AI-Mining Optimization Tutorial

pip install torch torchvision tensorflow-gpu tos-ai-mining-sdk

# Create dedicated environment
python -m venv tos-ai-mining
source tos-ai-mining/bin/activate  # Linux/Mac
# tos-ai-mining\Scripts\activate  # Windows
 
# Install required packages
pip install tos-ai-mining-sdk torch torchvision tensorflow-gpu numpy pandas matplotlib

# config/ai_mining_config.py
AI_MINING_CONFIG = {
    'network': 'mainnet',
    'rpc_url': 'https://rpc.tos.network',
    'worker_address': 'your_tos_wallet_address',
    'private_key': 'your_private_key',
 
    # Hardware configuration
    'hardware': {
        'gpu_enabled': True,
        'max_gpu_memory': 0.8,  # Use 80% of GPU memory
        'cpu_threads': -1,      # Use all CPU cores
        'mixed_precision': True, # Enable FP16 for better performance
    },
 
    # Task preferences
    'task_preferences': {
        'min_reward': 1.0,      # Minimum reward in TOS
        'max_duration': 3600,   # Maximum task duration in seconds
        'preferred_types': ['machine_learning', 'computer_vision', 'nlp'],
        'avoid_types': ['blockchain_analysis']  # Tasks to avoid
    },
 
    # Quality optimization
    'quality_settings': {
        'target_score': 8.5,    # Target quality score
        'verification_enabled': True,
        'ensemble_methods': True,
        'cross_validation': True
    }
}

# src/ai_mining_client.py
import torch
import tensorflow as tf
from tos_ai_mining import TOSAIMiningClient
from config.ai_mining_config import AI_MINING_CONFIG
 
class OptimizedAIMiningClient:
    def __init__(self, config):
        self.config = config
        self.client = TOSAIMiningClient(
            rpc_url=config['rpc_url'],
            worker_address=config['worker_address'],
            private_key=config['private_key']
        )
 
        self.setup_hardware()
        self.performance_metrics = {
            'tasks_completed': 0,
            'total_earnings': 0.0,
            'average_quality_score': 0.0,
            'energy_consumed': 0.0
        }
 
    def setup_hardware(self):
        """Optimize hardware configuration"""
        # GPU optimization
        if self.config['hardware']['gpu_enabled'] and torch.cuda.is_available():
            torch.cuda.empty_cache()
 
            # Set memory fraction
            if hasattr(torch.cuda, 'set_per_process_memory_fraction'):
                torch.cuda.set_per_process_memory_fraction(
                    self.config['hardware']['max_gpu_memory']
                )
 
            print(f"GPU: {torch.cuda.get_device_name()}")
            print(f"CUDA Version: {torch.version.cuda}")
            print(f"Available GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")
 
        # TensorFlow GPU configuration
        gpus = tf.config.experimental.list_physical_devices('GPU')
        if gpus:
            try:
                for gpu in gpus:
                    tf.config.experimental.set_memory_growth(gpu, True)
 
                # Set memory limit
                memory_limit = int(tf.config.experimental.get_device_details(gpus[0])['device_memory_size'] *
                                  self.config['hardware']['max_gpu_memory'])
                tf.config.experimental.set_memory_limit(gpus[0], memory_limit)
 
            except RuntimeError as e:
                print(f"GPU configuration error: {e}")
 
        # CPU optimization
        torch.set_num_threads(self.config['hardware']['cpu_threads'])
        tf.config.threading.set_intra_op_parallelism_threads(self.config['hardware']['cpu_threads'])
 
        print("Hardware optimization completed")
 
    async def connect(self):
        """Connect to TOS AI-Mining network"""
        await self.client.connect()
        print("Connected to TOS AI-Mining network")

# src/task_selector.py
import numpy as np
from dataclasses import dataclass
from typing import List, Dict
 
@dataclass
class TaskMetrics:
    task_id: str
    reward: float
    difficulty: str
    estimated_duration: int
    success_rate: float
    average_quality_score: float
    energy_cost: float
 
    @property
    def reward_per_hour(self) -> float:
        return (self.reward * self.success_rate) / (self.estimated_duration / 3600)
 
    @property
    def energy_efficiency(self) -> float:
        return self.reward_per_hour / self.energy_cost if self.energy_cost > 0 else 0
 
    @property
    def score(self) -> float:
        # Weighted scoring algorithm
        weights = {
            'reward_per_hour': 0.4,
            'energy_efficiency': 0.3,
            'success_rate': 0.2,
            'quality_score': 0.1
        }
 
        normalized_scores = {
            'reward_per_hour': min(self.reward_per_hour / 10, 1),
            'energy_efficiency': min(self.energy_efficiency / 5, 1),
            'success_rate': self.success_rate,
            'quality_score': self.average_quality_score / 10
        }
 
        return sum(weights[key] * normalized_scores[key] for key in weights)
 
class IntelligentTaskSelector:
    def __init__(self, client, config):
        self.client = client
        self.config = config
        self.task_history = []
        self.performance_data = {}
 
    async def get_optimal_tasks(self, limit: int = 5) -> List[TaskMetrics]:
        """Select optimal tasks based on profitability and hardware capabilities"""
        available_tasks = await self.client.get_available_tasks()
 
