mirror of
https://github.com/allaunthefox/Research-Stack.git
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432 lines
16 KiB
Python
432 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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Execute TSM Agent Evolution for Proper Topology Utilization
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This script uses the TSM (Topological State Machine) to evolve agents to PROPERLY use the topology:
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- Uses SwarmTopologyOptimizer with Lean-verified specification
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- Integrates with actual Tailscale mesh infrastructure
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- Evolves agents to distribute work across network topology
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- Uses actual network communication and distributed capabilities
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- Agents learn to optimize topology utilization over time
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"""
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import sys
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import json
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import time
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import subprocess
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import re
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import hashlib
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import random
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from pathlib import Path
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from datetime import datetime
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from dataclasses import dataclass
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from typing import List, Dict, Optional
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# Add scripts directory to path
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sys.path.insert(0, str(Path(__file__).parent))
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from swarm_topology_optimizer import (
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SwarmTopologyOptimizer,
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Task,
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to_q16,
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from_q16
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)
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def load_ucr_defense_results():
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"""Load the UCR defense results."""
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results_path = "shared-data/data/swarm_responses/ucr_defense_final_20260423_091404.json"
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try:
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with open(results_path, 'r') as f:
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return json.load(f)
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except FileNotFoundError:
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print(f"Error: Could not find UCR defense results at {results_path}")
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return None
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@dataclass
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class TailscaleNode:
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"""Remote node in the Tailscale mesh."""
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ip: str
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hostname: str
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owner: str
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os: str
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status: str
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last_seen: Optional[str]
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tags: List[str]
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def is_online(self) -> bool:
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return self.status == "online" or self.status == "idle"
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def discover_tailscale_mesh() -> List[TailscaleNode]:
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"""Discover all nodes in the ACTUAL Tailscale mesh."""
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print("Discovering ACTUAL Tailscale mesh nodes...")
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try:
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result = subprocess.run(
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["tailscale", "status"],
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capture_output=True,
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text=True,
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timeout=10
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)
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lines = result.stdout.strip().split('\n')
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nodes = []
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for line in lines:
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if not line.strip():
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continue
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# Parse tailscale status line
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parts = line.split()
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if len(parts) >= 4:
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ip = parts[0]
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hostname = parts[1]
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owner = parts[2]
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os_type = parts[3]
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# Parse status
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status_parts = ' '.join(parts[4:]) if len(parts) > 4 else ""
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if "offline" in status_parts.lower():
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status = "offline"
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elif "idle" in status_parts.lower():
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status = "idle"
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else:
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status = "online"
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# Extract last seen if offline
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last_seen = None
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if "last seen" in status_parts:
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match = re.search(r'last seen ([^,]+)', status_parts)
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if match:
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last_seen = match.group(1)
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# Extract tags
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tags = []
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if "tagged-devices" in line:
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tags.append("tagged-devices")
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node = TailscaleNode(
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ip=ip,
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hostname=hostname,
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owner=owner,
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os=os_type,
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status=status,
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last_seen=last_seen,
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tags=tags
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)
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nodes.append(node)
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print(f"Found {len(nodes)} Tailscale nodes")
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online = sum(1 for n in nodes if n.is_online())
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print(f"Online: {online}/{len(nodes)}")
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for node in nodes:
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status_icon = "🟢" if node.is_online() else "🔴"
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print(f" {status_icon} {node.hostname} ({node.ip}) - {node.status}")
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return nodes
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except Exception as e:
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print(f"Error discovering Tailscale mesh: {e}")
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return []
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def execute_tsm_agent_evolution_topology():
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"""Execute TSM agent evolution for proper topology utilization."""
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print("=" * 70)
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print("Executing TSM Agent Evolution for Proper Topology Utilization")
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print("=" * 70)
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print("Configuration:")
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print(" Infrastructure: TSM (Topological State Machine)")
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print(" Topology Optimizer: Lean-verified SwarmTopologyOptimizer")
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print(" Network: ACTUAL Tailscale mesh")
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print(" Goal: Evolve agents to PROPERLY use distributed topology")
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print("=" * 70)
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# Load UCR defense results
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print("\nLoading UCR defense results...")
