Production Deployment

class OptimizedInference:
    def __init__(self, model, max_batch_size=8):
        self.model = model
        self.max_batch_size = max_batch_size
        self.request_queue = asyncio.Queue()
        self.response_futures = {}

    async def batch_processor(self):
        """Process requests in batches for efficiency"""
        while True:
            batch = []
            futures = []

            # Collect requests up to batch size
            while len(batch) < self.max_batch_size:
                try:
                    request = await asyncio.wait_for(
                        self.request_queue.get(),
                        timeout=0.01  # 10ms wait
                    )
                    batch.append(request['prompt'])
                    futures.append(request['future'])
                except asyncio.TimeoutError:
                    break

            if batch:
                # Process batch
                outputs = self.model.generate(batch)

                # Return results
                for future, output in zip(futures, outputs):
                    future.set_result(output)

    async def generate(self, prompt):
        """Queue request for batch processing"""
        future = asyncio.Future()
        await self.request_queue.put({
            'prompt': prompt,
            'future': future
        })
        return await future

class MultiLevelCache:
    def __init__(self):
        # L1: In-memory cache (fast, limited size)
        self.memory_cache = LRUCache(maxsize=1000)

        # L2: Redis cache (medium speed, larger)
        self.redis_cache = redis.Redis(
            host='localhost',
            port=6379
        )

        # L3: Disk cache (slow, unlimited)
        self.disk_cache = DiskCache('/var/cache/atlas')

    async def get(self, key):
        """Try caches in order of speed"""
        # L1: Memory
        value = self.memory_cache.get(key)
        if value:
            return value

        # L2: Redis
        value = self.redis_cache.get(key)
        if value:
            self.memory_cache[key] = value
            return value

        # L3: Disk
        value = self.disk_cache.get(key)
        if value:
            self.redis_cache.setex(key, 3600, value)
            self.memory_cache[key] = value
            return value

        return None

    async def set(self, key, value, ttl=3600):
        """Update all cache levels"""
        self.memory_cache[key] = value
        self.redis_cache.setex(key, ttl, value)
        self.disk_cache.set(key, value)

from prometheus_client import Counter, Histogram, Gauge
import time

# Define metrics
request_count = Counter(
    'atlas_requests_total',
    'Total requests processed',
    ['model', 'status']
)

request_duration = Histogram(
    'atlas_request_duration_seconds',
    'Request latency',
    ['model', 'operation']
)

model_memory = Gauge(
    'atlas_model_memory_bytes',
    'Model memory usage',
    ['model', 'device']
)

improvement_score = Histogram(
    'atlas_improvement_score',
    'Enhancement improvement scores',
    buckets=[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
)

class MonitoredATLAS:
    def __init__(self, atlas_instance):
        self.atlas = atlas_instance

    async def enhance_with_metrics(self, prompt):
        """Enhanced inference with monitoring"""
        start_time = time.time()

        try:
            result = await self.atlas.run_full_protocol(prompt)

            # Record success metrics
            request_count.labels(
                model='atlas',
                status='success'
            ).inc()

            improvement_score.observe(result.improvement_score)

            return result

        except Exception as e:
            # Record failure
            request_count.labels(
                model='atlas',
                status='error'
            ).inc()
            raise

        finally:
            # Record duration
            duration = time.time() - start_time
            request_duration.labels(
                model='atlas',
                operation='enhance'
            ).observe(duration)

import structlog
import json

# Configure structured logging
structlog.configure(
    processors=[
        structlog.stdlib.filter_by_level,
        structlog.stdlib.add_logger_name,
        structlog.stdlib.add_log_level,
        structlog.stdlib.PositionalArgumentsFormatter(),
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.StackInfoRenderer(),
        structlog.processors.format_exc_info,
        structlog.processors.UnicodeDecoder(),
        structlog.processors.JSONRenderer()
    ],
    context_class=dict,
    logger_factory=structlog.stdlib.LoggerFactory(),
    cache_logger_on_first_use=True,
)

logger = structlog.get_logger()

class LoggedInference:
    def __init__(self):
        self.logger = logger.bind(service="atlas")

    async def process_request(self, request_id, prompt):
        """Process with comprehensive logging"""
        log = self.logger.bind(request_id=request_id)

        log.info(
            "request_received",
            prompt_length=len(prompt),
            prompt_preview=prompt[:100]
        )

        try:
            # Diagnostic phase
            log.info("diagnostic_started")
            diagnosis = await self.diagnose(prompt)
            log.info(
                "diagnostic_complete",
                capability_score=diagnosis.score
            )

            # Guidance phase
            log.info("guidance_started")
            guidance = await self.generate_guidance(diagnosis)
            log.info(
                "guidance_complete",
                guidance_length=len(guidance)
            )

            # Enhancement phase
            log.info("enhancement_started")
            result = await self.enhance(prompt, guidance)
            log.info(
                "enhancement_complete",
                improvement=result.improvement_score,
                tokens_used=result.total_tokens
            )

            return result

        except Exception as e:
            log.error(
                "processing_failed",
                error=str(e),
                exc_info=True
            )
            raise

class CostOptimizedRouter:
    def __init__(self):
        # Models ordered by cost/capability
        self.models = [
            {'name': 'small', 'cost': 0.001, 'threshold': 0.3},
            {'name': 'medium', 'cost': 0.005, 'threshold': 0.6},
            {'name': 'large', 'cost': 0.02, 'threshold': 0.9}
        ]

    def select_model(self, prompt, max_cost=0.01):
        """Select most cost-effective model"""
        complexity = self.assess_complexity(prompt)

        for model in self.models:
            if model['cost'] <= max_cost and complexity <= model['threshold']:
                return model['name']