        # Analyze each task
        task_metrics = []
        for task in available_tasks:
            metrics = await self.analyze_task(task)
            if self.meets_criteria(metrics):
                task_metrics.append(metrics)
 
        # Sort by optimization score
        task_metrics.sort(key=lambda x: x.score, reverse=True)
 
        return task_metrics[:limit]
 
    async def analyze_task(self, task) -> TaskMetrics:
        """Analyze task profitability and feasibility"""
        # Get historical performance data
        historical_performance = self.get_historical_performance(task['type'])
 
        # Estimate duration based on hardware and task complexity
        estimated_duration = self.estimate_duration(task)
 
        # Calculate energy cost
        energy_cost = self.estimate_energy_cost(task, estimated_duration)
 
        return TaskMetrics(
            task_id=task['task_id'],
            reward=float(task['reward']),
            difficulty=task['difficulty'],
            estimated_duration=estimated_duration,
            success_rate=historical_performance.get('success_rate', 0.8),
            average_quality_score=historical_performance.get('avg_quality', 7.0),
            energy_cost=energy_cost
        )
 
    def meets_criteria(self, metrics: TaskMetrics) -> bool:
        """Check if task meets minimum criteria"""
        criteria = self.config['task_preferences']
 
        return (
            metrics.reward >= criteria['min_reward'] and
            metrics.estimated_duration <= criteria['max_duration'] and
            metrics.success_rate >= 0.6 and
            metrics.energy_efficiency >= 0.5
        )
 
    def estimate_duration(self, task) -> int:
        """Estimate task completion time based on hardware and complexity"""
        base_duration = task.get('estimated_time', 3600)
 
        # Hardware performance factors
        gpu_factor = 1.0
        if torch.cuda.is_available():
            gpu_name = torch.cuda.get_device_name()
            if 'RTX 4090' in gpu_name or 'A100' in gpu_name:
                gpu_factor = 0.5
            elif 'RTX 3080' in gpu_name or 'RTX 3090' in gpu_name:
                gpu_factor = 0.7
            elif 'RTX 2080' in gpu_name or 'GTX 1080' in gpu_name:
                gpu_factor = 1.2
 
        # Task complexity factors
        complexity_factors = {
            'easy': 0.8,
            'medium': 1.0,
            'hard': 1.5,
            'expert': 2.0
        }
 
        complexity_factor = complexity_factors.get(task['difficulty'], 1.0)
 
        return int(base_duration * gpu_factor * complexity_factor)
 
    def estimate_energy_cost(self, task, duration) -> float:
        """Estimate energy consumption for task"""
        # Base power consumption (watts)
        base_power = 100  # CPU base
 
        if torch.cuda.is_available():
            gpu_name = torch.cuda.get_device_name()
            gpu_power = {
                'RTX 4090': 450,
                'RTX 3080': 320,
                'RTX 3070': 220,
                'GTX 1060': 120
            }
 
            for model, power in gpu_power.items():
                if model in gpu_name:
                    base_power += power
                    break
 
        # Convert to kWh
        energy_kwh = (base_power * duration / 3600) / 1000
 
        # Assume $0.10 per kWh
        return energy_kwh * 0.10
 
    def get_historical_performance(self, task_type) -> Dict:
        """Get historical performance data for task type"""
        return self.performance_data.get(task_type, {
            'success_rate': 0.8,
            'avg_quality': 7.0,
            'avg_duration': 3600
        })

# src/optimized_models.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import tensorflow as tf
from tensorflow.keras import layers, Model
 
class OptimizedResNet(nn.Module):
    """Optimized ResNet for AI-Mining tasks with mixed precision support"""
 
    def __init__(self, num_classes=10, use_mixed_precision=True):
        super(OptimizedResNet, self).__init__()
        self.use_mixed_precision = use_mixed_precision
 
        # Efficient stem
        self.stem = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
 
        # Residual blocks with different channel sizes
        self.layer1 = self._make_layer(64, 64, 2)
        self.layer2 = self._make_layer(64, 128, 2, stride=2)
        self.layer3 = self._make_layer(128, 256, 2, stride=2)
        self.layer4 = self._make_layer(256, 512, 2, stride=2)
 
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)
 
        # Initialize weights
        self._initialize_weights()
 
    def _make_layer(self, in_channels, out_channels, blocks, stride=1):
        layers = []
        layers.append(OptimizedBasicBlock(in_channels, out_channels, stride))
 
        for _ in range(1, blocks):
            layers.append(OptimizedBasicBlock(out_channels, out_channels))
 
        return nn.Sequential(*layers)
 
    def forward(self, x):
        with torch.cuda.amp.autocast(enabled=self.use_mixed_precision):
            x = self.stem(x)
            x = self.layer1(x)
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)
 
            x = self.avgpool(x)
            x = torch.flatten(x, 1)
            x = self.fc(x)
 
        return x
 
    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
 
class OptimizedBasicBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(OptimizedBasicBlock, self).__init__()
 
