Neural Architecture

pip install praisonaiagents

export OPENAI_API_KEY=your_api_key_here

from praisonaiagents import Agent, Task, PraisonAIAgents
import time
from typing import Dict, List
import asyncio

def analyze_hardware_constraints():
    """Simulates hardware analysis"""
    constraints = {
        "gpu_memory": 16 * 1024,  # MB
        "compute_capability": "7.5",
        "memory_bandwidth": 900,  # GB/s
        "tensor_cores": True,
        "max_power_consumption": 300,  # Watts
        "thermal_limits": 85  # Celsius
    }
    return constraints

def generate_architecture_candidates():
    """Generates candidate architectures"""
    candidates = [
        {
            "type": "transformer",
            "layers": 12 + (int(time.time()) % 6),
            "attention_heads": 8,
            "embedding_dim": 512,
            "estimated_params": 125_000_000
        },
        {
            "type": "cnn",
            "layers": 50 + (int(time.time()) % 50),
            "filters": [64, 128, 256, 512],
            "estimated_params": 25_000_000
        }
    ]
    return candidates[int(time.time()) % 2]

def optimize_hyperparameters(architecture: Dict):
    """Optimizes hyperparameters"""
    optimization = {
        "learning_rate": 0.001 + (time.time() % 9) / 10000,
        "batch_size": 32 * (1 + int(time.time()) % 4),
        "optimizer": "adam",
        "weight_decay": 0.0001,
        "dropout_rate": 0.1 + (time.time() % 3) / 10,
        "activation": "gelu" if architecture["type"] == "transformer" else "relu"
    }
    return optimization

def estimate_performance(architecture: Dict, hyperparams: Dict):
    """Estimates model performance"""
    performance = {
        "estimated_accuracy": 0.85 + (time.time() % 10) / 100,
        "estimated_training_time": {
            "hours": 24 + (int(time.time()) % 24),
            "gpu_hours": 96
        },
        "memory_requirements": {
            "training": architecture["estimated_params"] * 4 / (1024 * 1024),  # GB
            "inference": architecture["estimated_params"] * 2 / (1024 * 1024)  # GB
        },
        "flops_count": architecture["estimated_params"] * 2
    }
    return performance

def optimize_deployment(architecture: Dict, performance: Dict):
    """Optimizes deployment configuration"""
    deployment = {
        "quantization": {
            "method": "dynamic",
            "precision": "int8",
            "estimated_speedup": 2.5
        },
        "pruning": {
            "method": "magnitude",
            "target_sparsity": 0.3,
            "estimated_size_reduction": 0.6
        },
        "batching": {
            "max_batch_size": 32,
            "dynamic_batching": True
        }
    }
    return deployment

# Create specialized agents
hardware_analyzer = Agent(
    name="Hardware Analyzer",
    role="Hardware Analysis",
    goal="Analyze hardware constraints",
    instructions="Evaluate available hardware resources",
    tools=[analyze_hardware_constraints]
)

architecture_generator = Agent(
    name="Architecture Generator",
    role="Architecture Generation",
    goal="Generate neural architectures",
    instructions="Create candidate neural architectures",
    tools=[generate_architecture_candidates]
)

hyperparameter_optimizer = Agent(
    name="Hyperparameter Optimizer",
    role="Hyperparameter Optimization",
    goal="Optimize hyperparameters",
    instructions="Find optimal hyperparameter settings",
    tools=[optimize_hyperparameters]
)

performance_estimator = Agent(
    name="Performance Estimator",
    role="Performance Estimation",
    goal="Estimate model performance",
    instructions="Predict model performance metrics",
    tools=[estimate_performance]
)

deployment_optimizer = Agent(
    name="Deployment Optimizer",
    role="Deployment Optimization",
    goal="Optimize deployment settings",
    instructions="Configure optimal deployment",
    tools=[optimize_deployment]
)

# Create workflow tasks
hardware_task = Task(
    name="analyze_hardware",
    description="Analyze hardware constraints",
    expected_output="Hardware analysis",
    agent=hardware_analyzer,
    is_start=True,
    next_tasks=["generate_architecture"]
)

architecture_task = Task(
    name="generate_architecture",
    description="Generate architecture candidates",
    expected_output="Architecture candidates",
    agent=architecture_generator,
    next_tasks=["optimize_hyperparameters"]
)

hyperparameter_task = Task(
    name="optimize_hyperparameters",
    description="Optimize hyperparameters",
    expected_output="Hyperparameter settings",
    agent=hyperparameter_optimizer,
    context=[architecture_task],
    next_tasks=["estimate_performance"]
)

performance_task = Task(
    name="estimate_performance",
    description="Estimate performance",
    expected_output="Performance estimates",
    agent=performance_estimator,
    context=[architecture_task, hyperparameter_task],
    next_tasks=["optimize_deployment"]
)

deployment_task = Task(
    name="optimize_deployment",
    description="Optimize deployment",
    expected_output="Deployment configuration",
    agent=deployment_optimizer,
    context=[architecture_task, performance_task],
    task_type="decision",
    condition={
        "success": ["generate_architecture"],  # Try new architecture
        "failure": ["optimize_hyperparameters"]  # Reoptimize hyperparameters
    }
)

# Create workflow
workflow = PraisonAIAgents(
    agents=[hardware_analyzer, architecture_generator, hyperparameter_optimizer,
            performance_estimator, deployment_optimizer],
    tasks=[hardware_task, architecture_task, hyperparameter_task,
           performance_task, deployment_task],
    process="workflow",
    verbose=True
)

async def main():
    print("\nStarting Neural Architecture Search Workflow...")
    print("=" * 50)
    
    # Run workflow
    results = await workflow.astart()
    
    # Print results
    print("\nArchitecture Search Results:")
    print("=" * 50)
    for task_id, result in results["task_results"].items():
        if result:
            print(f"\nTask: {task_id}")
            print(f"Result: {result.raw}")
            print("-" * 50)

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