# Sample Dockerfile for A6K ArtPark Server
# This template creates a user inside the container matching your server user

# Choose appropriate base image for your needs
# Examples:
# - nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04 (for GPU workloads)
# - nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04 (newer CUDA)
# - pytorch/pytorch:2.0.0-cuda11.8-cudnn8-devel (PyTorch pre-installed)
# - tensorflow/tensorflow:latest-gpu (TensorFlow pre-installed)
FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04

### USER CONFIGURATION - CHANGE THESE ###
# Set these to match your server username and IDs
# Find your user ID with: id -u
# Find your group ID with: id -g
# Find your username with: whoami
ENV USER_NAME=<your_username>
ENV USER_ID=<your_user_id>
ENV GROUP_ID=<your_group_id>
ENV USER_PSWD=<container_password>
### END USER CONFIGURATION ###

WORKDIR /app

# Prevent interactive prompts during package installation
ENV DEBIAN_FRONTEND=noninteractive

# Install system dependencies
# Customize this list based on your project needs
RUN apt-get update && \
    apt-get install -y \
    git \
    python3-pip \
    zsh \
    curl \
    wget \
    vim \
    tmux \
    sudo \
    libgl1-mesa-dev \
    libglib2.0-0 \
    # Add any other system packages you need here
    && rm -rf /var/lib/apt/lists/*

### USER SETUP - DO NOT MODIFY ###
# Creates a non-root user matching your server user
# This ensures files created in mounted volumes have correct permissions
RUN groupadd --gid $GROUP_ID $USER_NAME && \
    useradd --uid $USER_ID --gid $GROUP_ID -d /home/$USER_NAME -m --shell /bin/zsh $USER_NAME && \
    echo "${USER_NAME}:${USER_PSWD}" | chpasswd && \
    usermod -aG sudo ${USER_NAME}

RUN chown -R $USER_NAME:$USER_NAME /app
USER $USER_NAME
WORKDIR /home/$USER_NAME
ENV PATH="/home/${USER_NAME}/.local/bin:${PATH}"
### END USER SETUP ###

# Optional: Install Oh My Zsh for better terminal experience
# Remove this section if you prefer bash
RUN sh -c "$(curl -fsSL https://raw.githubusercontent.com/ohmyzsh/ohmyzsh/master/tools/install.sh)" "" --unattended
RUN git clone https://github.com/zsh-users/zsh-autosuggestions ${ZSH_CUSTOM:-~/.oh-my-zsh/custom}/plugins/zsh-autosuggestions
RUN sed -i 's/plugins=(git)/plugins=(git zsh-autosuggestions)/' ~/.zshrc

### PROJECT-SPECIFIC SETUP - CUSTOMIZE THIS SECTION ###

# Copy your project files
# COPY --chown=$USER_NAME:$USER_NAME <source_directory> /home/$USER_NAME/<destination>
# Example:
# COPY --chown=$USER_NAME:$USER_NAME ./my_project /home/$USER_NAME/my_project

# Set working directory to your project
# WORKDIR /home/$USER_NAME/<your_project_directory>

# Install Python packages
# Option 1: Using requirements.txt
# RUN pip install --no-cache-dir -r requirements.txt

# Option 2: Installing specific packages
# Example PyTorch installation (adjust CUDA version as needed):
# RUN pip install torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 --index-url https://download.pytorch.org/whl/cu118

# Example common ML packages:
# RUN pip install numpy pandas scikit-learn matplotlib seaborn jupyter

# Install any additional Python packages
# RUN pip install <package1> <package2> <package3>

# Build/compile any native code if needed
# Example CMake build:
# RUN cd <directory> && \
#     mkdir -p build && \
#     cd build && \
#     cmake .. && \
#     make

# Switch to root if you need system-level installation
# USER root
# RUN cd <directory>/build && make install
# USER ${USER_NAME}

# Download any required models, weights, or data
# RUN bash ./scripts/download_models.sh
# Or using wget/curl:
# RUN wget <url> -O <destination>

# Install your project as a package (if applicable)
# RUN python3 setup.py install --user

### END PROJECT-SPECIFIC SETUP ###

# Set the default command when container starts
# Options:
# - Interactive shell: CMD ["/bin/zsh"] or CMD ["/bin/bash"]
# - Run a script: CMD ["python", "main.py"]
# - Start a service: CMD ["jupyter", "lab", "--ip=0.0.0.0", "--port=8888"]
CMD ["/bin/zsh"]