Performance Optimization Guide

This comprehensive guide covers performance optimization strategies for Chutes applications, from basic optimizations to advanced scaling techniques.

Overview

Performance optimization in Chutes involves several key areas:

Model Optimization: Optimizing AI model inference speed and memory usage
Resource Management: Efficient use of GPU, CPU, and memory resources
Scaling Strategies: Horizontal and vertical scaling approaches
Caching: Implementing effective caching strategies
Network Optimization: Reducing latency and improving throughput

Model Performance Optimization

Model Quantization

Reduce model size and increase inference speed:

from chutes.image import Image
from chutes.chute import Chute, NodeSelector

# Quantized model image
quantized_image = (
    Image(
        username="myuser",
        name="quantized-model",
        tag="1.0.0",
        python_version="3.11"
    )
    .pip_install([
        "torch==2.1.0",
        "transformers==4.35.0",
        "optimum[onnxruntime]==1.14.0",
        "bitsandbytes==0.41.0"
    ])
)

# Example: 8-bit quantization
def load_quantized_model():
    from transformers import AutoModelForCausalLM, BitsAndBytesConfig

    quantization_config = BitsAndBytesConfig(
        load_in_8bit=True,
        llm_int8_threshold=6.0,
        llm_int8_has_fp16_weight=False
    )

    model = AutoModelForCausalLM.from_pretrained(
        "microsoft/DialoGPT-medium",
        quantization_config=quantization_config,
        device_map="auto"
    )

    return model

async def run_quantized_inference(inputs):
    model = load_quantized_model()
    # Inference logic here
    return {"result": "optimized inference"}

Model Compilation with TorchScript

Optimize model execution:

import torch
from transformers import AutoModel, AutoTokenizer

class OptimizedModel:
    def __init__(self, model_name: str):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModel.from_pretrained(model_name)

        # Compile model for better performance
        self.model = torch.jit.script(self.model)
        self.model.eval()

    @torch.no_grad()
    def predict(self, text: str):
        inputs = self.tokenizer(text, return_tensors="pt")
        outputs = self.model(**inputs)
        return outputs

# Use in chute
optimized_model = OptimizedModel("bert-base-uncased")

async def run_optimized(inputs):
    result = optimized_model.predict(inputs["text"])
    return {"embeddings": result.last_hidden_state.mean(dim=1).tolist()}

Batch Processing

Process multiple inputs efficiently:

from typing import List
import torch

class BatchProcessor:
    def __init__(self, model, tokenizer, max_batch_size: int = 32):
        self.model = model
        self.tokenizer = tokenizer
        self.max_batch_size = max_batch_size

    async def process_batch(self, texts: List[str]):
        results = []

        # Process in batches
        for i in range(0, len(texts), self.max_batch_size):
            batch = texts[i:i + self.max_batch_size]

            # Tokenize batch
            inputs = self.tokenizer(
                batch,
                padding=True,
                truncation=True,
                return_tensors="pt",
                max_length=512
            )

            # Process batch
            with torch.no_grad():
                outputs = self.model(**inputs)

            # Extract results
            batch_results = outputs.last_hidden_state.mean(dim=1)
            results.extend(batch_results.tolist())

        return results

async def run_batch_processing(inputs):
    processor = BatchProcessor(model, tokenizer)
    results = await processor.process_batch(inputs["texts"])
    return {"embeddings": results}

Resource Optimization

GPU Memory Management

Optimize GPU memory usage:

import torch
import gc

class MemoryOptimizedModel:
    def __init__(self):
        self.model = None
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    def load_model_on_demand(self, model_name: str):
        """Load model only when needed"""
        if self.model is None:
            self.model = AutoModel.from_pretrained(model_name)
            self.model.to(self.device)
            self.model.eval()

    def clear_cache(self):
        """Clear GPU cache"""
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        gc.collect()

    @torch.no_grad()
    def predict(self, inputs):
        # Enable gradient checkpointing for memory efficiency
        if hasattr(self.model, 'gradient_checkpointing_enable'):
            self.model.gradient_checkpointing_enable()

        result = self.model(**inputs)

        # Clear intermediate tensors
        del inputs
        self.clear_cache()

        return result

# Configure for memory optimization
torch.backends.cudnn.benchmark = True  # Optimize for fixed input sizes
torch.backends.cudnn.deterministic = False  # Allow non-deterministic for speed

