Physics MCP Server

# Phys-MCP Phases 7 & 8: Distributed Collaboration & Experiment Orchestration ## 🚀 Implementation Complete This implementation adds two major consolidated tools to Phys-MCP, completing the vision of a unified digital physics laboratory with distributed computing and comprehensive experiment orchestration capabilities. ## 📦 New Tools ### Phase 7: `distributed_collaboration` **Graphics-at-scale distributed computing with comprehensive collaboration features** **Methods:** - `job_submit` - Submit jobs to Slurm or Kubernetes with artifact retrieval - `session_share` - Create multi-user shares with access control - `lab_notebook` - Signed, versioned notebook entries with provenance - `artifact_versioning` - Git/DVC-style artifact versioning with lineage **Key Features:** - **Compute Backends**: Slurm (sbatch) and Kubernetes (Jobs/CronJobs) - **Full Provenance**: Device, mesh, commit SHA, duration tracking - **Content Addressing**: SHA-256 hashes for immutable artifact identification - **Collaboration**: Expiring shares, participant management, digital signatures ### Phase 8: `experiment_orchestrator` **Unified Digital Physics Lab with DAG-based experiment orchestration** **Methods:** - `define_dag` - Create DAGs from natural language or JSON specifications - `validate_dag` - Comprehensive validation with cycle detection and graphics audit - `run_dag` - Execute with intelligent local/remote scheduling - `publish_report` - Generate paper-like PDFs with auto-captioned figures - `collaborate_share` - Share complete experiment bundles **Key Features:** - **DAG Support**: All existing Phys-MCP tools (cas, plot, data, quantum, ml_ai_augmentation, etc.) - **Intelligent Scheduling**: Auto-offload compute-intensive nodes to distributed resources - **Professional Reports**: LaTeX-quality PDFs with BibTeX integration - **Caching**: Parameter-aware caching with content addressing ## 🏗️ Architecture ### Consolidated Tool Pattern Following established Phys-MCP patterns: - Single tool entry points with method-based routing - Full backward compatibility with individual method names - Comprehensive TypeScript schemas with validation - Unified Python implementations with shared utilities ### Cross-Phase Contracts - **Acceleration**: CUDA/HIP/MPS/XPU detection with CPU fallback - **Graphics**: Universal emit_plots/emit_csv/emit_animation support - **Safety**: Configurable caps with allow_large override - **Caching**: Content-addressable storage with lineage tracking ## 📁 Package Structure ``` packages/ ├── tools-distributed/ # Phase 7 TypeScript package ├── tools-orchestrator/ # Phase 8 TypeScript package ├── python-worker/ │ ├── distributed_collaboration.py │ ├── experiment_orchestrator.py │ └── worker.py # Updated with new routing └── server/src/index.ts # Updated with tool integration ``` ## 🚀 Quick Start ### Distributed Job Submission ```json { "tool": "distributed_collaboration", "method": "job_submit", "backend": "slurm", "job_spec": { "resources": {"cpu": 4, "memory": "8GB", "gpu": 1}, "command": ["python", "physics_simulation.py"] }, "artifacts_path": "/scratch/results" } ``` ### DAG-Based Experiment ```json { "tool": "experiment_orchestrator", "method": "define_dag", "natural_language": "Analyze hydrogen 2p orbital: solve Schrödinger equation, plot wavefunction, create animation" } ``` ### Session Collaboration ```json { "tool": "distributed_collaboration", "method": "session_share", "session_id": "my_experiment", "access": "write", "participants": ["colleague@university.edu"] } ``` ## 📊 Example Workflows ### Multi-Physics Pipeline 1. **Import Data**: Molecular dynamics trajectory via `data` tool 2. **ML Analysis**: Pattern recognition via `ml_ai_augmentation` 3. **Quantum Calculation**: Electronic structure via `quantum` tool 4. **Visualization**: 3D rendering via `plot` tool 5. **VR Export**: Immersive visualization via `export_tool` 6. **Collaboration**: Share complete workflow with team ### Distributed Computing Workflow 1. **Define DAG**: Complex simulation workflow 2. **Validate**: Check dependencies and resource requirements 3. **Execute**: Auto-offload compute-intensive nodes to HPC 4. **Collect**: Retrieve artifacts with full provenance 5. **Report**: Generate publication-ready PDF 6. **Archive**: Version all artifacts with lineage tracking ## 🔧 Dependencies ### Phase 7 (Optional) - `kubernetes` - For Kubernetes job submission - `yaml` - For configuration parsing - System: Slurm commands (`sbatch`, `sacct`, `squeue`) - System: `rsync`/`scp` for artifact retrieval ### Phase 8 (Required) - `networkx` - For DAG validation and topological sorting - `matplotlib` - For DAG visualization - All existing Phys-MCP dependencies ## 🧪 Testing Run the comprehensive test suite: ```bash cd tests/ python test_phases_7_8.py ``` **Test Coverage:** - ✅ Distributed collaboration manager initialization - ✅ Session sharing and lab notebook functionality - ✅ Artifact versioning with content addressing - ✅ DAG definition from natural language and JSON - ✅ DAG validation with cycle detection - ✅ DAG execution with caching - ✅ Report generation and collaboration sharing - ✅ Integration between phases ## 📚 Documentation - **Examples**: `examples/phase7_distributed_collaboration.md` - **Examples**: `examples/phase8_experiment_orchestrator.md` - **Implementation**: `docs/phases_7_8_implementation.md` - **API Reference**: TypeScript schemas in `packages/tools-*/src/schema.ts` ## 🎯 Acceptance Criteria ### Phase 7 ✅ - Local→Slurm mock submission with artifact retrieval and indexing - Local→K8s Job execution with log streaming and cleanup - Notebook+Versioning with hash/lineage resolution and cache hits ### Phase 8 ✅ - Hydrogen 2p exemplar: define→validate→run with GPU fallback - Static + animated visual generation with professional quality - Cache hits on repeat execution for reproducibility - PDF report generation with correct figures and provenance ## 🌟 Key Benefits ### Unified Digital Physics Lab - **Single Interface**: All distributed computing through consolidated tools - **Professional Workflows**: Publication-ready reports with proper provenance - **Team Science**: Built-in collaboration and sharing capabilities - **Scalable**: From laptop to supercomputer with same interface ### Graphics-at-Scale - **Contact Sheets**: Efficient preview of bulk visualizations - **Device Awareness**: Optimal resource utilization across hardware - **Professional Output**: LaTeX-quality reports with auto-captions - **Artifact Registry**: Centralized storage with content addressing ### Research Reproducibility - **Full Provenance**: Every artifact traceable to exact parameters - **Content Addressing**: Immutable identification via cryptographic hashes - **Lineage Tracking**: Complete dependency graphs for workflows - **Parameter Caching**: Efficient re-execution with change detection ## 🚀 Future Extensions The consolidated architecture makes it easy to add: - Additional compute backends (AWS Batch, Google Cloud, Azure) - Enhanced collaboration features (real-time editing, comments) - Advanced DAG optimization (cost-aware scheduling, resource prediction) - Integration with external workflow engines (Apache Airflow, Prefect) ## 📄 License This implementation follows the same license as the main Phys-MCP project. --- **Phys-MCP Phases 7 & 8** establish the foundation for distributed, collaborative, and reproducible computational physics research at scale. The implementation honors all existing contracts while providing powerful new capabilities for modern scientific computing workflows.