""" cloud_agents.py - Cloud Agents Integration for Intelligent Task Orchestration This module integrates OpenPeer AI's Cloud Agents for AI-driven task distribution, resource optimization, and intelligent scheduling of molecular docking workloads. Authors: OpenPeer AI, Riemann Computing Inc., Bleunomics, Andrew Magdy Kamal Version: 1.0.0 Date: 2025 """ import os import json import asyncio from typing import Dict, List, Optional, Any from dataclasses import dataclass, asdict from datetime import datetime import logging try: from huggingface_hub import InferenceClient from transformers import AutoTokenizer, AutoModel except ImportError: print("Warning: HuggingFace libraries not installed. Install with: pip install transformers huggingface-hub") # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @dataclass class Task: """Represents a molecular docking task""" task_id: str ligand_file: str receptor_file: str priority: str = "normal" # low, normal, high, critical estimated_compute_time: float = 0.0 required_memory: int = 0 use_gpu: bool = True status: str = "pending" assigned_node: Optional[str] = None created_at: datetime = None def __post_init__(self): if self.created_at is None: self.created_at = datetime.now() @dataclass class ComputeNode: """Represents a compute node in the distributed system""" node_id: str cpu_cores: int gpu_available: bool gpu_type: Optional[str] = None memory_gb: int = 16 current_load: float = 0.0 tasks_completed: int = 0 average_task_time: float = 0.0 is_active: bool = True location: Optional[str] = None class CloudAgentsOrchestrator: """ AI-powered orchestration using Cloud Agents for intelligent task distribution and resource optimization """ def __init__(self, config: Optional[Dict] = None): """ Initialize Cloud Agents orchestrator Args: config: Configuration dictionary """ self.config = config or {} self.hf_token = self.config.get('hf_token', os.getenv('HF_TOKEN')) self.model_name = self.config.get('model_name', 'OpenPeer AI/Cloud-Agents') self.tasks: Dict[str, Task] = {} self.nodes: Dict[str, ComputeNode] = {} self.task_queue: List[str] = [] self.client: Optional[InferenceClient] = None self.is_initialized = False logger.info("CloudAgentsOrchestrator initialized") async def initialize(self) -> bool: """ Initialize the Cloud Agents system Returns: True if initialization successful """ try: logger.info("Initializing Cloud Agents...") # Initialize HuggingFace Inference Client if self.hf_token: self.client = InferenceClient( model=self.model_name, token=self.hf_token ) logger.info(f"Connected to Cloud Agents model: {self.model_name}") else: logger.warning("HuggingFace token not provided. Using local mode.") self.is_initialized = True logger.info("Cloud Agents initialized successfully") return True except Exception as e: logger.error(f"Failed to initialize Cloud Agents: {e}") return False def register_node(self, node: ComputeNode) -> bool: """ Register a compute node with the orchestrator Args: node: ComputeNode instance Returns: True if registration successful """ try: self.nodes[node.node_id] = node logger.info(f"Node registered: {node.node_id} (GPU: {node.gpu_available})") return True except Exception as e: logger.error(f"Failed to register node: {e}") return False def submit_task(self, task: Task) -> str: """ Submit a new docking task Args: task: Task instance Returns: Task ID """ self.tasks[task.task_id] = task self.task_queue.append(task.task_id) logger.info(f"Task submitted: {task.task_id} (Priority: {task.priority})") return task.task_id async def optimize_task_distribution(self) -> Dict[str, Any]: """ Use AI to optimize task distribution across nodes Returns: Optimization recommendations """ try: # Prepare context for AI agent context = { "total_tasks": len(self.tasks), "pending_tasks": len(self.task_queue), "active_nodes": len([n for n in self.nodes.values() if n.is_active]), "gpu_nodes": len([n for n in self.nodes.values() if n.gpu_available]), "avg_node_load": sum(n.current_load for n in self.nodes.values()) / max(len(self.nodes), 1) } # Use Cloud Agents for intelligent decision making prompt = self._create_optimization_prompt(context) if self.client: # Query Cloud Agents model response = await self._query_cloud_agents(prompt) recommendations = self._parse_ai_response(response) else: # Fallback to rule-based optimization recommendations = self._rule_based_optimization() logger.info(f"Optimization complete: {recommendations}") return recommendations except Exception as e: logger.error(f"Optimization failed: {e}") return self._rule_based_optimization() async def schedule_tasks(self) -> List[Dict[str, str]]: """ Schedule pending tasks to available nodes using AI optimization Returns: List of task assignments """ assignments = [] # Get optimization recommendations recommendations = await self.optimize_task_distribution() # Process pending tasks for task_id in self.task_queue[:]: task = self.tasks.get(task_id) if not task or task.status != "pending": continue # Find optimal node for this task node = self._select_optimal_node(task, recommendations) if node: # Assign task to node task.assigned_node = node.node_id task.status = "assigned" node.current_load += 0.1 # Increment load assignments.append({ "task_id": task_id, "node_id": node.node_id, "priority": task.priority }) self.task_queue.remove(task_id) logger.info(f"Task {task_id} assigned to node {node.node_id}") return assignments def _select_optimal_node(self, task: Task, recommendations: Dict) -> Optional[ComputeNode]: """ Select the optimal node for a task