ruvector/examples/google-cloud/Dockerfile.gpu
rUv 4d5d3bb092 feat(micro-hnsw-wasm): Add Neuromorphic HNSW v2.3 with SNN Integration (#40)
* docs: Add comprehensive GNN v2 implementation plans

Add 22 detailed planning documents for 19 advanced GNN features:

Tier 1 (Immediate - 3-6 months):
- GNN-Guided HNSW Routing (+25% QPS)
- Incremental Graph Learning/ATLAS (10-100x faster updates)
- Neuro-Symbolic Query Execution (hybrid neural + logical)

Tier 2 (Medium-Term - 6-12 months):
- Hyperbolic Embeddings (Poincaré ball model)
- Degree-Aware Adaptive Precision (2-4x memory reduction)
- Continuous-Time Dynamic GNN (concept drift detection)

Tier 3 (Research - 12+ months):
- Graph Condensation (10-100x smaller graphs)
- Native Sparse Attention (8-15x GPU speedup)
- Quantum-Inspired Attention (long-range dependencies)

Novel Innovations (10 experimental features):
- Gravitational Embedding Fields, Causal Attention Networks
- Topology-Aware Gradient Routing, Embedding Crystallization
- Semantic Holography, Entangled Subspace Attention
- Predictive Prefetch Attention, Morphological Attention
- Adversarial Robustness Layer, Consensus Attention

Includes comprehensive regression prevention strategy with:
- Feature flag system for safe rollout
- Performance baseline (186 tests + 6 search_v2 tests)
- Automated rollback mechanisms

Related to #38

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat(micro-hnsw-wasm): Add neuromorphic HNSW v2.3 with SNN integration

## New Crate: micro-hnsw-wasm v2.3.0
- Published to crates.io: https://crates.io/crates/micro-hnsw-wasm
- 11.8KB WASM binary with 58 exported functions
- Neuromorphic vector search combining HNSW + Spiking Neural Networks

### Core Features
- HNSW graph-based approximate nearest neighbor search
- Multi-distance metrics: L2, Cosine, Dot product
- GNN extensions: typed nodes, edge weights, neighbor aggregation
- Multi-core sharding: 256 cores × 32 vectors = 8K total

### Spiking Neural Network (SNN)
- LIF (Leaky Integrate-and-Fire) neurons with membrane dynamics
- STDP (Spike-Timing Dependent Plasticity) learning
- Spike propagation through graph topology
- HNSW→SNN bridge for similarity-driven neural activation

### Novel Neuromorphic Features (v2.3)
- Spike-Timing Vector Encoding (rate-to-time conversion)
- Homeostatic Plasticity (self-stabilizing thresholds)
- Oscillatory Resonance (40Hz gamma synchronization)
- Winner-Take-All Circuits (competitive selection)
- Dendritic Computation (nonlinear branch integration)
- Temporal Pattern Recognition (spike history matching)
- Combined Neuromorphic Search pipeline

### Performance Optimizations
- 5.5x faster SNN tick (2,726ns → 499ns)
- 18% faster STDP learning
- Pre-computed reciprocal constants
- Division elimination in hot paths

### Documentation & Organization
- Reorganized docs into subdirectories (gnn/, implementation/, publishing/, status/)
- Added comprehensive README with badges, SEO, citations
- Added benchmark.js and test_wasm.js test suites
- Added DEEP_REVIEW.md with performance analysis
- Added Verilog RTL for ASIC synthesis

