Production Deployment
While Mini-YAIE is primarily educational, understanding production considerations helps bridge the gap between learning and real-world deployment.
Deployment Architecture
graph TD
LoadBalancer -->|HTTP| API1
LoadBalancer -->|HTTP| API2
LoadBalancer -->|HTTP| API3
API1 -->|gRPC| Inference1
API2 -->|gRPC| Inference2
API3 -->|gRPC| Inference3
Inference1 -->|GPU| Model1
Inference2 -->|GPU| Model2
Inference3 -->|GPU| Model3
1. Performance Optimization
Batching & Latency Management
- Request Timeouts: Implement configurable timeouts for requests waiting in queue
- Priority Queues: Support different priority levels for requests
- Preemption: Pause low-priority requests and swap their KV-cache to CPU when high-priority requests arrive
- Adaptive Batching: Dynamically adjust batch sizes based on current load and latency requirements
Continuous Batching Strategies
# Advanced scheduling policies
class SchedulingPolicy(Enum):
FCFS = "fcfs" # First-come-first-served
PRIORITY = "priority" # Priority-based
FAIR = "fair" # Fair sharing
LATENCY_OPTIMIZED = "latency" # Minimize latency
2. Distributed Inference
Tensor Parallelism
# Split model weights across multiple GPUs
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map={
"layer.0": "cuda:0",
"layer.1": "cuda:1",
"layer.2": "cuda:2",
"layer.3": "cuda:3"
}
)
Pipeline Parallelism
# Split layers across different stages
# Stage 1: Layers 0-10 on GPU 0
# Stage 2: Layers 11-20 on GPU 1
# Stage 3: Layers 21-30 on GPU 2
Model Parallelism
- Expert Parallelism: For mixture-of-experts models
- Sequence Parallelism: For very long sequences
- Hybrid Parallelism: Combining multiple strategies
3. Memory Optimization
Quantization
# Load model with quantization
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_8bit=True, # 8-bit quantization
device_map="auto"
)
# Or use 4-bit quantization
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_4bit=True,
device_map="auto"
)
Memory Management Strategies
- Paged Attention: Efficient memory management for KV-cache
- Memory Pooling: Reuse memory blocks across requests
- Swapping: Move inactive KV-cache blocks to CPU memory
- Compression: Compress KV-cache values with minimal quality loss
4. Scalability & Reliability
Horizontal Scaling
# Multiple inference workers behind load balancer
workers = [
InferenceWorker(model_name, gpu_id=0),
InferenceWorker(model_name, gpu_id=1),
InferenceWorker(model_name, gpu_id=2),
InferenceWorker(model_name, gpu_id=3)
]
Load Balancing
- Round Robin: Simple distribution across workers
- Least Connections: Send to least busy worker
- Latency-based: Send to worker with lowest current latency
- Content-based: Route based on request characteristics
Health Monitoring
# Health check endpoints
@app.get("/health")
def health_check():
return {
"status": "healthy",
"gpu_memory": get_gpu_memory_usage(),
"active_requests": scheduler.get_active_request_count(),
"queue_length": scheduler.get_queue_status()
}
5. Observability
Metrics Collection
# Prometheus metrics
REQUEST_LATENCY = Histogram(
'request_latency_seconds',
'Request latency in seconds',
buckets=[0.1, 0.5, 1.0, 2.5, 5.0, 10.0]
)
TOKENS_PER_SECOND = Counter(
'tokens_per_second',
'Tokens generated per second'
)
Logging
# Structured logging
logger.info(
"Request completed",
request_id=request.id,
latency=latency_seconds,
tokens_generated=token_count,
model=model_name
)
Tracing
# Distributed tracing
with tracer.start_span("generate_response") as span:
span.set_attribute("model", model_name)
span.set_attribute("request_id", request.id)
response = engine.generate(prompt)
6. Security
Authentication
# API key authentication
@app.post("/v1/chat/completions")
async def chat_completions(
request: ChatCompletionRequest,
api_key: str = Header(None)
):
if not validate_api_key(api_key):
raise HTTPException(status_code=401, detail="Unauthorized")
