entry point
CMD yes | streamlit run app.py --server.port=8501 --server.address=0.0.0.0 |