# GPU training image for ML model training Jobs
# Usage: docker build -f infra/docker/Dockerfile.training .
# Run:   docker run --gpus all foxhunt-training train_dqn [args...]

# =============================================================================
# Stage 1: Builder (CUDA dev image with Rust toolchain)
# =============================================================================
FROM nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04 AS builder

ENV DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y --no-install-recommends \
    curl \
    git \
    build-essential \
    pkg-config \
    libssl-dev \
    protobuf-compiler \
    perl \
    make \
    ca-certificates \
    && rm -rf /var/lib/apt/lists/*

# Install Rust stable toolchain
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain stable
ENV PATH="/root/.cargo/bin:${PATH}"

# Install sccache for build caching (no-op when SCCACHE_BUCKET is empty)
ARG SCCACHE_BUCKET=""
ARG AWS_ACCESS_KEY_ID=""
ARG AWS_SECRET_ACCESS_KEY=""
ARG SCCACHE_ENDPOINT=""
RUN if [ -n "$SCCACHE_BUCKET" ]; then \
      curl -fsSL https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
        | tar xz --strip-components=1 -C /usr/local/bin sccache-v0.8.1-x86_64-unknown-linux-musl/sccache \
      && chmod +x /usr/local/bin/sccache; \
    fi
ENV RUSTC_WRAPPER=${SCCACHE_BUCKET:+/usr/local/bin/sccache}
ENV SCCACHE_BUCKET=${SCCACHE_BUCKET}
ENV SCCACHE_ENDPOINT=${SCCACHE_ENDPOINT}
ENV SCCACHE_S3_USE_SSL=true
ENV AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
ENV AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}

WORKDIR /build

# Copy workspace manifests first for layer caching
COPY Cargo.toml Cargo.lock ./
COPY .sqlx ./.sqlx

# Copy all workspace crate directories
COPY trading_engine ./trading_engine
COPY risk ./risk
COPY risk-data ./risk-data
COPY trading-data ./trading-data
COPY fxt ./fxt
COPY ml ./ml
COPY ml-data ./ml-data
COPY data ./data
COPY backtesting ./backtesting
COPY adaptive-strategy ./adaptive-strategy
COPY common ./common
COPY storage ./storage
COPY model_loader ./model_loader
COPY market-data ./market-data
COPY database ./database
COPY config ./config
COPY web-gateway ./web-gateway
COPY ctrader-openapi ./ctrader-openapi
COPY services ./services
COPY tests ./tests

ENV SQLX_OFFLINE=true
ENV CUDA_COMPUTE_CAP=90

# Build all training example binaries with CUDA support
RUN cargo build --release -p ml --features ml/cuda \
    --example train_dqn_es_fut \
    --example train_ppo_parquet \
    --example train_tft_dbn \
    --example train_mamba2_dbn \
    --example train_liquid_dbn \
    --example train_tggn_dbn \
    --example train_kan_dbn \
    --example train_xlstm_dbn \
    --example train_diffusion_dbn \
    --example train_tlob \
    --example train_baseline \
    --example evaluate_baseline \
    --example hyperopt_dqn_demo \
    --example hyperopt_ppo_demo \
    --example hyperopt_tft_demo \
    --example hyperopt_mamba2_demo \
    && mkdir -p /build/out \
    && for bin in train_dqn_es_fut train_ppo_parquet train_tft_dbn train_mamba2_dbn train_liquid_dbn train_tggn_dbn train_kan_dbn train_xlstm_dbn train_diffusion_dbn train_tlob train_baseline evaluate_baseline hyperopt_dqn_demo hyperopt_ppo_demo hyperopt_tft_demo hyperopt_mamba2_demo; do \
        cp target/release/examples/${bin} /build/out/ && strip /build/out/${bin}; \
    done

# =============================================================================
# Stage 2: Runtime (CUDA runtime — no compiler, smaller image)
# =============================================================================
FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04

ENV DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y --no-install-recommends \
    ca-certificates \
    libssl3 \
    curl \
    unzip \
    && curl -fsSL https://downloads.rclone.org/current/rclone-current-linux-amd64.zip -o /tmp/rclone.zip \
    && unzip -j /tmp/rclone.zip '*/rclone' -d /usr/local/bin/ \
    && chmod +x /usr/local/bin/rclone \
    && rm /tmp/rclone.zip \
    && apt-get purge -y unzip \
    && rm -rf /var/lib/apt/lists/*

RUN groupadd -g 1000 foxhunt \
    && useradd -u 1000 -g foxhunt -m -s /bin/false foxhunt

COPY --from=builder /build/out/* /usr/local/bin/

# CUDA runtime environment
ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
ENV CUDA_COMPUTE_CAP=90

USER foxhunt

WORKDIR /data

CMD ["echo", "Specify training command via K8s Job args"]