        # Default to smallest model if budget exceeded
        return self.models[0]['name']

    def assess_complexity(self, prompt):
        """Estimate query complexity"""
        indicators = {
            'length': len(prompt) / 1000,
            'technical_terms': self.count_technical_terms(prompt) / 10,
            'nested_logic': self.detect_nested_logic(prompt)
        }
        return np.mean(list(indicators.values()))

class SpotInstanceManager:
    def __init__(self, aws_client):
        self.ec2 = aws_client
        self.active_instances = []

    async def provision_spot_instance(self, instance_type='g5.xlarge'):
        """Request spot instance for inference"""
        response = self.ec2.request_spot_instances(
            InstanceCount=1,
            Type='one-time',
            LaunchSpecification={
                'ImageId': 'ami-atlas-inference',
                'InstanceType': instance_type,
                'KeyName': 'atlas-key',
                'SecurityGroups': ['atlas-sg'],
                'UserData': self.get_startup_script()
            },
            SpotPrice=str(self.calculate_bid_price(instance_type))
        )

        instance_id = response['SpotInstanceRequests'][0]['InstanceId']
        self.active_instances.append(instance_id)
        return instance_id

    def calculate_bid_price(self, instance_type):
        """Calculate optimal bid price"""
        history = self.ec2.describe_spot_price_history(
            InstanceTypes=[instance_type],
            MaxResults=100
        )
        prices = [float(h['SpotPrice']) for h in history['SpotPriceHistory']]
        return np.percentile(prices, 75)  # 75th percentile bid

from fastapi import FastAPI, Depends, HTTPException
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
import jwt

security = HTTPBearer()

class AuthHandler:
    def __init__(self, secret_key):
        self.secret = secret_key

    def decode_token(self, token):
        try:
            payload = jwt.decode(
                token,
                self.secret,
                algorithms=['HS256']
            )
            return payload
        except jwt.ExpiredSignatureError:
            raise HTTPException(401, 'Token expired')
        except jwt.InvalidTokenError:
            raise HTTPException(401, 'Invalid token')

auth_handler = AuthHandler(os.environ['JWT_SECRET'])

async def verify_token(
    credentials: HTTPAuthorizationCredentials = Depends(security)
):
    token = credentials.credentials
    return auth_handler.decode_token(token)

@app.post('/api/enhance', dependencies=[Depends(verify_token)])
async def enhance_endpoint(request: EnhanceRequest):
    # Authenticated request processing
    pass

from pydantic import BaseModel, validator, Field
import re

class SafePrompt(BaseModel):
    text: str = Field(..., max_length=4096)
    temperature: float = Field(0.7, ge=0.0, le=2.0)
    max_tokens: int = Field(512, ge=1, le=2048)

    @validator('text')
    def sanitize_text(cls, v):
        # Remove potential injection attempts
        v = re.sub(r'[<>]', '', v)  # Remove HTML
        v = re.sub(r'[\x00-\x08\x0B\x0C\x0E-\x1F]', '', v)  # Control chars
        return v

    @validator('text')
    def check_content(cls, v):
        # Content filtering
        blocked_patterns = [
            r'(?i)api[_\s]?key',
            r'(?i)password',
            r'(?i)secret'
        ]
        for pattern in blocked_patterns:
            if re.search(pattern, v):
                raise ValueError('Potentially sensitive content detected')
        return v

#!/bin/bash
# backup.sh

# Backup model weights
aws s3 sync /models s3://atlas-backups/models/$(date +%Y%m%d)/ \
    --exclude "*.pyc" \
    --exclude "__pycache__/*"

# Backup cache data
redis-cli BGSAVE
aws s3 cp /var/lib/redis/dump.rdb \
    s3://atlas-backups/cache/redis-$(date +%Y%m%d).rdb

# Backup configuration
kubectl get configmaps -o yaml > configs-$(date +%Y%m%d).yaml
aws s3 cp configs-*.yaml s3://atlas-backups/configs/

# Backup metrics
prometheus_backup --output s3://atlas-backups/metrics/

class FailoverManager:
    def __init__(self, primary_url, backup_url):
        self.primary = primary_url
        self.backup = backup_url
        self.use_primary = True

    async def health_check(self, url):
        """Check service health"""
        try:
            response = await httpx.get(f"{url}/health", timeout=5.0)
            return response.status_code == 200
        except:
            return False

    async def get_service_url(self):
        """Get active service URL with failover"""
        if self.use_primary:
            if await self.health_check(self.primary):
                return self.primary
            else:
                logger.warning("Primary service down, failing over")
                self.use_primary = False
                return self.backup
        else:
            # Try to restore primary
            if await self.health_check(self.primary):
                logger.info("Primary service restored")
                self.use_primary = True
                return self.primary
            return self.backup