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
                              stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
                              stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
 
        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )
 
    def forward(self, x):
        residual = self.shortcut(x)
 
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += residual
        out = F.relu(out)
 
        return out
 
class EfficientTransformer(nn.Module):
    """Memory-efficient transformer for NLP tasks"""
 
    def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6, max_seq_len=512):
        super(EfficientTransformer, self).__init__()
 
        self.d_model = d_model
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.pos_encoding = self._generate_positional_encoding(max_seq_len, d_model)
 
        # Use memory-efficient attention
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model,
            nhead=nhead,
            dim_feedforward=d_model * 4,
            dropout=0.1,
            activation='gelu',
            batch_first=True
        )
 
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
        self.output_layer = nn.Linear(d_model, vocab_size)
 
        # Gradient checkpointing for memory efficiency
        self.use_checkpoint = True
 
    def _generate_positional_encoding(self, max_len, d_model):
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
 
        div_term = torch.exp(torch.arange(0, d_model, 2).float() *
                           (-torch.log(torch.tensor(10000.0)) / d_model))
 
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
 
        return pe.unsqueeze(0)
 
    def forward(self, x, mask=None):
        seq_len = x.size(1)
 
        # Add positional encoding
        x = self.embedding(x) * torch.sqrt(torch.tensor(self.d_model, dtype=torch.float))
        x = x + self.pos_encoding[:, :seq_len, :].to(x.device)
 
        # Apply transformer with gradient checkpointing
        if self.use_checkpoint and self.training:
            x = torch.utils.checkpoint.checkpoint(self.transformer, x, mask)
        else:
            x = self.transformer(x, mask)
 
        return self.output_layer(x)

# src/ensemble_methods.py
import torch
import numpy as np
from typing import List, Dict, Any
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score, mean_squared_error
 
class AdvancedEnsemble:
    """Advanced ensemble methods for maximizing quality scores"""
 
    def __init__(self, models: List[torch.nn.Module], weights: List[float] = None):
        self.models = models
        self.weights = weights or [1.0] * len(models)
        self.calibration_data = None
 
    def weighted_prediction(self, inputs):
        """Weighted ensemble prediction"""
        predictions = []
 
        for model, weight in zip(self.models, self.weights):
            with torch.no_grad():
                pred = model(inputs)
                if isinstance(pred, torch.Tensor):
                    pred = torch.softmax(pred, dim=-1)
                predictions.append(pred * weight)
 
        # Weighted average
        ensemble_pred = torch.stack(predictions).sum(dim=0) / sum(self.weights)
 
        return ensemble_pred
 
    def uncertainty_weighted_prediction(self, inputs):
        """Uncertainty-based ensemble weighting"""
        predictions = []
        uncertainties = []
 
        for model in self.models:
            # Enable dropout for uncertainty estimation
            model.train()
 
            # Multiple forward passes
            mc_predictions = []
            for _ in range(10):
                with torch.no_grad():
                    pred = model(inputs)
                    mc_predictions.append(torch.softmax(pred, dim=-1))
 
            model.eval()
 
            # Calculate mean and uncertainty
            mc_predictions = torch.stack(mc_predictions)
            mean_pred = mc_predictions.mean(dim=0)
            uncertainty = mc_predictions.std(dim=0).mean(dim=-1)
 
            predictions.append(mean_pred)
            uncertainties.append(uncertainty)
 
        # Weight by inverse uncertainty
        uncertainties = torch.stack(uncertainties)
        weights = 1.0 / (uncertainties + 1e-6)
        weights = weights / weights.sum(dim=0)
 
        # Weighted prediction
        predictions = torch.stack(predictions)
        ensemble_pred = (predictions * weights.unsqueeze(-1)).sum(dim=0)
 
        return ensemble_pred
 
    def temperature_scaling_calibration(self, validation_loader):
        """Calibrate model confidence using temperature scaling"""
        all_outputs = []
        all_labels = []
 
        # Collect validation data
        for inputs, labels in validation_loader:
            outputs = self.weighted_prediction(inputs)
            all_outputs.append(outputs)
            all_labels.append(labels)
 
        all_outputs = torch.cat(all_outputs, dim=0)
        all_labels = torch.cat(all_labels, dim=0)
 
        # Find optimal temperature
        temperature = nn.Parameter(torch.ones(1) * 1.5)
        optimizer = torch.optim.LBFGS([temperature], lr=0.01, max_iter=50)
 
        def eval():
            optimizer.zero_grad()
            scaled_outputs = all_outputs / temperature
            loss = F.cross_entropy(scaled_outputs, all_labels)
            loss.backward()
            return loss
 
        optimizer.step(eval)
 
        self.temperature = temperature.item()
        print(f"Optimal temperature: {self.temperature:.3f}")
 
    def adaptive_ensemble_weights(self, validation_loader):
        """Learn optimal ensemble weights on validation data"""
        # Collect individual model predictions
        model_predictions = [[] for _ in self.models]
        all_labels = []
 
        for inputs, labels in validation_loader:
            for i, model in enumerate(self.models):
                with torch.no_grad():
                    pred = model(inputs)
                    model_predictions[i].append(torch.softmax(pred, dim=-1))
            all_labels.append(labels)
 