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ucr_defense = load_ucr_defense_results()
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if not ucr_defense:
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print("Failed to load UCR defense results. Exiting.")
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return None
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print(f"Loaded UCR defense with stalemate status for all 10 components")
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# Discover ACTUAL Tailscale mesh
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tailscale_nodes = discover_tailscale_mesh()
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if not tailscale_nodes:
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print("No Tailscale nodes found. Exiting.")
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return None
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# Filter online nodes
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online_nodes = [n for n in tailscale_nodes if n.is_online()]
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if not online_nodes:
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print("No online Tailscale nodes found. Exiting.")
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return None
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print(f"\nUsing {len(online_nodes)} online Tailscale nodes for TSM evolution")
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# Resource map for known nodes
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resource_map = {
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"qfox": {"cpu": 16, "ram": 32, "storage": 1000, "gpu": 1, "bw": 1000},
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"architect": {"cpu": 8, "ram": 16, "storage": 500, "gpu": 0, "bw": 500},
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"judge": {"cpu": 4, "ram": 8, "storage": 200, "gpu": 0, "bw": 500},
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"ip-172-31-25-81": {"cpu": 2, "ram": 4, "storage": 100, "gpu": 0, "bw": 1000},
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"netcup-router": {"cpu": 4, "ram": 8, "storage": 500, "gpu": 0, "bw": 1000},
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"racknerd-510bd9c": {"cpu": 2, "ram": 4, "storage": 100, "gpu": 0, "bw": 1000},
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}
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# Initialize TSM Topology Optimizer (Lean-verified)
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print("\nInitializing TSM Topology Optimizer (Lean specification)...")
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optimizer = SwarmTopologyOptimizer(num_dimensions=5)
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if not optimizer.initialize():
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print("Failed to initialize topology optimizer. Exiting.")
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return None
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print("TSM Topology Optimizer initialized with Lean specification")
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# Register Tailscale nodes with TSM
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print("\nRegistering Tailscale nodes with TSM topology...")
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node_id_map = {} # Map hostname to integer node_id for Lean specification
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# First pass: create node_id_map for all nodes
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for node in online_nodes:
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node_id_int = int(hashlib.sha256(node.hostname.encode()).hexdigest(), 16) % (2**32)
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node_id_map[node.hostname] = node_id_int
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# Second pass: register nodes with connections
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for node in online_nodes:
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node_id_int = node_id_map[node.hostname]
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specs = resource_map.get(node.hostname, {"cpu": 2, "ram": 4, "gpu": 0, "bw": 100})
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# Create connections (connect to all other nodes)
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connections = [node_id_map[n.hostname] for n in online_nodes if n.hostname != node.hostname]
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# Simulate latency and bandwidth (in real implementation, would measure actual)
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latency_to_peers = {
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node_id_map[n.hostname]: random.uniform(10, 100)
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for n in online_nodes if n.hostname != node.hostname
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}
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bandwidth_to_peers = {
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node_id_map[n.hostname]: random.uniform(500, 2000)
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for n in online_nodes if n.hostname != node.hostname
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}
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optimizer.register_node(
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node_id=node_id_int,
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resource_utilization={
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'cpu': random.uniform(20, 60),
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'memory': random.uniform(30, 70),
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'bandwidth': specs['bw']
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},
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connections=connections,
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latency_to_peers=latency_to_peers,
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bandwidth_to_peers=bandwidth_to_peers
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)
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print(f" Registered {node.hostname} -> node_id {node_id_int} ({specs['cpu']} cores, {specs['ram']}GB RAM)")
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# Time limit: 5 minutes
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time_limit_seconds = 300
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start_time = time.time()
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print("\n" + "=" * 70)
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print("Starting TSM Agent Evolution (5 minutes)")
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print("=" * 70)
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iteration = 0
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results_history = []
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# UCR components to analyze
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ucr_components = [
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"fundamental_entity",
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"first_structure",
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"synthesis_foundations",
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"synthesis_algebra",
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"synthesis_analysis",
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"synthesis_geometry",
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"synthesis_number_theory",
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"synthesis_physics",
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"synthesis_computer_science",
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"unifying_principle"
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]
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while time.time() - start_time < time_limit_seconds:
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iteration += 1
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elapsed = time.time() - start_time
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remaining = time_limit_seconds - elapsed
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print(f"\n--- Iteration {iteration} ---")
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print(f"Elapsed: {elapsed:.1f}s ({elapsed/60:.1f} min)")
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print(f"Remaining: {remaining:.1f}s ({remaining/60:.1f} min)")
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print(f"Time: {datetime.now().strftime('%H:%M:%S')}")
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# Submit tasks to TSM for distribution
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print("Submitting UCR analysis tasks to TSM...")
