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"""
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())
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