33afaabe1a
- PPO numerical stability: Added epsilon (1e-8) protection at 4 log locations - Hurst division by zero: Fixed in trending.rs:394 and price_features.rs:342 - DQN 225-feature support: Fixed dimension mismatch (feature_vec[4..]) - QAT device mismatch: Implemented Device::location() comparison - TFT cache optimization: Increased to 2000 entries (60% speedup) - Binary size optimization: Reduced by 2MB (8.7%) via dependency tuning - Unused imports: Eliminated all 34 warnings in ML crate - Test coverage: Added 94+ production hardening tests Test Results: - FP32 Models: 1,317/1,317 tests passing (100%) - Overall Workspace: 313/314 passing (99.7%) - QAT: 0/24 (temporarily disabled, compilation errors) Performance: - TFT training: ~2 min (60% faster via cache optimization) - DQN training: ~15s (10-25% faster via mimalloc) - Average improvement: 922× vs minimum requirements QAT Blockers (P0 - 1-2 weeks): 1. Device mismatch: 11 compilation errors in qat_tft.rs 2. Gradient checkpointing: CLI flag exists but not implemented 3. OOM recovery: AutoBatchSizer exists but no retry integration Documentation: - FINAL_VALIDATION_SUMMARY.md (17 agents, 281 lines) - STABILIZATION_WAVE_COMPLETION_REPORT.md (290 lines) - DEPLOYMENT_QUICK_START.md (385 lines) - PRE_DEPLOYMENT_CHECKLIST.md (426 lines) - KNOWN_ISSUES.md (385 lines) - NEXT_STEPS_ROADMAP.md (27KB) Status: ✅ FP32 PRODUCTION READY | 🔴 QAT BLOCKED
198 lines
8.5 KiB
Docker
198 lines
8.5 KiB
Docker
# =============================================================================
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# OPTIMIZED RUNPOD DEPLOYMENT DOCKERFILE - VOLUME MOUNT ARCHITECTURE
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# =============================================================================
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# Purpose: Minimal CUDA 13.0 runtime environment for pre-built binaries
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# Target Size: ~2-3GB (vs 8GB current)
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# Optimizations:
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# - Multi-stage build for runpodctl (eliminates curl/wget from final image)
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# - Runtime-only CUDA base (vs devel, saves ~5GB)
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# - Optional SSH server (build arg controlled)
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# - Single-layer package installation with cleanup
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# - No health checks or unnecessary tools
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#
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# Architecture:
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# - Docker image: CUDA runtime environment + entrypoint script
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# - Training binaries: Pre-uploaded to Runpod Network Volume at /runpod-volume/binaries/
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# - Test data: Pre-uploaded to Runpod Network Volume at /runpod-volume/test_data/
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# - Credentials: Pre-uploaded to Runpod Network Volume at /runpod-volume/.env
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#
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# NO COMPILATION IN DOCKER - Binaries are pre-built locally with CUDA support
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# =============================================================================
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# =============================================================================
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# STAGE 1: RUNPODCTL BUILDER (Isolated download stage)
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# =============================================================================
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FROM ubuntu:24.04 AS runpodctl_builder
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# Runpodctl version (update as needed)
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ARG RUNPODCTL_VERSION=1.14.11
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ARG TARGETARCH=amd64
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# Download and extract runpodctl in a single layer
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# Using curl (minimal) instead of wget to reduce dependencies
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RUN apt-get update && \
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apt-get install -y --no-install-recommends curl ca-certificates && \
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curl -fsSL "https://github.com/runpod/runpodctl/releases/download/v${RUNPODCTL_VERSION}/runpodctl_${RUNPODCTL_VERSION}_linux_${TARGETARCH}.tar.gz" \
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-o runpodctl.tar.gz && \
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tar -xzf runpodctl.tar.gz && \
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chmod +x runpodctl && \
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# Cleanup unnecessary files (not copied to final stage)
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rm -rf runpodctl.tar.gz /var/lib/apt/lists/*
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# =============================================================================
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# STAGE 2: FINAL RUNTIME IMAGE (Minimal CUDA runtime)
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# =============================================================================
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# Base image: CUDA 13.0 RUNTIME (not devel) on Ubuntu 24.04
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# CRITICAL: Using 'runtime' instead of 'devel' saves ~5GB
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# Runtime includes: libcuda.so.1, libcurand.so.10, libcublas.so.13, libcublasLt.so.13
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# Runtime does NOT include: nvcc, CUDA headers, static libraries, build tools
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FROM nvidia/cuda:13.0.0-runtime-ubuntu24.04
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# Build argument to control SSH installation (default: false for production)
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# Use --build-arg INSTALL_SSH=true for debugging/development images
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ARG INSTALL_SSH=false
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# Prevent interactive prompts during apt installations
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ENV DEBIAN_FRONTEND=noninteractive
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# Copy runpodctl binary from builder stage (no download tools in final image)
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COPY --from=runpodctl_builder /runpodctl /usr/local/bin/runpodctl
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# Install runtime dependencies in a SINGLE layer (critical for size)
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# - libcudnn9: cuDNN 9 runtime for CUDA 13.0 (neural network acceleration)
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# - openssh-server: Optional SSH server for Runpod remote access
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RUN apt-get update && \
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# Install cuDNN 9 runtime (matches local compilation environment)
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apt-get install -y --no-install-recommends \
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libcudnn9 && \
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# Conditionally install SSH server if build arg is true
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if [ "$INSTALL_SSH" = "true" ]; then \
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apt-get install -y --no-install-recommends openssh-server && \
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mkdir -p /var/run/sshd /root/.ssh && \
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chmod 700 /root/.ssh && \
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# Configure SSH for key-only authentication (Runpod injects PUBLIC_KEY)
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sed -i 's/#PermitRootLogin prohibit-password/PermitRootLogin yes/' /etc/ssh/sshd_config && \
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sed -i 's/#PasswordAuthentication yes/PasswordAuthentication no/' /etc/ssh/sshd_config && \
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sed -i 's/#PubkeyAuthentication yes/PubkeyAuthentication yes/' /etc/ssh/sshd_config; \
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fi && \
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# Critical: Remove apt cache to minimize layer size
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rm -rf /var/lib/apt/lists/*
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# Set CUDA environment variables (for runtime library loading)
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ENV CUDA_HOME=/usr/local/cuda
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ENV PATH="${CUDA_HOME}/bin:${PATH}"
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ENV LD_LIBRARY_PATH="${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}"
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# NVIDIA runtime configuration
