Agent 106: ML health endpoint (HTTP/8095) Agent 107: Redis test fix (serial_test isolation) Agent 108: CLAUDE.md draft update (95-97% → 100%) Agent 109: Prometheus/Grafana setup (31 alerts, 6 dashboards) Agent 110: Deployment docs (9 files + 4 scripts) Agent 111: Security audit prep (0 critical vulnerabilities) Service Health: 4/4 healthy (100%) Tests: 99%+ pass rate Production: ~98% readiness Next: Wave 2 (E2E, load, perf, security validation)
114 lines
3.6 KiB
Docker
114 lines
3.6 KiB
Docker
# Multi-stage build for Foxhunt ML Training Service
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# Wave 112 Agent 18: CUDA-enabled production Docker image
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# =============================================================================
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# Builder Stage with CUDA Support
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# =============================================================================
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FROM nvidia/cuda:12.3.0-devel-ubuntu22.04 AS builder
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# Install Rust
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RUN apt-get update && apt-get install -y \
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curl \
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build-essential \
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&& curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y \
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&& . $HOME/.cargo/env \
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&& rustup default stable \
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&& rustup component add rustfmt clippy
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ENV PATH="/root/.cargo/bin:${PATH}"
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# Install build dependencies including CUDA toolkit
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RUN apt-get update && apt-get install -y \
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pkg-config \
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libssl-dev \
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protobuf-compiler \
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nvidia-cuda-toolkit \
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&& rm -rf /var/lib/apt/lists/*
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# Set working directory
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WORKDIR /build
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# Set CUDA environment variables to skip GPU detection during build
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# CUDA 13.0 matches development environment, RTX 3050 Ti = compute capability 8.6
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ENV CUDARC_CUDA_VERSION=13000
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ENV CUDA_COMPUTE_CAP=86
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# Copy workspace manifests
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COPY Cargo.toml Cargo.lock ./
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# Copy workspace members to satisfy manifest dependencies
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COPY trading_engine ./trading_engine
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COPY risk ./risk
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COPY risk-data ./risk-data
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COPY trading-data ./trading-data
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COPY tli ./tli
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COPY ml ./ml
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COPY ml-data ./ml-data
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COPY data ./data
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COPY backtesting ./backtesting
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COPY adaptive-strategy ./adaptive-strategy
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COPY common ./common
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COPY storage ./storage
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COPY model_loader ./model_loader
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COPY market-data ./market-data
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COPY database ./database
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COPY config ./config
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COPY services/backtesting_service ./services/backtesting_service
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COPY services/trading_service ./services/trading_service
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COPY services/ml_training_service ./services/ml_training_service
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COPY services/api_gateway ./services/api_gateway
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COPY services/load_tests ./services/load_tests
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COPY services/stress_tests ./services/stress_tests
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COPY services/integration_tests ./services/integration_tests
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COPY tests ./tests
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COPY migrations ./migrations
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# Build the application (CUDA available at runtime via nvidia/cuda base image)
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RUN cargo build --release -p ml_training_service
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# =============================================================================
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# Runtime Stage with CUDA Runtime
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# =============================================================================
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FROM nvidia/cuda:12.3.0-runtime-ubuntu22.04
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# Install runtime dependencies
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RUN apt-get update && apt-get install -y \
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ca-certificates \
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libssl3 \
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curl \
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&& rm -rf /var/lib/apt/lists/*
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# Download and install grpc_health_probe
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RUN curl -sSL https://github.com/grpc-ecosystem/grpc-health-probe/releases/download/v0.4.25/grpc_health_probe-linux-amd64 \
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-o /usr/local/bin/grpc_health_probe && \
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chmod +x /usr/local/bin/grpc_health_probe
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# Create non-root user
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RUN groupadd --system --gid 1000 foxhunt && \
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useradd --system --uid 1000 --gid foxhunt --shell /bin/bash foxhunt
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# Create application directories
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RUN mkdir -p /app/config /app/logs /app/models /app/checkpoints && \
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chown -R foxhunt:foxhunt /app
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# Set working directory
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WORKDIR /app
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# Copy binary from builder
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COPY --from=builder /build/target/release/ml_training_service ./ml_training_service
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RUN chmod +x ./ml_training_service
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# Switch to non-root user
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USER foxhunt
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# Expose gRPC, metrics, and health ports
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EXPOSE 50053 9094 8080
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# Health check using HTTP endpoint (no TLS required)
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HEALTHCHECK --interval=10s --timeout=5s --start-period=30s --retries=3 \
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CMD curl -f http://localhost:8080/health || exit 1
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# Run the application with default serve command
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ENTRYPOINT ["./ml_training_service"]
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CMD ["serve"]
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