File: //opt/PraisonAI/docs/examples/neural-architecture.mdx
---
title: "Neural Architecture"
description: "Learn how to create AI agents for neural architecture search and optimization."
icon: "brain"
---
```mermaid
flowchart LR
In[In] --> Hardware[Hardware Analyzer]
Hardware --> Generator[Architecture Generator]
Generator --> Hyperparams[Hyperparameter Optimizer]
Hyperparams --> Performance[Performance Estimator]
Performance --> Deployment[Deployment Optimizer]
Deployment --> Out[Out]
style In fill:#8B0000,color:#fff
style Hardware fill:#2E8B57,color:#fff
style Generator fill:#2E8B57,color:#fff
style Hyperparams fill:#2E8B57,color:#fff
style Performance fill:#2E8B57,color:#fff
style Deployment fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
```
## What is Neural Architecture Search?
Neural Architecture Search (NAS) is an automated process for designing optimal neural network architectures. It involves analyzing hardware constraints, generating candidate architectures, optimizing hyperparameters, and configuring deployment settings to create efficient and performant models.
## Features
<CardGroup cols={2}>
<Card title="Hardware Analysis" icon="microchip">
Analysis of GPU memory, compute capability, bandwidth, and thermal constraints.
</Card>
<Card title="Architecture Generation" icon="diagram-project">
Creation of candidate architectures including transformers and CNNs with optimized structures.
</Card>
<Card title="Hyperparameter Tuning" icon="sliders">
Optimization of learning rates, batch sizes, optimizers, and regularization parameters.
</Card>
<Card title="Performance Estimation" icon="gauge-high">
Prediction of model accuracy, training time, memory requirements, and FLOPS.
</Card>
<Card title="Deployment Optimization" icon="rocket">
Configuration of quantization, pruning, and batching for efficient deployment.
</Card>
</CardGroup>
## Quick Start
<Steps>
<Step title="Install Package">
First, install the PraisonAI Agents package:
```bash
pip install praisonaiagents
```
</Step>
<Step title="Set API Key">
Set your OpenAI API key as an environment variable in your terminal:
```bash
export OPENAI_API_KEY=your_api_key_here
```
</Step>
<Step title="Create a file">
Create a new file `app.py` with the basic setup:
```python
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())
```
</Step>
</Steps>
## Next Steps
<CardGroup>
<Card title="Introduction" icon="book" href="/introduction">
Learn more about PraisonAI and its core concepts
</Card>
<Card title="Quick Start" icon="bolt" href="/quickstart">
Get started with the basics of PraisonAI
</Card>
</CardGroup>