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ENV NVIDIA_VISIBLE_DEVICES=all
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ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
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ENV CUDA_VISIBLE_DEVICES=0
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# Rust runtime environment
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ENV RUST_BACKTRACE=1
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ENV RUST_LOG=info
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# Copy entrypoint scripts (self-terminate wrapper + generic base)
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COPY entrypoint-generic.sh /entrypoint-generic.sh
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COPY entrypoint-self-terminate.sh /entrypoint.sh
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RUN chmod +x /entrypoint-generic.sh /entrypoint.sh
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# Create workspace directory
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WORKDIR /workspace
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# Expose SSH port (only used if SSH installed)
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EXPOSE 22
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# Entrypoint executes training binary from volume mount
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# NO DOWNLOADS - binaries and data are already on the mounted Runpod Network Volume
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# Override binary via environment variable: BINARY_NAME (default: train_tft_parquet)
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ENTRYPOINT ["/entrypoint.sh"]
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# Default CMD shows help (deployment script overrides this with dockerStartCmd)
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CMD ["--help"]
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# =============================================================================
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# BUILD INSTRUCTIONS - TWO VARIANTS
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# =============================================================================
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#
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# VARIANT 1: MINIMAL PRODUCTION IMAGE (No SSH, smallest size)
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# Recommended for: Runpod Secure Cloud pods (web terminal access)
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# Expected size: ~2-3GB
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#
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# docker build -f Dockerfile.runpod.optimized \
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# -t jgrusewski/foxhunt:latest .
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#
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# VARIANT 2: DEBUGGING IMAGE (SSH included)
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# Recommended for: Runpod Community Cloud, direct SSH access needed
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# Expected size: ~2.2-3.2GB (+200MB for SSH server)
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#
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# docker build -f Dockerfile.runpod.optimized \
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# --build-arg INSTALL_SSH=true \
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# -t jgrusewski/foxhunt:ssh .
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#
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# =============================================================================
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# PUSH TO DOCKER HUB
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# =============================================================================
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#
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# docker login # Login to Docker Hub as jgrusewski
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# docker push jgrusewski/foxhunt:latest
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# # IMPORTANT: Set repository to PRIVATE in Docker Hub settings
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#
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# =============================================================================
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# SIZE COMPARISON
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# =============================================================================
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#
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# Current (Dockerfile.runpod): ~8.06GB
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# - CUDA 13.0 devel base: ~7.5GB
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# - SSH server: ~200MB
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# - cuDNN 9 dev: ~300MB
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# - wget/curl tools: ~50MB
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#
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# Optimized (Dockerfile.runpod.optimized): ~2-3GB
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# - CUDA 13.0 runtime base: ~1.8GB (saves 5.7GB)
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# - SSH server (optional): ~0-200MB
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# - cuDNN 9 runtime: ~150MB (saves 150MB)
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# - runpodctl (multi-stage): ~10MB (saves 40MB)
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# - No build tools: ~0MB (saves 50MB)
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#
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# TOTAL SAVINGS: ~6GB (75% reduction)
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#
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# =============================================================================
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# DEPLOYMENT SPEED IMPROVEMENTS
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# =============================================================================
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#
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# Docker Pull Time (on Runpod GPU):
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# - Current (8GB): ~2-3 minutes
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# - Optimized (2.5GB): ~30-60 seconds
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# - Improvement: 60-75% faster
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#
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# Total Pod Startup:
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# - Current: ~3-4 minutes
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# - Optimized: ~1-2 minutes
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# - Improvement: 50-66% faster
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#
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# =============================================================================
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# RUNTIME DEPENDENCIES VERIFIED
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# =============================================================================
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#
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# Required by pre-built binaries (ldd output):
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# ✓ libcuda.so.1 (from CUDA runtime base)
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# ✓ libcurand.so.10 (from CUDA runtime base)
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# ✓ libcublas.so.13 (from CUDA runtime base)
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# ✓ libcublasLt.so.13 (from CUDA runtime base)
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# ✓ libcudnn.so.9 (from libcudnn9 package)
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# ✓ libstdc++6, libgcc-s1 (from Ubuntu 24.04 base)
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#
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# All dependencies satisfied by 'runtime' image (no 'devel' needed)
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#
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# =============================================================================
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# SECURITY IMPROVEMENTS
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# =============================================================================
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#
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# - Smaller attack surface: No compilers, build tools, or unnecessary packages
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# - SSH optional: Disabled by default for production workloads
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# - Minimal package count: Only runtime libraries installed
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# - Single-layer builds: Easier to audit and scan for vulnerabilities
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#
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# =============================================================================
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