        # Concatenate predictions
        for i in range(len(self.models)):
            model_predictions[i] = torch.cat(model_predictions[i], dim=0)
        all_labels = torch.cat(all_labels, dim=0)
 
        # Optimize weights using validation performance
        weights = nn.Parameter(torch.ones(len(self.models)) / len(self.models))
        optimizer = torch.optim.Adam([weights], lr=0.01)
 
        for epoch in range(100):
            optimizer.zero_grad()
 
            # Normalize weights
            normalized_weights = F.softmax(weights, dim=0)
 
            # Weighted ensemble prediction
            ensemble_pred = sum(pred * w for pred, w in zip(model_predictions, normalized_weights))
 
            # Loss
            loss = F.cross_entropy(torch.log(ensemble_pred + 1e-8), all_labels)
            loss.backward()
            optimizer.step()
 
        self.weights = F.softmax(weights, dim=0).detach().tolist()
        print(f"Optimized weights: {self.weights}")
 
class QualityScoreOptimizer:
    """Optimize solutions specifically for TOS quality scoring"""
 
    def __init__(self, base_model, task_type):
        self.base_model = base_model
        self.task_type = task_type
        self.quality_history = []
 
    def optimize_for_quality(self, data, target_score=8.5):
        """Optimize solution to achieve target quality score"""
 
        if self.task_type == 'classification':
            return self._optimize_classification(data, target_score)
        elif self.task_type == 'regression':
            return self._optimize_regression(data, target_score)
        elif self.task_type == 'generation':
            return self._optimize_generation(data, target_score)
        else:
            return self._generic_optimization(data, target_score)
 
    def _optimize_classification(self, data, target_score):
        """Optimize classification task for quality"""
 
        # Cross-validation for robustness
        kfold = KFold(n_splits=5, shuffle=True, random_state=42)
        cv_scores = []
 
        for train_idx, val_idx in kfold.split(data['X']):
            X_train, X_val = data['X'][train_idx], data['X'][val_idx]
            y_train, y_val = data['y'][train_idx], data['y'][val_idx]
 
            # Train with augmentation
            model_copy = self._create_model_copy()
            augmented_X, augmented_y = self._augment_data(X_train, y_train)
 
            self._train_with_regularization(model_copy, augmented_X, augmented_y)
 
            # Validate
            predictions = model_copy(X_val)
            score = accuracy_score(y_val.cpu(), predictions.argmax(dim=1).cpu())
            cv_scores.append(score)
 
        # Use best performing model
        best_score = max(cv_scores)
 
        # Apply confidence calibration
        calibrated_predictions = self._calibrate_confidence(data['X'], data['y'])
 
        # Quality enhancement techniques
        if best_score < target_score:
            calibrated_predictions = self._apply_quality_boosting(
                calibrated_predictions, target_score - best_score
            )
 
        return {
            'predictions': calibrated_predictions,
            'confidence_score': best_score,
            'quality_enhancements': ['cross_validation', 'data_augmentation', 'calibration']
        }
 
    def _apply_quality_boosting(self, predictions, score_gap):
        """Apply techniques to boost quality score"""
 
        # Ensemble with multiple random seeds
        ensemble_predictions = []
 
        for seed in range(5):
            torch.manual_seed(seed)
            model_variant = self._create_model_copy()
            variant_pred = model_variant(predictions)
            ensemble_predictions.append(variant_pred)
 
        # Average ensemble
        boosted_predictions = torch.stack(ensemble_predictions).mean(dim=0)
 
        # Apply temperature scaling for better calibration
        temperature = 1.0 + score_gap * 0.5
        boosted_predictions = torch.softmax(boosted_predictions / temperature, dim=-1)
 
        return boosted_predictions
 
    def _create_model_copy(self):
        """Create a copy of the base model"""
        import copy
        return copy.deepcopy(self.base_model)
 
    def _augment_data(self, X, y):
        """Data augmentation techniques"""
        if len(X.shape) == 4:  # Image data
            # Image augmentations
            augmented_X = []
            augmented_y = []
 
            for x, label in zip(X, y):
                augmented_X.append(x)
                augmented_y.append(label)
 
                # Random rotation
                angle = torch.rand(1) * 30 - 15  # ±15 degrees
                rotated = self._rotate_image(x, angle)
                augmented_X.append(rotated)
                augmented_y.append(label)
 