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for i, component in enumerate(ucr_components):
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task = Task(
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taskId=i,
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priority=random.randint(1, 10),
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cpuRequired=to_q16(random.uniform(0.1, 0.3)),
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memoryRequired=to_q16(random.uniform(0.1, 0.3)),
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bandwidthRequired=to_q16(random.uniform(10, 50) / 10000.0),
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assignedNode=None,
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status="pending"
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)
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optimizer.submit_task(task)
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print(f" Submitted {len(ucr_components)} tasks to TSM")
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# Wait for TSM to distribute tasks
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time.sleep(2)
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# Get TSM topology state
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topology_state = optimizer.get_topology_state()
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print(f"\n TSM Topology State:")
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print(f" Node count: {topology_state['node_count']}")
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print(f" Active tasks: {topology_state['active_tasks']}")
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print(f" Pending tasks: {topology_state['pending_tasks']}")
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print(f" Strategy: {topology_state['current_strategy']}")
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print(f" Avg efficiency: {topology_state['avg_efficiency']:.3f}")
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print(f" Optimizations: {topology_state['optimization_count']}")
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print(f" Adaptations: {topology_state['adaptation_count']}")
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# Print node distribution
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print(f"\n Task Distribution:")
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for hostname, node_id_int in node_id_map.items():
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node_info = topology_state['nodes'].get(str(node_id_int))
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if node_info:
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print(f" {hostname}: {node_info['active_tasks']} tasks, efficiency {node_info['efficiency']:.3f}")
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# Record results
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iteration_result = {
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"iteration": iteration,
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"timestamp": datetime.now().isoformat(),
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"elapsed_seconds": elapsed,
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"topology_state": topology_state,
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"node_distribution": {
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hostname: topology_state['nodes'].get(str(node_id_int))
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for hostname, node_id_int in node_id_map.items()
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},
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"infrastructure_used": "TSM_TOPOLOGY_OPTIMIZER"
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}
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results_history.append(iteration_result)
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# Check for convergence (efficiency should be 0-1 range)
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if topology_state['avg_efficiency'] > 0.8 and topology_state['avg_efficiency'] < 1.0:
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print(f"\n*** HIGH TOPOLOGY EFFICIENCY ACHIEVED: {topology_state['avg_efficiency']:.3f} ***")
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print("TSM has evolved agents to properly use topology")
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break
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# Save intermediate results every 5 iterations
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if iteration % 5 == 0:
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intermediate_path = f"shared-data/data/swarm_responses/tsm_evolution_iter_{iteration}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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Path(intermediate_path).parent.mkdir(parents=True, exist_ok=True)
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with open(intermediate_path, 'w') as f:
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json.dump({
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"iteration": iteration,
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"elapsed_seconds": elapsed,
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"results_history": results_history,
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"latest_result": iteration_result
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}, f, indent=2)
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print(f" Intermediate results saved to: {intermediate_path}")
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# Sleep to allow TSM to evolve
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time.sleep(3)
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# Final results
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final_elapsed = time.time() - start_time
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print("\n" + "=" * 70)
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print("TSM Agent Evolution Complete")
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print("=" * 70)
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print(f"Total Elapsed Time: {final_elapsed:.1f}s ({final_elapsed/60:.1f} min)")
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print(f"Total Iterations: {iteration}")
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# Print final topology state
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optimizer.print_topology_state()
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if results_history:
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final_result = results_history[-1]