CPU Optimization

Optimize CPU-bound operations:

import multiprocessing as mp
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import asyncio

class CPUOptimizedProcessor:
    def __init__(self, num_workers: int = None):
        self.num_workers = num_workers or mp.cpu_count()
        self.thread_pool = ThreadPoolExecutor(max_workers=self.num_workers)
        self.process_pool = ProcessPoolExecutor(max_workers=self.num_workers)

    async def process_cpu_intensive(self, data_list):
        """Process CPU-intensive tasks in parallel"""
        loop = asyncio.get_event_loop()

        # Use process pool for CPU-bound tasks
        tasks = [
            loop.run_in_executor(self.process_pool, self.cpu_task, data)
            for data in data_list
        ]

        results = await asyncio.gather(*tasks)
        return results

    async def process_io_bound(self, urls):
        """Process I/O-bound tasks in parallel"""
        loop = asyncio.get_event_loop()

        # Use thread pool for I/O-bound tasks
        tasks = [
            loop.run_in_executor(self.thread_pool, self.io_task, url)
            for url in urls
        ]

        results = await asyncio.gather(*tasks)
        return results

    def cpu_task(self, data):
        # CPU-intensive processing
        return data ** 2

    def io_task(self, url):
        # I/O operation
        import requests
        response = requests.get(url)
        return response.status_code

Caching Strategies

Redis Caching

Implement efficient caching:

import redis
import pickle
import hashlib
from typing import Optional, Any
import asyncio

class CacheManager:
    def __init__(self, redis_url: str = "redis://localhost:6379"):
        self.redis_client = redis.from_url(redis_url)
        self.default_ttl = 3600  # 1 hour

    def generate_cache_key(self, prefix: str, *args) -> str:
        """Generate deterministic cache key"""
        key_data = str(args)
        key_hash = hashlib.md5(key_data.encode()).hexdigest()
        return f"{prefix}:{key_hash}"

    async def get_cached(self, key: str) -> Optional[Any]:
        """Get value from cache"""
        try:
            cached_data = self.redis_client.get(key)
            if cached_data:
                return pickle.loads(cached_data)
        except Exception as e:
            print(f"Cache read error: {e}")
        return None

    async def set_cached(self, key: str, value: Any, ttl: int = None):
        """Set value in cache"""
        try:
            ttl = ttl or self.default_ttl
            serialized_data = pickle.dumps(value)
            self.redis_client.setex(key, ttl, serialized_data)
        except Exception as e:
            print(f"Cache write error: {e}")

    async def cached_function(self, func, cache_prefix: str, ttl: int = None):
        """Decorator for caching function results"""
        async def wrapper(*args, **kwargs):
            # Generate cache key
            cache_key = self.generate_cache_key(cache_prefix, args, kwargs)

            # Try to get from cache
            cached_result = await self.get_cached(cache_key)
            if cached_result is not None:
                return cached_result

            # Execute function
            result = await func(*args, **kwargs)

            # Cache result
            await self.set_cached(cache_key, result, ttl)

            return result

        return wrapper

# Usage in chute
cache_manager = CacheManager()

@cache_manager.cached_function("model_inference", ttl=1800)
async def cached_inference(text: str):
    # Expensive model inference
    result = model.predict(text)
    return result

In-Memory Caching

Implement local caching:

from functools import lru_cache
import time
from typing import Dict, Any, Tuple

class MemoryCache:
    def __init__(self, max_size: int = 1000, ttl: int = 3600):
        self.cache: Dict[str, Tuple[Any, float]] = {}
        self.max_size = max_size
        self.ttl = ttl

    def get(self, key: str) -> Optional[Any]:
        if key in self.cache:
            value, timestamp = self.cache[key]
            if time.time() - timestamp < self.ttl:
                return value
            else:
                # Expired
                del self.cache[key]
        return None

    def set(self, key: str, value: Any):
        # Evict oldest if at capacity
        if len(self.cache) >= self.max_size:
            oldest_key = min(self.cache.keys(),
                           key=lambda k: self.cache[k][1])
            del self.cache[oldest_key]

        self.cache[key] = (value, time.time())

    def cached_method(self, func):
        """Decorator for caching method results"""
        def wrapper(*args, **kwargs):
            cache_key = f"{func.__name__}:{hash(str(args) + str(kwargs))}"

            # Try cache first
            cached_result = self.get(cache_key)
            if cached_result is not None:
                return cached_result

            # Execute and cache
            result = func(*args, **kwargs)
            self.set(cache_key, result)
            return result

        return wrapper

# Global cache instance
memory_cache = MemoryCache(max_size=500, ttl=1800)

@memory_cache.cached_method
def expensive_computation(data: str) -> str:
    # Simulate expensive operation
    time.sleep(1)
    return data.upper()