                # Random noise
                noise = torch.randn_like(x) * 0.1
                noisy = torch.clamp(x + noise, 0, 1)
                augmented_X.append(noisy)
                augmented_y.append(label)
 
            return torch.stack(augmented_X), torch.stack(augmented_y)
        else:
            # Generic augmentation for other data types
            noise = torch.randn_like(X) * 0.05
            augmented_X = torch.cat([X, X + noise], dim=0)
            augmented_y = torch.cat([y, y], dim=0)
 
            return augmented_X, augmented_y
 
    def record_quality_score(self, score, task_info):
        """Record quality score for learning"""
        self.quality_history.append({
            'score': score,
            'task_type': task_info.get('type'),
            'difficulty': task_info.get('difficulty'),
            'timestamp': time.time(),
            'techniques_used': task_info.get('techniques', [])
        })
 
        # Analyze patterns for future optimization
        self._analyze_quality_patterns()
 
    def _analyze_quality_patterns(self):
        """Analyze historical data to improve future performance"""
        if len(self.quality_history) < 10:
            return
 
        recent_scores = [h['score'] for h in self.quality_history[-10:]]
        avg_score = np.mean(recent_scores)
 
        if avg_score < 8.0:
            print("Quality scores below target. Adjusting strategy...")
            # Implement adaptive strategy changes
        elif avg_score > 9.0:
            print("Excellent quality scores! Maintaining current strategy.")

# src/performance_monitor.py
import time
import psutil
import GPUtil
import matplotlib.pyplot as plt
from dataclasses import dataclass
from typing import List, Dict
import threading
import queue
 
@dataclass
class PerformanceMetrics:
    timestamp: float
    cpu_usage: float
    memory_usage: float
    gpu_usage: float
    gpu_memory: float
    power_consumption: float
    task_throughput: float
    quality_score: float
    earnings_rate: float
 
class RealTimeMonitor:
    def __init__(self, update_interval=5):
        self.update_interval = update_interval
        self.metrics_history = []
        self.is_monitoring = False
        self.metrics_queue = queue.Queue()
 
        # Performance targets
        self.targets = {
            'min_quality_score': 8.0,
            'max_power_consumption': 500,  # watts
            'min_earnings_rate': 2.0,      # TOS per hour
            'max_gpu_temperature': 80      # celsius
        }
 
    def start_monitoring(self):
        """Start real-time monitoring"""
        self.is_monitoring = True
        monitor_thread = threading.Thread(target=self._monitoring_loop)
        monitor_thread.daemon = True
        monitor_thread.start()
 
        print("Performance monitoring started")
 
    def stop_monitoring(self):
        """Stop monitoring"""
        self.is_monitoring = False
        print("Performance monitoring stopped")
 
    def _monitoring_loop(self):
        """Main monitoring loop"""
        while self.is_monitoring:
            try:
                metrics = self._collect_metrics()
                self.metrics_history.append(metrics)
                self.metrics_queue.put(metrics)
 
                # Check for alerts
                self._check_alerts(metrics)
 
                # Limit history size
                if len(self.metrics_history) > 1000:
                    self.metrics_history = self.metrics_history[-1000:]
 
                time.sleep(self.update_interval)
 
            except Exception as e:
                print(f"Monitoring error: {e}")
                time.sleep(self.update_interval)
 
    def _collect_metrics(self) -> PerformanceMetrics:
        """Collect current system metrics"""
 
        # CPU and Memory
        cpu_usage = psutil.cpu_percent(interval=1)
        memory = psutil.virtual_memory()
        memory_usage = memory.percent
 
        # GPU metrics
        gpu_usage = 0
        gpu_memory = 0
 
        try:
            gpus = GPUtil.getGPUs()
            if gpus:
                gpu = gpus[0]
                gpu_usage = gpu.load * 100
                gpu_memory = gpu.memoryUtil * 100
        except:
            pass
 
        # Power consumption (estimated)
        power_consumption = self._estimate_power_consumption(cpu_usage, gpu_usage)
 
        return PerformanceMetrics(
            timestamp=time.time(),
            cpu_usage=cpu_usage,
            memory_usage=memory_usage,
            gpu_usage=gpu_usage,
            gpu_memory=gpu_memory,
            power_consumption=power_consumption,
            task_throughput=self._calculate_throughput(),
            quality_score=self._get_current_quality_score(),
            earnings_rate=self._calculate_earnings_rate()
        )
 
    def _estimate_power_consumption(self, cpu_usage, gpu_usage):
        """Estimate power consumption based on usage"""
        base_power = 100  # Base system power
        cpu_power = (cpu_usage / 100) * 65  # CPU TDP
 
        gpu_power = 0
        if gpu_usage > 0:
            # Estimate based on GPU model
            gpu_power = (gpu_usage / 100) * 350  # Estimated GPU power
 
        return base_power + cpu_power + gpu_power
 
    def _check_alerts(self, metrics: PerformanceMetrics):
        """Check for performance alerts"""
        alerts = []
 
        if metrics.quality_score < self.targets['min_quality_score']:
            alerts.append(f"Quality score below target: {metrics.quality_score:.2f}")
 
        if metrics.power_consumption > self.targets['max_power_consumption']:
            alerts.append(f"Power consumption high: {metrics.power_consumption:.1f}W")
 
        if metrics.earnings_rate < self.targets['min_earnings_rate']:
            alerts.append(f"Earnings rate low: {metrics.earnings_rate:.2f} TOS/hr")
 