based on AI recommendations Args: task: Task to assign recommendations: AI recommendations Returns: Selected ComputeNode or None """ # Filter available nodes available_nodes = [ node for node in self.nodes.values() if node.is_active and node.current_load < 0.9 ] if not available_nodes: return None # Prefer GPU nodes for GPU tasks if task.use_gpu: gpu_nodes = [n for n in available_nodes if n.gpu_available] if gpu_nodes: available_nodes = gpu_nodes # Sort by load and performance available_nodes.sort(key=lambda n: ( n.current_load, -n.tasks_completed, n.average_task_time )) # Apply priority boost for high-priority tasks if task.priority == "critical": # Select fastest node regardless of load available_nodes.sort(key=lambda n: n.average_task_time) return available_nodes[0] if available_nodes else None async def _query_cloud_agents(self, prompt: str) -> str: """ Query Cloud Agents model for intelligent decision making Args: prompt: Input prompt Returns: AI response """ try: # Use HuggingFace Inference API response = self.client.text_generation( prompt, max_new_tokens=500, temperature=0.7, top_p=0.9 ) return response except Exception as e: logger.error(f"Cloud Agents query failed: {e}") return "" def _create_optimization_prompt(self, context: Dict) -> str: """ Create optimization prompt for Cloud Agents Args: context: System context Returns: Formatted prompt """ prompt = f""" You are an AI orchestrator for a distributed molecular docking system. Current System Status: - Total Tasks: {context['total_tasks']} - Pending Tasks: {context['pending_tasks']} - Active Nodes: {context['active_nodes']} - GPU-enabled Nodes: {context['gpu_nodes']} - Average Node Load: {context['avg_node_load']:.2f} Task: Optimize task distribution to: 1. Maximize throughput 2. Minimize waiting time for high-priority tasks 3. Balance load across nodes 4. Utilize GPU resources efficiently Provide recommendations for: - Load balancing strategy - Priority handling - GPU allocation - Estimated completion time Response format (JSON): """ return prompt def _parse_ai_response(self, response: str) -> Dict[str, Any]: """ Parse AI response into actionable recommendations Args: response: Raw AI response Returns: Parsed recommendations """ try: # Attempt to parse JSON response recommendations = json.loads(response) return recommendations except: # Fallback to default recommendations return self._rule_based_optimization() def _rule_based_optimization(self) -> Dict[str, Any]: """ Fallback rule-based optimization Returns: Optimization recommendations """ return { "strategy": "load_balanced", "gpu_priority": True, "max_tasks_per_node": 10, "rebalance_threshold": 0.8 } def get_task_status(self, task_id: str) -> Optional[Dict[str, Any]]: """ Get status of a specific task Args: task_id: Task ID Returns: Task status dictionary """ task = self.tasks.get(task_id) if not task: return None return asdict(task) def get_system_statistics(self) -> Dict[str, Any]: """ Get overall system statistics Returns: Statistics dictionary """ total_tasks = len(self.tasks) completed_tasks = len([t for t in self.tasks.values() if t.status == "completed"]) pending_tasks = len(self.task_queue) active_nodes = [n for n in self.nodes.values() if n.is_active] total_compute_power = sum(n.cpu_cores for n in active_nodes) return { "total_tasks": total_tasks, "completed_tasks": completed_tasks, "pending_tasks": pending_tasks, "active_nodes": len(active_nodes), "total_compute_power": total_compute_power, "gpu_nodes": len([n for n in active_nodes if n.gpu_available]), "average_node_load": sum(n.current_load for n in active_nodes) / max(len(active_nodes), 1), "throughput": completed_tasks / max((datetime.now() - list(self.tasks.values())[0].created_at).total_seconds() / 3600, 1) if self.tasks else 0 } async def auto_scale(self) -> Dict[str, Any]: """ Automatically scale resources based on workload Returns: Scaling recommendations """ stats = self.get_system_statistics() recommendations = { "action": "none", "reason": "", "suggested_nodes": 0 } # Check if we need more resources if stats["pending_tasks"] > stats["active_nodes"] * 5: recommendations["action"] = "scale_up" recommendations["suggested_nodes"] = stats["pending_tasks"] // 5 recommendations["reason"] = "High pending task count" # Check if we have excess capacity elif stats["average_node_load"] < 0.3 and stats["pending_tasks"] == 0: recommendations["action"] = "scale_down" recommendations["suggested_nodes"] = -1 recommendations["reason"] = "Low resource utilization" logger.info(f"Auto-scale recommendation: {recommendations}") return recommendations async def main(): """Example usage of Cloud Agents orchestrator""" # Initialize orchestrator orchestrator = CloudAgentsOrchestrator() await orchestrator.initialize() # Register some compute nodes node1 = ComputeNode( node_id="node_1", cpu_cores=16, gpu_available=True, gpu_type="RTX 3090", memory_gb=64 ) node2 = ComputeNode( node_id="node_2", cpu_cores=8, gpu_available=False, memory_gb=32 ) orchestrator.register_node(node1) orchestrator.register_node(node2) # Submit tasks for i in range(5): task = Task( task_id=f"task_{i}", ligand_file=f"ligand_{i}.pdbqt", receptor_file="receptor.pdbqt", priority="normal" if i < 3 else "high" ) orchestrator.submit_task(task) # Schedule tasks assignments = await orchestrator.schedule_tasks() print(f"Scheduled {len(assignments)} tasks") # Get statistics stats = orchestrator.get_system_statistics() print(f"System stats: {json.dumps(stats, indent=2)}") if __name__ == "__main__": asyncio.run(main())