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metadata
title: Docker Neural Memory
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
license: mit
Docker Neural Memory
Real Test-Time Training - Not a Simulation
This demo runs actual PyTorch code implementing Google's Titans architecture. When you observe content, real gradients flow and real neural network weights update.
What Makes This Real
- Real Neural Network: 2-layer MLP with ~250K parameters
- Real Gradient Descent:
torch.autograd.grad()computes gradients - Real Weight Updates: Parameters physically change during inference
- Real Surprise Metric: MSE loss measures prediction error
Docker-Native Design
This project demonstrates production-grade AI infrastructure:
- MCP Server: Model Context Protocol for Claude Desktop integration
- Docker Volumes: Persist learned state across container restarts
- CI/CD Pipeline: GitHub Actions with Docker build and deploy
- Kubernetes Ready: Designed for orchestrated deployment
Key Features
| Feature | Implementation |
|---|---|
| Test-Time Training | PyTorch autograd during inference |
| State Persistence | Docker volumes for checkpoints |
| MCP Integration | Tools: observe, surprise, checkpoint, restore |
| Bounded Memory | Fixed parameters (doesn't grow like vector DBs) |
Built By
Carlos Crespo Macaya - AI Engineer
- 10+ years production ML experience
- Expert in Docker, Kubernetes, MCP servers
- Currently at HP AICoE building multi-agent systems
Contact: macayaven@gmail.com