Rate Limiting
# Rate limiting per API key
limiter = RateLimiter(
requests_per_minute=1000,
burst_capacity=100
)
@app.post("/v1/chat/completions")
@limiter.limit("1000/minute")
async def chat_completions(request: ChatCompletionRequest):
# Process request
Input Validation
# Validate and sanitize inputs
@app.post("/v1/chat/completions")
async def chat_completions(request: ChatCompletionRequest):
# Validate prompt length
if len(request.prompt) > MAX_PROMPT_LENGTH:
raise HTTPException(status_code=400, detail="Prompt too long")
# Sanitize inputs
sanitized_prompt = sanitize_input(request.prompt)
7. Configuration Management
Environment Variables
# Configure through environment variables
export MODEL_NAME="gpt2"
export MAX_BATCH_SIZE="16"
export GPU_MEMORY_UTIL="0.9"
export ENABLE_RADIX_CACHE="true"
Configuration Files
# YAML configuration
model:
name: "gpt2"
dtype: "float16"
tensor_parallel_size: 1
scheduler:
max_batch_size: 16
max_prefill_batch_size: 32
max_decode_batch_size: 256
memory:
gpu_blocks: 2000
cpu_blocks: 1000
block_size: 16
8. Deployment Strategies
Containerization
# Dockerfile for Mini-YAIE
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
RUN pip install -e .
CMD ["yaie", "serve", "gpt2", "--host", "0.0.0.0", "--port", "8000"]
Kubernetes Deployment
# Kubernetes deployment
apiVersion: apps/v1
kind: Deployment
metadata:
name: yaie-inference
spec:
replicas: 3
selector:
matchLabels:
app: yaie-inference
template:
metadata:
labels:
app: yaie-inference
spec:
containers:
- name: yaie
image: yaie:latest
ports:
- containerPort: 8000
resources:
limits:
nvidia.com/gpu: 1
9. Performance Monitoring
Key Metrics to Track
- Latency: Time from request to first token (TTFT) and time per token
- Throughput: Tokens generated per second
- GPU Utilization: Percentage of GPU time spent on computation
- Memory Usage: GPU and CPU memory consumption
- Queue Length: Number of requests waiting
- Error Rates: Percentage of failed requests
Alerting
# Set up alerts for critical conditions
if gpu_memory_usage > 0.95:
alert("High GPU memory usage")
if request_latency > 5.0: # 5 seconds
alert("High request latency")
if error_rate > 0.01: # 1%
alert("High error rate")
10. Cost Optimization
Resource Management
- Auto-scaling: Scale workers based on demand
- Spot Instances: Use cheaper spot instances for non-critical workloads
- Right-sizing: Choose appropriate instance types for workload
- Batch Processing: Process offline requests in batches during low-traffic periods
Model Selection
# Choose appropriate model size for use case
small_models = ["gpt2", "DialoGPT-small"] # Fast, low cost
medium_models = ["gpt2-medium", "DialoGPT-medium"] # Balanced
large_models = ["gpt2-large", "DialoGPT-large"] # High quality, expensive
Educational Focus
Understanding production considerations helps you:
- Bridge the gap between educational implementations and real-world systems
- Appreciate the complexity of production-grade inference engines
- Make informed decisions about trade-offs in your implementations
- Design for scalability from the beginning
From Mini-YAIE to Production
Mini-YAIE provides the foundation for understanding key concepts:
- Continuous Batching: The core of efficient inference
- Memory Management: Critical for handling multiple requests
- Prefix Sharing: Advanced optimization for similar requests
- API Design: Standard interfaces for integration
Production systems build on these foundations with:
- Scalability: Handling thousands of concurrent requests
- Reliability: High availability and fault tolerance
- Observability: Comprehensive monitoring and logging
- Security: Authentication, authorization, and input validation
By mastering the concepts in Mini-YAIE, you’ll be well-prepared to understand and contribute to production-grade inference systems like vLLM, SGLang, and TensorRT-LLM.