        if metrics.gpu_usage > 95:
            alerts.append("GPU usage critically high")
 
        if metrics.memory_usage > 90:
            alerts.append("Memory usage critically high")
 
        for alert in alerts:
            print(f"⚠️ ALERT: {alert}")
 
    def generate_performance_report(self) -> Dict:
        """Generate comprehensive performance report"""
        if not self.metrics_history:
            return {"error": "No performance data available"}
 
        recent_metrics = self.metrics_history[-100:]  # Last 100 readings
 
        report = {
            'monitoring_period': {
                'start': recent_metrics[0].timestamp,
                'end': recent_metrics[-1].timestamp,
                'duration_hours': (recent_metrics[-1].timestamp - recent_metrics[0].timestamp) / 3600
            },
 
            'averages': {
                'cpu_usage': np.mean([m.cpu_usage for m in recent_metrics]),
                'memory_usage': np.mean([m.memory_usage for m in recent_metrics]),
                'gpu_usage': np.mean([m.gpu_usage for m in recent_metrics]),
                'power_consumption': np.mean([m.power_consumption for m in recent_metrics]),
                'quality_score': np.mean([m.quality_score for m in recent_metrics]),
                'earnings_rate': np.mean([m.earnings_rate for m in recent_metrics])
            },
 
            'efficiency_metrics': {
                'power_efficiency': np.mean([m.earnings_rate / m.power_consumption
                                           for m in recent_metrics if m.power_consumption > 0]),
                'quality_consistency': np.std([m.quality_score for m in recent_metrics]),
                'uptime_percentage': self._calculate_uptime()
            },
 
            'recommendations': self._generate_recommendations(recent_metrics)
        }
 
        return report
 
    def _generate_recommendations(self, metrics: List[PerformanceMetrics]) -> List[str]:
        """Generate optimization recommendations"""
        recommendations = []
 
        avg_quality = np.mean([m.quality_score for m in metrics])
        avg_power = np.mean([m.power_consumption for m in metrics])
        avg_earnings = np.mean([m.earnings_rate for m in metrics])
 
        if avg_quality < 8.0:
            recommendations.append("Consider enabling ensemble methods to improve quality scores")
 
        if avg_power > 400:
            recommendations.append("High power consumption detected. Consider optimizing GPU settings")
 
        if avg_earnings < 2.0:
            recommendations.append("Low earnings rate. Review task selection strategy")
 
        gpu_usage_variation = np.std([m.gpu_usage for m in metrics])
        if gpu_usage_variation > 20:
            recommendations.append("Inconsistent GPU usage. Check for task scheduling issues")
 
        return recommendations
 
    def plot_performance_dashboard(self):
        """Create visual performance dashboard"""
        if len(self.metrics_history) < 10:
            print("Insufficient data for plotting")
            return
 
        recent_metrics = self.metrics_history[-100:]
        timestamps = [m.timestamp for m in recent_metrics]
 
        fig, axes = plt.subplots(2, 3, figsize=(15, 10))
        fig.suptitle('AI-Mining Performance Dashboard', fontsize=16)
 
        # CPU Usage
        axes[0, 0].plot(timestamps, [m.cpu_usage for m in recent_metrics])
        axes[0, 0].set_title('CPU Usage (%)')
        axes[0, 0].set_ylim(0, 100)
 
        # GPU Usage
        axes[0, 1].plot(timestamps, [m.gpu_usage for m in recent_metrics])
        axes[0, 1].set_title('GPU Usage (%)')
        axes[0, 1].set_ylim(0, 100)
 
        # Power Consumption
        axes[0, 2].plot(timestamps, [m.power_consumption for m in recent_metrics])
        axes[0, 2].set_title('Power Consumption (W)')
 
        # Quality Score
        axes[1, 0].plot(timestamps, [m.quality_score for m in recent_metrics])
        axes[1, 0].axhline(y=8.0, color='r', linestyle='--', label='Target')
        axes[1, 0].set_title('Quality Score')
        axes[1, 0].set_ylim(0, 10)
        axes[1, 0].legend()
 
        # Earnings Rate
        axes[1, 1].plot(timestamps, [m.earnings_rate for m in recent_metrics])
        axes[1, 1].set_title('Earnings Rate (TOS/hr)')
 
        # Efficiency
        efficiency = [m.earnings_rate / m.power_consumption * 1000
                     for m in recent_metrics if m.power_consumption > 0]
        axes[1, 2].plot(timestamps[:len(efficiency)], efficiency)
        axes[1, 2].set_title('Efficiency (TOS/kWh)')
 
        plt.tight_layout()
        plt.savefig('performance_dashboard.png', dpi=300)
        plt.show()
 
        print("Performance dashboard saved as 'performance_dashboard.png'")

# src/auto_optimizer.py
import numpy as np
from scipy.optimize import minimize
import torch
import time
from typing import Dict, List, Tuple
 
class AutomaticOptimizer:
    """Automatically optimize AI-Mining parameters for maximum efficiency"""
 
    def __init__(self, client, monitor):
        self.client = client
        self.monitor = monitor
        self.optimization_history = []
 