Scaling Strategies

Auto-scaling Configuration

Configure automatic scaling:

from chutes.chute import Chute, NodeSelector

# Auto-scaling chute configuration
high_performance_chute = Chute(
    username="myuser",
    name="high-performance-service",
    image=optimized_image,
    entry_file="optimized_service.py",
    entry_point="run",
    node_selector=NodeSelector(
        gpu_count=1,
        min_vram_gb_per_gpu=16preferred_provider="runpod"
    ),
    timeout_seconds=300,
    concurrency=20,

    # Auto-scaling settings
    auto_scale=True,
    min_instances=2,
    max_instances=10,
    scale_up_threshold=0.8,    # Scale up when 80% utilized
    scale_down_threshold=0.3,  # Scale down when <30% utilized
    scale_up_cooldown=300,     # Wait 5 min before scaling up again
    scale_down_cooldown=600    # Wait 10 min before scaling down
)

Load Balancing

Implement custom load balancing:

import random
import time
from typing import List, Dict

class LoadBalancer:
    def __init__(self, endpoints: List[str]):
        self.endpoints = endpoints
        self.health_status = {endpoint: True for endpoint in endpoints}
        self.response_times = {endpoint: 0.0 for endpoint in endpoints}
        self.request_counts = {endpoint: 0 for endpoint in endpoints}

    def get_endpoint(self, strategy: str = "round_robin") -> str:
        """Get next endpoint based on strategy"""
        healthy_endpoints = [
            ep for ep in self.endpoints
            if self.health_status[ep]
        ]

        if not healthy_endpoints:
            raise Exception("No healthy endpoints available")

        if strategy == "round_robin":
            return self._round_robin(healthy_endpoints)
        elif strategy == "least_connections":
            return self._least_connections(healthy_endpoints)
        elif strategy == "fastest_response":
            return self._fastest_response(healthy_endpoints)
        else:
            return random.choice(healthy_endpoints)

    def _round_robin(self, endpoints: List[str]) -> str:
        # Simple round-robin implementation
        min_requests = min(self.request_counts[ep] for ep in endpoints)
        candidates = [ep for ep in endpoints
                     if self.request_counts[ep] == min_requests]
        return candidates[0]

    def _least_connections(self, endpoints: List[str]) -> str:
        return min(endpoints, key=lambda ep: self.request_counts[ep])

    def _fastest_response(self, endpoints: List[str]) -> str:
        return min(endpoints, key=lambda ep: self.response_times[ep])

    def record_request(self, endpoint: str, response_time: float):
        """Record request metrics"""
        self.request_counts[endpoint] += 1
        # Exponential moving average
        self.response_times[endpoint] = (
            0.7 * self.response_times[endpoint] +
            0.3 * response_time
        )

    async def health_check(self):
        """Periodic health check"""
        import httpx

        async with httpx.AsyncClient() as client:
            for endpoint in self.endpoints:
                try:
                    response = await client.get(f"{endpoint}/health", timeout=5.0)
                    self.health_status[endpoint] = response.status_code == 200
                except:
                    self.health_status[endpoint] = False

# Usage with multiple chute instances
load_balancer = LoadBalancer([
    "https://chute1.example.com",
    "https://chute2.example.com",
    "https://chute3.example.com"
])

async def run_with_load_balancing(inputs):
    endpoint = load_balancer.get_endpoint("fastest_response")

    start_time = time.time()

    try:
        # Make request to selected endpoint
        import httpx
        async with httpx.AsyncClient() as client:
            response = await client.post(f"{endpoint}/run", json=inputs)
            result = response.json()

        # Record success
        response_time = time.time() - start_time
        load_balancer.record_request(endpoint, response_time)

        return result

    except Exception as e:
        # Mark endpoint as unhealthy on failure
        load_balancer.health_status[endpoint] = False
        raise e

Network Optimization

Response Compression

Reduce payload sizes:

import gzip
import json
from typing import Any

class ResponseOptimizer:
    @staticmethod
    def compress_response(data: Any, compression_threshold: int = 1024) -> dict:
        """Compress large responses"""
        json_data = json.dumps(data)

        if len(json_data) > compression_threshold:
            compressed_data = gzip.compress(json_data.encode())

            return {
                "compressed": True,
                "data": compressed_data.hex(),
                "original_size": len(json_data),
                "compressed_size": len(compressed_data)
            }
        else:
            return {
                "compressed": False,
                "data": data,
                "size": len(json_data)
            }

