Performance Optimization Guide

This comprehensive guide covers performance optimization strategies for Chutes applications, from model inference to network efficiency and resource management.

Overview

Performance optimization in Chutes involves several key areas:

Model Optimization: Quantization, compilation, and batching
Resource Management: Efficient GPU and memory usage
Scaling Strategies: Auto-scaling and load balancing
Caching: Reducing redundant computations
Network Optimization: Minimizing latency and payload size
Monitoring: Tracking metrics to identify bottlenecks

Model Inference Optimization

Dynamic Batching

Processing requests in batches significantly improves GPU utilization. Here's a robust dynamic batcher implementation:

import asyncio
import time
from typing import List, Dict, Any
from dataclasses import dataclass

@dataclass
class BatchRequest:
    data: Dict[str, Any]
    future: asyncio.Future
    timestamp: float

class DynamicBatcher:
    def __init__(self, max_batch_size: int = 32, max_wait_time: float = 0.01):
        self.max_batch_size = max_batch_size
        self.max_wait_time = max_wait_time
        self.pending_requests: List[BatchRequest] = []
        self.processing = False
        self.lock = asyncio.Lock()

    async def add_request(self, data: Dict[str, Any]) -> Any:
        """Add request to batch queue"""
        future = asyncio.Future()
        request = BatchRequest(data, future, time.time())

        async with self.lock:
            self.pending_requests.append(request)
            if not self.processing:
                asyncio.create_task(self._process_batch())

        return await future

    async def _process_batch(self):
        """Process accumulated requests"""
        async with self.lock:
            if self.processing or not self.pending_requests:
                return
            self.processing = True

        while True:
            # Wait for batch to accumulate or timeout
            start_time = time.time()
            while (len(self.pending_requests) < self.max_batch_size and
                   time.time() - start_time < self.max_wait_time):
                await asyncio.sleep(0.001)

            async with self.lock:
                if not self.pending_requests:
                    break
                
                # Extract batch
                batch_size = min(len(self.pending_requests), self.max_batch_size)
                batch = self.pending_requests[:batch_size]
                self.pending_requests = self.pending_requests[batch_size:]

            # Run inference
            try:
                batch_data = [req.data for req in batch]
                results = await self._run_inference(batch_data)
                
                for req, result in zip(batch, results):
                    if not req.future.done():
                        req.future.set_result(result)
            except Exception as e:
                for req in batch:
                    if not req.future.done():
                        req.future.set_exception(e)

        async with self.lock:
            self.processing = False

    async def _run_inference(self, batch_data: List[Dict]) -> List[Any]:
        """Override this with your actual inference logic"""
        # Example:
        # inputs = tokenizer([item["text"] for item in batch_data], padding=True, return_tensors="pt")
        # outputs = model(**inputs)
        # return outputs
        return [{"result": "mock_result"} for _ in batch_data]

Model Quantization

Reduce model size and memory footprint using quantization (e.g., 8-bit or 4-bit):

from chutes.image import Image

# Build image with quantization support
image = (
    Image(username="myuser", name="quantized-model", tag="1.0")
    .pip_install([
        "torch",
        "transformers",
        "bitsandbytes",  # Required for 8-bit/4-bit
        "accelerate"
    ])
)

# Loading a quantized model
def load_quantized_model():
    from transformers import AutoModelForCausalLM, BitsAndBytesConfig
    import torch

    quant_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.float16
    )

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

TorchScript Compilation

Compile PyTorch models for faster execution:

import torch

def optimize_model(model, example_input):
    # Trace the model
    traced_model = torch.jit.trace(model, example_input)
    return torch.jit.freeze(traced_model)

Resource Management

GPU Memory Management

Properly managing GPU memory is critical to avoid OOM errors and maximize throughput.

import torch
import gc
from contextlib import contextmanager

class GPUMemoryManager:
    @contextmanager
    def optimization_context(self):
        """Context manager to clear cache before and after operations"""
        self.cleanup()
        try:
            yield
        finally:
            self.cleanup()

    def cleanup(self):
        """Aggressive memory cleanup"""
        gc.collect()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            torch.cuda.synchronize()

    def get_usage(self):
        if not torch.cuda.is_available():
            return 0
        return torch.cuda.memory_allocated() / torch.cuda.max_memory_allocated()

# Usage
memory_manager = GPUMemoryManager()

async def run_inference(inputs):
    with memory_manager.optimization_context():
        # Run heavy inference here
        pass

Scaling Strategies

Auto-scaling Configuration

Configure your chute to scale automatically based on load:

from chutes.chute import Chute, NodeSelector

chute = Chute(
    # ... other args ...
    concurrency=10,  # Max concurrent requests per instance
    
    # Auto-scaling settings
    auto_scale=True,
    min_instances=1,
    max_instances=10,
    scale_up_threshold=0.8,    # Scale up when 80% concurrency reached
    scale_down_threshold=0.3,  # Scale down when <30% utilized
    scale_up_cooldown=60,      # Wait 60s before next scale up
    scale_down_cooldown=300    # Wait 5m before scaling down
)

Caching Strategies

Redis Caching

Use Redis for distributed caching across multiple chute instances:

import redis
import pickle
import hashlib

class CacheManager:
    def __init__(self, redis_url="redis://localhost:6379"):
        self.redis = redis.from_url(redis_url)

    def get_key(self, prefix, *args, **kwargs):
        key_str = str(args) + str(sorted(kwargs.items()))
        return f"{prefix}:{hashlib.md5(key_str.encode()).hexdigest()}"

    def get(self, key):
        data = self.redis.get(key)
        return pickle.loads(data) if data else None

    def set(self, key, value, ttl=3600):
        self.redis.setex(key, ttl, pickle.dumps(value))

# Decorator usage
def cached(ttl=3600):
    def decorator(func):
        async def wrapper(self, *args, **kwargs):
            key = self.cache.get_key(func.__name__, *args, **kwargs)
            result = self.cache.get(key)
            if result:
                return result
            
            result = await func(self, *args, **kwargs)
            self.cache.set(key, result, ttl)
            return result
        return wrapper
    return decorator

Network Optimization

Response Compression

Compress large JSON responses to reduce network transfer time:

import gzip
import json

def compress_response(data: dict) -> dict:
    json_str = json.dumps(data)
    if len(json_str) < 1024:  # Don't compress small responses
        return data
        
    compressed = gzip.compress(json_str.encode())
    return {
        "compressed": True,
        "data": compressed.hex()
    }

Streaming

For long-running generations (like LLMs), use streaming to provide immediate feedback. See the Streaming Guide for details.

Monitoring

Track performance metrics to identify bottlenecks.

import time
from prometheus_client import Histogram, Counter

REQUEST_TIME = Histogram('request_processing_seconds', 'Time spent processing request')
REQUEST_COUNT = Counter('request_count', 'Total request count')

@chute.cord(public_api_path="/run", method="POST")
async def run(self, data: dict):
    REQUEST_COUNT.inc()
    with REQUEST_TIME.time():
        # Process request
        return await self.process(data)

Next Steps

Cost Optimization: Balance performance with cost
Best Practices: General deployment guidelines
Streaming Guide: Implement real-time responses