Dockerfile.train

# Use CUDA 12.1 base image
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04

# Avoid prompts
ENV DEBIAN_FRONTEND=noninteractive

# Install Python 3.11 and other essentials
RUN apt-get update && apt-get install -y \
    python3.11 \
    python3-pip \
    python3.11-dev \
    git \
    && rm -rf /var/lib/apt/lists/*

# Set python3.11 as default python
RUN ln -s /usr/bin/python3.11 /usr/bin/python

WORKDIR /app

# Upgrade pip
RUN pip install --no-cache-dir -U pip setuptools wheel

# Install PyTorch with CUDA 12.1 support
RUN pip install --no-cache-dir \
    torch==2.4.0 \
    triton \
    xformers \
    --index-url https://download.pytorch.org/whl/cu121

# Install Unsloth and let it resolve its own compatible TRL/PEFT stack.
RUN pip install --no-cache-dir \
    "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" \
    datasets \
    wandb \
    matplotlib \
    fastapi \
    uvicorn \
    pydantic

# Copy the project files
COPY . .

# Install the local package in editable mode
RUN pip install -e .

# Make scripts executable
RUN chmod +x scripts/*.py

# Set environment variables
ENV MODEL_NAME="meta-llama/Llama-3.2-3B-Instruct"
ENV OUTPUT_DIR="outputs/commitguard-llama-3b-grpo"
ENV WANDB_PROJECT="commitguard"

# Default command: Run training and push to Hub
# Note: HF_TOKEN and WANDB_API_KEY should be set as Space Secrets
CMD ["python", "scripts/train_grpo.py", "--samples", "200", "--max-steps", "300", "--push-to-hub"]