Spaces:
Sleeping
Sleeping
| 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 | |
| ## Links | |
| - [GitHub Repository](https://github.com/macayaven/docker-neural-memory) | |
| - [Titans Paper](https://arxiv.org/abs/2501.00663) | |