    @staticmethod
    def decompress_response(response: dict) -> Any:
        """Decompress response if needed"""
        if response.get("compressed", False):
            compressed_data = bytes.fromhex(response["data"])
            json_data = gzip.decompress(compressed_data).decode()
            return json.loads(json_data)
        else:
            return response["data"]

# Use in chute
async def run_optimized_response(inputs):
    # Process inputs
    result = await process_large_data(inputs)

    # Optimize response
    optimized_response = ResponseOptimizer.compress_response(result)

    return optimized_response

Streaming Responses

Stream large responses:

import asyncio
from typing import AsyncGenerator

async def stream_large_results(inputs) -> AsyncGenerator[dict, None]:
    """Stream results as they become available"""

    # Process data in chunks
    data_chunks = split_data_into_chunks(inputs["data"])

    for i, chunk in enumerate(data_chunks):
        # Process chunk
        result = await process_chunk(chunk)

        # Yield partial result
        yield {
            "chunk_id": i,
            "total_chunks": len(data_chunks),
            "result": result,
            "is_final": i == len(data_chunks) - 1
        }

        # Allow other tasks to run
        await asyncio.sleep(0)

async def run_streaming(inputs):
    """Handle streaming request"""
    if inputs.get("stream", False):
        # Return async generator for streaming
        return stream_large_results(inputs)
    else:
        # Return complete result
        return await process_all_data(inputs)

Monitoring and Profiling

Performance Metrics

Track performance metrics:

import time
import psutil
import torch
from prometheus_client import Counter, Histogram, Gauge, start_http_server

# Metrics
REQUEST_COUNT = Counter('requests_total', 'Total requests')
REQUEST_DURATION = Histogram('request_duration_seconds', 'Request duration')
MEMORY_USAGE = Gauge('memory_usage_bytes', 'Memory usage')
GPU_UTILIZATION = Gauge('gpu_utilization_percent', 'GPU utilization')

class PerformanceMonitor:
    def __init__(self):
        self.start_time = time.time()

        # Start metrics server
        start_http_server(8001)

    def track_request(self, func):
        """Decorator to track request metrics"""
        async def wrapper(*args, **kwargs):
            REQUEST_COUNT.inc()
            start_time = time.time()

            try:
                result = await func(*args, **kwargs)
                return result
            finally:
                duration = time.time() - start_time
                REQUEST_DURATION.observe(duration)

                # Update system metrics
                self.update_system_metrics()

        return wrapper

    def update_system_metrics(self):
        """Update system performance metrics"""
        # Memory usage
        memory_info = psutil.virtual_memory()
        MEMORY_USAGE.set(memory_info.used)

        # GPU utilization
        if torch.cuda.is_available():
            gpu_util = torch.cuda.utilization()
            GPU_UTILIZATION.set(gpu_util)

# Global monitor
monitor = PerformanceMonitor()

@monitor.track_request
async def run_monitored(inputs):
    # Your processing logic
    result = await process_inputs(inputs)
    return result

Profiling Tools

Profile code performance:

import cProfile
import pstats
import io
from contextlib import contextmanager

@contextmanager
def profile_code():
    """Context manager for profiling code blocks"""
    profiler = cProfile.Profile()
    profiler.enable()

    try:
        yield profiler
    finally:
        profiler.disable()

        # Get profile stats
        stream = io.StringIO()
        stats = pstats.Stats(profiler, stream=stream)
        stats.sort_stats('cumulative')
        stats.print_stats(20)  # Top 20 functions

        print("Performance Profile:")
        print(stream.getvalue())

# Usage
async def run_with_profiling(inputs):
    with profile_code():
        result = await expensive_operation(inputs)

    return result

# Memory profiling
from memory_profiler import profile

@profile
def memory_intensive_function(data):
    # Function that uses a lot of memory
    large_list = [data] * 1000000
    processed = [item.upper() for item in large_list]
    return processed[:10]

Best Practices Summary

Model Optimization

Use quantization for faster inference
Implement model compilation where possible
Process inputs in batches
Cache model outputs for repeated inputs

Resource Management

Monitor GPU memory usage
Implement proper cleanup
Use appropriate data types
Optimize for your specific hardware

Scaling

Configure auto-scaling based on your traffic patterns
Implement health checks
Use load balancing for high availability
Monitor performance metrics

Caching

Cache expensive computations
Use appropriate TTL values
Implement cache invalidation strategies
Monitor cache hit rates

Next Steps

Best Practices - General deployment best practices
Monitoring Guide - Advanced monitoring strategies
Cost Optimization - Optimize costs while maintaining performance
Scaling Guide - Advanced scaling strategies

For enterprise performance optimization, see the Enterprise Performance Guide.