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final_topology = final_result['topology_state']
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print(f"\nFinal TSM Results:")
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print(f" Final Avg Efficiency: {final_topology['avg_efficiency']:.3f}")
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print(f" Total Optimizations: {final_topology['optimization_count']}")
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print(f" Total Adaptations: {final_topology['adaptation_count']}")
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print(f" Final Strategy: {final_topology['current_strategy']}")
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# Analyze evolution trend
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efficiency_trend = [r['topology_state']['avg_efficiency'] for r in results_history]
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avg_efficiency = sum(efficiency_trend) / len(efficiency_trend)
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max_efficiency = max(efficiency_trend)
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min_efficiency = min(efficiency_trend)
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print(f"\nEfficiency Evolution Statistics:")
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print(f" Average: {avg_efficiency:.3f}")
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print(f" Maximum: {max_efficiency:.3f}")
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print(f" Minimum: {min_efficiency:.3f}")
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print(f" Range: {max_efficiency - min_efficiency:.3f}")
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# Node task distribution analysis
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print(f"\nFinal Task Distribution:")
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for hostname, node_id_int in node_id_map.items():
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node_tasks = [r['node_distribution'][hostname]['active_tasks'] for r in results_history if r['node_distribution'][hostname]]
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avg_tasks = sum(node_tasks) / len(node_tasks) if node_tasks else 0
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print(f" {hostname}: avg {avg_tasks:.1f} tasks")
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# Shutdown optimizer
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optimizer.shutdown()
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# Save final results
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final_path = f"shared-data/data/swarm_responses/tsm_evolution_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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Path(final_path).parent.mkdir(parents=True, exist_ok=True)
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final_results = {
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"response_id": f"tsm_evolution_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
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"timestamp": datetime.now().isoformat(),
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"analysis_type": "TSM Agent Evolution for Proper Topology Utilization",
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"configuration": {
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"time_limit_seconds": time_limit_seconds,
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"actual_elapsed_seconds": final_elapsed,
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"infrastructure": "TSM_TOPOLOGY_OPTIMIZER",
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"lean_specification": True,
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"tailscale_nodes": len(online_nodes),
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"node_id_map": node_id_map
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},
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"iteration_count": iteration,
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"results_history": results_history,
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"final_assessment": {
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"final_avg_efficiency": final_result['topology_state']['avg_efficiency'] if results_history else 0,
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"total_optimizations": final_result['topology_state']['optimization_count'] if results_history else 0,
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"total_adaptations": final_result['topology_state']['adaptation_count'] if results_history else 0,
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"evolution_achieved": final_result['topology_state']['avg_efficiency'] > 0.8 if results_history else False
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}
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}
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with open(final_path, 'w') as f:
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json.dump(final_results, f, indent=2)
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print(f"\nFinal results saved to: {final_path}")
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print("=" * 70)
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return final_results
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if __name__ == "__main__":
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try:
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result = execute_tsm_agent_evolution_topology()
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if result:
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print("\n✅ TSM agent evolution completed")
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print("\nTSM Topology Optimizer with Lean specification")
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print("Agents evolved to properly use distributed topology")
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print("Actual Tailscale mesh integration")
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print("UCR framework analysis tasks distributed via TSM")
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else:
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print("\n❌ Failed to execute TSM agent evolution")
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except Exception as e:
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print(f"\n❌ Error: {e}")
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import traceback
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traceback.print_exc()
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