        # Parameter bounds
        self.parameter_bounds = {
            'learning_rate': (1e-5, 1e-2),
            'batch_size': (8, 128),
            'model_complexity': (0.5, 2.0),
            'ensemble_size': (3, 10),
            'gpu_memory_fraction': (0.5, 0.95),
            'task_selection_threshold': (0.1, 0.9)
        }
 
        # Current parameters
        self.current_params = {
            'learning_rate': 1e-3,
            'batch_size': 32,
            'model_complexity': 1.0,
            'ensemble_size': 5,
            'gpu_memory_fraction': 0.8,
            'task_selection_threshold': 0.6
        }
 
    def optimize_parameters(self, optimization_target='earnings_per_watt'):
        """Automatically optimize parameters using Bayesian optimization"""
 
        print("Starting automatic parameter optimization...")
 
        # Define objective function
        def objective(params_array):
            return -self._evaluate_parameter_set(params_array, optimization_target)
 
        # Convert current parameters to array
        param_names = list(self.current_params.keys())
        initial_params = np.array([self.current_params[name] for name in param_names])
        bounds = [self.parameter_bounds[name] for name in param_names]
 
        # Bayesian optimization
        result = minimize(
            objective,
            initial_params,
            method='L-BFGS-B',
            bounds=bounds,
            options={'maxiter': 20, 'disp': True}
        )
 
        # Update parameters
        optimized_params = {name: value for name, value in zip(param_names, result.x)}
        self._apply_parameters(optimized_params)
 
        print(f"Optimization complete. Best {optimization_target}: {-result.fun:.4f}")
        print(f"Optimized parameters: {optimized_params}")
 
        return optimized_params
 
    def _evaluate_parameter_set(self, params_array, target) -> float:
        """Evaluate a parameter set and return the target metric"""
 
        param_names = list(self.current_params.keys())
        params = {name: value for name, value in zip(param_names, params_array)}
 
        # Apply parameters temporarily
        old_params = self.current_params.copy()
        self._apply_parameters(params)
 
        try:
            # Run evaluation period
            start_time = time.time()
            evaluation_duration = 300  # 5 minutes
 
            metrics_sum = {
                'quality_score': 0,
                'earnings_rate': 0,
                'power_consumption': 0,
                'task_completion_rate': 0
            }
 
            measurement_count = 0
 
            while time.time() - start_time < evaluation_duration:
                # Get current metrics
                current_metrics = self.monitor._collect_metrics()
 
                metrics_sum['quality_score'] += current_metrics.quality_score
                metrics_sum['earnings_rate'] += current_metrics.earnings_rate
                metrics_sum['power_consumption'] += current_metrics.power_consumption
 
                measurement_count += 1
                time.sleep(10)  # Measure every 10 seconds
 
            # Calculate averages
            if measurement_count > 0:
                for key in metrics_sum:
                    metrics_sum[key] /= measurement_count
 
            # Calculate target metric
            if target == 'earnings_per_watt':
                if metrics_sum['power_consumption'] > 0:
                    score = metrics_sum['earnings_rate'] / (metrics_sum['power_consumption'] / 1000)
                else:
                    score = 0
            elif target == 'quality_score':
                score = metrics_sum['quality_score']
            elif target == 'earnings_rate':
                score = metrics_sum['earnings_rate']
            else:
                score = metrics_sum['quality_score'] * metrics_sum['earnings_rate']
 
            # Record optimization attempt
            self.optimization_history.append({
                'params': params.copy(),
                'score': score,
                'target': target,
                'timestamp': time.time()
            })
 
            return score
 
        except Exception as e:
            print(f"Evaluation error: {e}")
            return 0.0
 
        finally:
            # Restore old parameters
            self._apply_parameters(old_params)
 
    def _apply_parameters(self, params: Dict):
        """Apply parameter changes to the system"""
 
        # Update current parameters
        self.current_params.update(params)
 
        # Apply GPU memory settings
        if 'gpu_memory_fraction' in params:
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
                # Note: In practice, you might need to restart the process
                # to change GPU memory allocation
 
        # Apply model parameters
        if 'learning_rate' in params or 'batch_size' in params:
            # Update model training parameters
            self._update_model_parameters(params)
 
        # Apply task selection parameters
        if 'task_selection_threshold' in params:
            self._update_task_selection(params['task_selection_threshold'])
 
        print(f"Applied parameters: {params}")
 
    def _update_model_parameters(self, params):
        """Update model training parameters"""
        # This would update the model configuration
        # Implementation depends on your specific model setup
        pass
 
    def _update_task_selection(self, threshold):
        """Update task selection threshold"""
        # Update task selector threshold
        pass
 
    def adaptive_learning_rate_schedule(self, base_lr=1e-3):
        """Implement adaptive learning rate based on performance"""
 
        if len(self.optimization_history) < 5:
            return base_lr
 
        # Analyze recent performance
        recent_scores = [h['score'] for h in self.optimization_history[-5:]]
        score_trend = np.polyfit(range(len(recent_scores)), recent_scores, 1)[0]
 
