Real-time AI Inferentie

# Real-time AI Inference Engine
class RealTimeInferenceEngine:
    def __init__(self, model_path, target_latency_ms=1.0):
        self.target_latency = target_latency_ms
        self.model = self.load_optimized_model(model_path)
        self.batch_manager = DynamicBatchManager(max_batch_size=32)
        self.memory_pool = PreallocatedMemoryPool()

        # Hardware acceleration setup
        self.accelerator = self.setup_hardware_acceleration()

    def load_optimized_model(self, model_path):
        # Model optimization pipeline
        model = torch.jit.load(model_path)

        # Quantization voor snellere inferentie
        model = torch.quantization.quantize_dynamic(
            model, {torch.nn.Linear}, dtype=torch.qint8
        )

        # TensorRT optimization voor NVIDIA GPUs
        if torch.cuda.is_available():
            model = torch.jit.optimize_for_inference(model)

        return model

    def setup_hardware_acceleration(self):
        if torch.cuda.is_available():
            # CUDA streams voor pipeline parallelism
            return {
                'device': torch.device('cuda'),
                'stream': torch.cuda.Stream(),
                'memory_format': torch.channels_last
            }
        else:
            # CPU optimization met OpenVINO
            return {
                'device': torch.device('cpu'),
                'num_threads': torch.get_num_threads()
            }

    async def predict(self, input_data):
        start_time = time.perf_counter()

        # Pre-allocated tensor voor zero-copy operations
        input_tensor = self.memory_pool.get_tensor(input_data.shape)
        input_tensor.copy_(input_data)

        # Asynchrone inferentie met timing constraints
        with torch.cuda.stream(self.accelerator['stream']):
            with torch.no_grad():
                output = self.model(input_tensor)

        # Latency monitoring en alerting
        inference_time = (time.perf_counter() - start_time) * 1000
        if inference_time > self.target_latency:
            self.handle_latency_violation(inference_time)

        return output, inference_time

# Real-time AI Development Pipeline

## 1. Model Architecture Design
   • Lightweight model architectures (MobileNet, EfficientNet)
   • Depthwise separable convolutions
   • Quantization-aware training
   • Knowledge distillation from larger models

## 2. Performance Optimization
   • Model quantization (INT8/INT4/FP16)
   • Graph optimization en fusion
   • Memory layout optimization
   • Batch size tuning voor latency/throughput balance

## 3. Hardware-Specific Compilation
   • TensorRT optimization voor NVIDIA GPUs
   • OpenVINO compilation voor Intel hardware
   • CoreML conversion voor Apple devices
   • FPGA synthesis voor custom acceleration

## 4. Deployment Infrastructure
   • Containerized serving met Docker/Kubernetes
   • Load balancing met health checks
   • Auto-scaling gebaseerd op latency metrics
   • Monitoring en alerting systemen

## 5. Performance Validation
   • Latency benchmarking onder verschillende loads
   • Stress testing met peak traffic simulation
   • Memory profiling en leak detection
   • Power consumption measurement

# Advanced Latency Optimization Framework
class LatencyOptimizer:
    def __init__(self, target_latency_us=500):
        self.target_latency = target_latency_us
        self.performance_monitor = PerformanceMonitor()
        self.resource_manager = ResourceManager()

    def optimize_inference_pipeline(self, model, input_spec):
        """Comprehensive latency optimization pipeline"""

        # 1. Model-level optimizations
        optimized_model = self.apply_model_optimizations(model)

        # 2. Memory optimization
        memory_layout = self.optimize_memory_layout(input_spec)

        # 3. Threading optimization
        thread_config = self.optimize_threading(input_spec.batch_size)

        # 4. Hardware-specific acceleration
        hardware_config = self.setup_hardware_acceleration()

        return InferenceEngine(
            model=optimized_model,
            memory_layout=memory_layout,
            thread_config=thread_config,
            hardware_config=hardware_config
        )

    def apply_model_optimizations(self, model):
        """Model-level optimizations voor reduced latency"""

        # Graph optimization: operator fusion
        model = torch.jit.optimize_for_inference(model)

        # Quantization: FP32 -> INT8 conversie
        model = self.apply_quantization(model, calibration_data)

        # Pruning: remove redundant weights
        model = self.apply_structured_pruning(model, sparsity=0.3)

        # Knowledge distillation: compress to smaller model
        if self.target_latency < 100:  # Ultra-low latency requirements
            model = self.distill_to_efficient_architecture(model)

        return model

    def optimize_memory_layout(self, input_spec):
        """Memory layout optimization voor cache efficiency"""

        return {
            'memory_format': torch.channels_last,  # Better cache locality
            'pin_memory': True,  # Faster GPU transfers
            'non_blocking': True,  # Async memory operations
            'prefetch_factor': 2,  # Pipeline memory operations
            'persistent_workers': True  # Avoid worker restart overhead
        }

    def optimize_threading(self, batch_size):
        """Threading configuration voor optimal parallelism"""

        # Determine optimal thread count gebaseerd op hardware
        optimal_threads = min(
            torch.get_num_threads(),
            batch_size,
            os.cpu_count()
        )

        # CPU affinity voor consistent performance
        thread_affinity = self.calculate_cpu_affinity(optimal_threads)

        return {
            'num_threads': optimal_threads,
            'cpu_affinity': thread_affinity,
            'thread_pool_type': 'dedicated',  # Avoid context switching
            'numa_aware': True  # NUMA topology optimization
        }

    def setup_hardware_acceleration(self):
        """Hardware-specific acceleration setup"""

        if torch.cuda.is_available():
            return self.setup_gpu_acceleration()
        elif self.has_intel_mkl():
            return self.setup_intel_optimization()
        elif self.has_arm_neon():
            return self.setup_arm_acceleration()
        else:
            return self.setup_generic_optimization()

    def monitor_real_time_performance(self):
        """Continuous performance monitoring en adjustment"""

        while self.is_running:
            metrics = self.performance_monitor.get_latest_metrics()

            if metrics.p99_latency > self.target_latency:
                # Automatic optimization adjustment
                self.adjust_optimization_parameters(metrics)

            # Alert voor performance violations
            if metrics.p99_latency > self.target_latency * 1.5:
                self.trigger_performance_alert(metrics)

            time.sleep(0.1)  # Monitor elke 100ms