Dockerfile.train

# Training Space Dockerfile — JupyterLab + auto-run on port 7860.
#
# Two modes (controlled by START_MODE env var in Space settings):
#   START_MODE=jupyter  → opens JupyterLab so you can run train_grpo.ipynb manually
#   START_MODE=auto     → runs train.py immediately, no interaction needed
#
# HF Space secrets to set:
#   HF_TOKEN, HF_USERNAME, WANDB_API_KEY
#   GRPO_STEPS (optional, default 100)

# Use the latest stable PyTorch image.
# unsloth-zoo pulls torchao>=0.13 which needs torch.utils._pytree.register_constant —
# available from torch 2.6.0+. cu126 is the matching CUDA toolkit.
FROM pytorch/pytorch:2.6.0-cuda12.6-cudnn9-devel

ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1

RUN apt-get update && apt-get install -y --no-install-recommends \
        git curl build-essential \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /workspace

# 1. Install unsloth exactly as their docs recommend.
#    --force-reinstall ensures no stale cached wheels.
RUN pip install --upgrade --force-reinstall --no-cache-dir unsloth unsloth_zoo

# 2. transformers v5 from git — required for Qwen3 support.
#    The stable 4.x release eagerly imports torchao's quantizer which cascades
#    into a hard import failure across peft → trl → SFTConfig.
RUN pip install --no-cache-dir \
    "git+https://github.com/huggingface/transformers.git@main"

# 3. Install project deps.
COPY pyproject.toml README.md ./
COPY src/ src/
RUN pip install --no-cache-dir -e ".[data,training]"

# 4. JupyterLab for interactive mode.
RUN pip install --no-cache-dir jupyterlab ipywidgets

# 5. Copy notebooks and training scripts.
COPY notebooks/ notebooks/
COPY train.py ./

# Persistent storage expected at /data (attach 20 GB disk in Space settings)
RUN mkdir -p /data/checkpoints /data/scenarios /data/sft_dataset

EXPOSE 7860

ENV START_MODE=jupyter

COPY train-entrypoint.sh /train-entrypoint.sh
RUN chmod +x /train-entrypoint.sh
ENTRYPOINT ["/train-entrypoint.sh"]