        # Adjust learning rate based on trend
        if score_trend > 0:
            # Performance improving, maintain or slightly increase LR
            adjusted_lr = base_lr * 1.1
        else:
            # Performance declining, reduce LR
            adjusted_lr = base_lr * 0.9
 
        # Clip to bounds
        lr_bounds = self.parameter_bounds['learning_rate']
        adjusted_lr = np.clip(adjusted_lr, lr_bounds[0], lr_bounds[1])
 
        return adjusted_lr
 
    def schedule_optimization(self, interval_hours=6):
        """Schedule periodic optimization"""
 
        def optimization_scheduler():
            while True:
                try:
                    print("Starting scheduled optimization...")
 
                    # Try different optimization targets
                    targets = ['earnings_per_watt', 'quality_score', 'earnings_rate']
                    best_score = -float('inf')
                    best_params = None
 
                    for target in targets:
                        params = self.optimize_parameters(target)
                        score = self._evaluate_parameter_set(
                            [params[k] for k in self.current_params.keys()],
                            target
                        )
 
                        if score > best_score:
                            best_score = score
                            best_params = params
 
                    if best_params:
                        self._apply_parameters(best_params)
                        print(f"Applied best parameters with score: {best_score:.4f}")
 
                    # Sleep until next optimization
                    time.sleep(interval_hours * 3600)
 
                except Exception as e:
                    print(f"Scheduled optimization error: {e}")
                    time.sleep(3600)  # Wait 1 hour before retry
 
        # Start scheduler in background thread
        import threading
        scheduler_thread = threading.Thread(target=optimization_scheduler)
        scheduler_thread.daemon = True
        scheduler_thread.start()
 
        print(f"Optimization scheduler started (interval: {interval_hours} hours)")
 
    def generate_optimization_report(self) -> Dict:
        """Generate optimization performance report"""
 
        if not self.optimization_history:
            return {"error": "No optimization history available"}
 
        history = self.optimization_history
 
        # Best performing parameters by target
        best_by_target = {}
        for target in ['earnings_per_watt', 'quality_score', 'earnings_rate']:
            target_history = [h for h in history if h['target'] == target]
            if target_history:
                best = max(target_history, key=lambda x: x['score'])
                best_by_target[target] = {
                    'params': best['params'],
                    'score': best['score'],
                    'timestamp': best['timestamp']
                }
 
        # Parameter sensitivity analysis
        param_sensitivity = self._analyze_parameter_sensitivity()
 
        report = {
            'optimization_runs': len(history),
            'best_parameters_by_target': best_by_target,
            'parameter_sensitivity': param_sensitivity,
            'current_parameters': self.current_params,
            'recommendations': self._generate_optimization_recommendations()
        }
 
        return report
 
    def _analyze_parameter_sensitivity(self) -> Dict:
        """Analyze which parameters have the most impact on performance"""
 
        if len(self.optimization_history) < 10:
            return {}
 
        sensitivity = {}
 
        for param_name in self.current_params.keys():
            param_values = []
            scores = []
 
            for h in self.optimization_history:
                if param_name in h['params']:
                    param_values.append(h['params'][param_name])
                    scores.append(h['score'])
 
            if len(param_values) > 5:
                # Calculate correlation between parameter value and score
                correlation = np.corrcoef(param_values, scores)[0, 1]
                sensitivity[param_name] = {
                    'correlation': correlation,
                    'impact': abs(correlation),
                    'recommendation': 'increase' if correlation > 0 else 'decrease'
                }
 
        return sensitivity
 
    def _generate_optimization_recommendations(self) -> List[str]:
        """Generate recommendations based on optimization history"""
 
        recommendations = []
 
        if len(self.optimization_history) < 5:
            recommendations.append("Run more optimization cycles for better insights")
            return recommendations
 
        # Analyze recent trends
        recent_scores = [h['score'] for h in self.optimization_history[-10:]]
        score_improvement = (recent_scores[-1] - recent_scores[0]) / recent_scores[0] * 100
 
        if score_improvement > 10:
            recommendations.append("Optimization is working well. Continue current strategy.")
        elif score_improvement < -5:
            recommendations.append("Performance declining. Consider manual review of parameters.")
        else:
            recommendations.append("Performance stable. Try more aggressive optimization.")
 
        # Parameter-specific recommendations
        sensitivity = self._analyze_parameter_sensitivity()
 
        for param, data in sensitivity.items():
            if data['impact'] > 0.5:
                recommendations.append(
                    f"Parameter '{param}' has high impact. Consider {data['recommendation']}ing it."
                )
 
        return recommendations

# Monitor GPU usage
nvidia-smi -l 1
 
# Check memory usage
ps aux --sort=-%mem | head
 
# Monitor network traffic
iftop
 
# Check disk I/O
iotop

# Enable multi-GPU training
if torch.cuda.device_count() > 1:
    model = nn.DataParallel(model)
    print(f"Using {torch.cuda.device_count()} GPUs")