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

---------

Co-authored-by: Claude <noreply@anthropic.com>
2025-12-01 22:30:15 -05:00

124 lines
4.8 KiB
Text

# =============================================================================
# RuVector Cloud Run GPU Dockerfile
# Optimized for NVIDIA L4 GPUs on Google Cloud Run
# =============================================================================
# -----------------------------------------------------------------------------
# Stage 1: Build Environment
# -----------------------------------------------------------------------------
FROM nvidia/cuda:12.3.1-devel-ubuntu22.04 AS builder
# Prevent interactive prompts
ENV DEBIAN_FRONTEND=noninteractive
# Install build dependencies
RUN apt-get update && apt-get install -y \
curl \
build-essential \
pkg-config \
libssl-dev \
cmake \
git \
clang \
llvm \
&& rm -rf /var/lib/apt/lists/*
# Install Rust
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
ENV PATH="/root/.cargo/bin:${PATH}"
# Set CUDA paths
ENV CUDA_HOME=/usr/local/cuda
ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
ENV PATH=${CUDA_HOME}/bin:${PATH}
WORKDIR /build
# Copy workspace Cargo files for dependency caching
COPY Cargo.toml Cargo.lock ./
# Copy all crate manifests
COPY crates/ruvector-core/Cargo.toml crates/ruvector-core/
COPY crates/ruvector-bench/Cargo.toml crates/ruvector-bench/
COPY crates/ruvector-gnn/Cargo.toml crates/ruvector-gnn/
COPY crates/ruvector-attention/Cargo.toml crates/ruvector-attention/
COPY crates/ruvector-raft/Cargo.toml crates/ruvector-raft/
COPY crates/ruvector-replication/Cargo.toml crates/ruvector-replication/
COPY crates/ruvector-cluster/Cargo.toml crates/ruvector-cluster/
COPY crates/ruvector-server/Cargo.toml crates/ruvector-server/
COPY crates/ruvector-collections/Cargo.toml crates/ruvector-collections/
COPY crates/ruvector-filter/Cargo.toml crates/ruvector-filter/
COPY crates/ruvector-metrics/Cargo.toml crates/ruvector-metrics/
COPY crates/ruvector-snapshot/Cargo.toml crates/ruvector-snapshot/
# Copy example manifest
COPY examples/google-cloud/Cargo.toml examples/google-cloud/
# Create stub files for dependency resolution
RUN mkdir -p crates/ruvector-core/src && echo "pub fn stub() {}" > crates/ruvector-core/src/lib.rs && \
mkdir -p crates/ruvector-bench/src && echo "pub fn stub() {}" > crates/ruvector-bench/src/lib.rs && \
mkdir -p crates/ruvector-gnn/src && echo "pub fn stub() {}" > crates/ruvector-gnn/src/lib.rs && \
mkdir -p crates/ruvector-attention/src && echo "pub fn stub() {}" > crates/ruvector-attention/src/lib.rs && \
mkdir -p crates/ruvector-raft/src && echo "pub fn stub() {}" > crates/ruvector-raft/src/lib.rs && \
mkdir -p crates/ruvector-replication/src && echo "pub fn stub() {}" > crates/ruvector-replication/src/lib.rs && \
mkdir -p crates/ruvector-cluster/src && echo "pub fn stub() {}" > crates/ruvector-cluster/src/lib.rs && \
mkdir -p crates/ruvector-server/src && echo "pub fn stub() {}" > crates/ruvector-server/src/lib.rs && \
mkdir -p crates/ruvector-collections/src && echo "pub fn stub() {}" > crates/ruvector-collections/src/lib.rs && \
mkdir -p crates/ruvector-filter/src && echo "pub fn stub() {}" > crates/ruvector-filter/src/lib.rs && \
mkdir -p crates/ruvector-metrics/src && echo "pub fn stub() {}" > crates/ruvector-metrics/src/lib.rs && \
mkdir -p crates/ruvector-snapshot/src && echo "pub fn stub() {}" > crates/ruvector-snapshot/src/lib.rs && \
mkdir -p examples/google-cloud/src && echo "fn main() {}" > examples/google-cloud/src/main.rs
# Build dependencies (cached layer)
RUN cargo build --release -p ruvector-cloudrun-gpu 2>/dev/null || true
# Copy actual source code
COPY crates/ crates/
COPY examples/google-cloud/src/ examples/google-cloud/src/
# Build the benchmark binary
RUN cargo build --release -p ruvector-cloudrun-gpu
# -----------------------------------------------------------------------------
# Stage 2: Runtime Environment
# -----------------------------------------------------------------------------
FROM nvidia/cuda:12.3.1-runtime-ubuntu22.04
# Install runtime dependencies
RUN apt-get update && apt-get install -y \
libssl3 \
ca-certificates \
curl \
&& rm -rf /var/lib/apt/lists/*
# Create non-root user
RUN useradd -m -u 1000 -s /bin/bash ruvector
# Create app directory
WORKDIR /app
# Copy binary from builder
COPY --from=builder /build/target/release/gpu-benchmark ./
# Set ownership
RUN chown -R ruvector:ruvector /app
# Switch to non-root user
USER ruvector
# Environment variables
ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
ENV RUVECTOR_GPU_ENABLED=true
ENV RUST_LOG=info
ENV PORT=8080
# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD curl -f http://localhost:${PORT}/health || exit 1
# Expose port
EXPOSE 8080
# Default command: start server
CMD ["./gpu-benchmark", "serve", "--port", "8080"]