distributed_system.py

# distributed_system.py
import ray
from typing import Dict, List, Any, Optional
import asyncio
import numpy as np
from dataclasses import dataclass

@dataclass
class WorkerConfig:
    worker_id: str
    compute_capacity: float
    memory_capacity: float
    specialization: Optional[str] = None

@ray.remote
class DistributedWorker:
    def __init__(self, config: WorkerConfig):
        self.config = config
        self.current_task = None
        self.state = "idle"
        
    async def process_task(self, task: Dict) -> Dict:
        self.state = "processing"
        self.current_task = task
        
        result = await self.execute_task(task)
        
        self.state = "idle"
        self.current_task = None
        return result
    
    async def execute_task(self, task: Dict) -> Dict:
        """Execute task based on type and specialization"""
        if task["type"] == "nas":
            return await self.execute_nas_task(task)
        elif task["type"] == "progressive_learning":
            return await self.execute_learning_task(task)
        return await self.execute_general_task(task)

class DistributedOrchestrator:
    def __init__(self, num_workers: int):
        ray.init()
        self.workers = [
            DistributedWorker.remote(WorkerConfig(
                worker_id=f"worker_{i}",
                compute_capacity=1.0,
                memory_capacity=1.0
            )) for i in range(num_workers)
        ]
        self.task_queue = asyncio.Queue()
        self.load_balancer = LoadBalancer(self.workers)
    
    async def submit_task(self, task: Dict):
        await self.task_queue.put(task)
        return await self.process_next_task()
    
    async def process_next_task(self):
        task = await self.task_queue.get()
        worker = await self.load_balancer.get_next_worker()
        return await worker.process_task.remote(task)

# neural_architecture_search.py
class NeuralArchitectureSearch:
    def __init__(self):
        self.search_space = self.define_search_space()
        self.optimizer = ArchitectureOptimizer()
        self.evaluator = ArchitectureEvaluator()
    
    def define_search_space(self) -> Dict:
        return {
            "layer_types": ["linear", "attention", "conv1d", "conv2d"],
            "activation_functions": ["relu", "gelu", "silu"],
            "attention_heads": [1, 2, 4, 8],
            "layer_dimensions": [64, 128, 256, 512],
            "dropout_rates": [0.0, 0.1, 0.2, 0.3]
        }
    
    async def search(self, task_requirements: Dict):
        """Perform neural architecture search"""
        current_architecture = self.initialize_architecture()
        
        for iteration in range(self.max_iterations):
            # Generate candidate architectures
            candidates = self.generate_candidates(current_architecture)
            
            # Evaluate candidates
            scores = await self.evaluate_candidates(candidates)
            
            # Update current architecture
            current_architecture = self.update_architecture(
                candidates, scores)
            
            if self.convergence_reached(scores):
                break
        
        return current_architecture

class ArchitectureOptimizer:
    def __init__(self):
        self.parameter_optimizer = ParameterOptimizer()
        self.structure_optimizer = StructureOptimizer()
        self.meta_optimizer = MetaOptimizer()
    
    async def optimize(self, architecture: Dict, performance_metrics: Dict):
        """Optimize architecture based on performance metrics"""
        # Optimize parameters
        params_opt = await self.parameter_optimizer.optimize(
            architecture, performance_metrics)
        
        # Optimize structure
        struct_opt = await self.structure_optimizer.optimize(
            params_opt, performance_metrics)
        
        # Meta-optimization
        return await self.meta_optimizer.optimize(
            struct_opt, performance_metrics)

# progressive_learning.py
class ProgressiveLearningSystem:
    def __init__(self):
        self.knowledge_accumulator = KnowledgeAccumulator()
        self.curriculum_manager = CurriculumManager()
        self.adaptation_system = AdaptationSystem()
    
    async def learn(self, task: Dict):
        """Progressive learning process"""
        # Get current curriculum stage
        current_stage = await self.curriculum_manager.get_current_stage()
        
        # Accumulate knowledge
        knowledge = await self.knowledge_accumulator.process(
            task, current_stage)
        
        # Adapt system based on performance
        adaptation = await self.adaptation_system.adapt(
            knowledge, current_stage)
        
        # Progress curriculum if ready
        if await self.curriculum_manager.should_progress(knowledge):
            await self.curriculum_manager.progress_stage()
        
        return adaptation

class CurriculumManager:
    def __init__(self):
        self.stages = self.define_curriculum_stages()
        self.current_stage = 0
        
    def define_curriculum_stages(self) -> List[Dict]:
        return [
            {
                "name": "foundation",
                "complexity": 0.2,
                "required_performance": 0.7
            },
            {
                "name": "intermediate",
                "complexity": 0.5,
                "required_performance": 0.75
            },
            {
                "name": "advanced",
                "complexity": 0.8,
                "required_performance": 0.8
            },
            {
                "name": "expert",
                "complexity": 1.0,
                "required_performance": 0.85
            }
        ]
    
    async def should_progress(self, knowledge: Dict) -> bool:
        """Determine if system should progress to next stage"""
        current_requirements = self.stages[self.current_stage]
        performance = await self.evaluate_performance(knowledge)
        
        return performance >= current_requirements["required_performance"]

# Integration
class EnhancedDistributedSystem:
    def __init__(self, num_workers: int):
        self.orchestrator = DistributedOrchestrator(num_workers)
        self.nas = NeuralArchitectureSearch()
        self.progressive_learning = ProgressiveLearningSystem()
    
    async def process_task(self, task: Dict):
        """Process task using distributed system"""
        # Submit to distributed system
        distributed_result = await self.orchestrator.submit_task(task)
        
        # Perform NAS if needed
        if task.get("require_nas"):
            architecture = await self.nas.search(task)
            distributed_result["optimized_architecture"] = architecture
        
        # Apply progressive learning
        learning_result = await self.progressive_learning.learn(
            distributed_result)
        
        return {
            "task_result": distributed_result,
            "architecture": architecture,
            "learning_progress": learning_result
        }

# Usage example
async def main():
    system = EnhancedDistributedSystem(num_workers=3)
    
    task = {
        "type": "nas",
        "require_nas": True,
        "data": {"some": "data"},
        "requirements": {
            "performance_threshold": 0.85,
            "max_complexity": 0.7
        }
    }
    
    result = await system.process_task(task)
    print("Processing Result:", result)

if __name__ == "__main__":
    asyncio.run(main())