// 设备管理核心类设计
class DeviceManager {
private:
    std::unordered_map<DeviceType, std::vector<DevicePtr>> devices_;
    std::mutex device_mutex_;

public:
    DevicePtr AcquireDevice(DeviceType type, const DeviceRequirements& req);
    void ReleaseDevice(DevicePtr device);
    Status OptimizeDeviceAllocation(const ModelSpec& spec);
};

// 计算调度策略实现
class ComputeScheduler {
public:
    virtual BatchRequest ScheduleInference(const std::vector<InferenceRequest>& requests) = 0;
    virtual void UpdateLoadBalancing(const SystemMetrics& metrics) = 0;
};

# 计算图优化核心逻辑
class GraphOptimizer:
    def __init__(self):
        self.optimizers = [
            ConstantFoldingOptimizer(),
            LayoutOptimizer(), 
            MemoryOptimizer(),
            ArithmeticOptimizer()
        ]

    def optimize_graph(self, graph_def, config):
        optimized_graph = graph_def
        for optimizer in self.optimizers:
            if optimizer.supports_config(config):
                optimized_graph = optimizer.optimize(optimized_graph)
        return self.apply_fusion_rules(optimized_graph)

    def apply_fusion_rules(self, graph):
        # 实现算子融合规则
        # Conv2D + BiasAdd + ReLU -> FusedConv2D
        # BatchNorm + ReLU -> FusedBatchNorm
        return fused_graph

class DynamicBatcher:
    def __init__(self, max_batch_size=32, timeout_ms=100, max_queue_size=1000):
        self.max_batch_size = max_batch_size
        self.timeout_ms = timeout_ms
        self.max_queue_size = max_queue_size
        self.request_queue = deque()
        self.batch_timer = None

    def add_request(self, request):
        if len(self.request_queue) >= self.max_queue_size:
            raise QueueFullError("Request queue is full")

        self.request_queue.append(request)

        # 批处理决策逻辑
        if len(self.request_queue) >= self.max_batch_size:
            return self.create_batch()
        elif self.batch_timer is None:
            self.start_batch_timer()
        return None

    def create_batch(self):
        batch_size = min(len(self.request_queue), self.max_batch_size)
        batch_requests = [self.request_queue.popleft() for _ in range(batch_size)]

        # 批处理优化：输入张量拼接
        batched_inputs = self.batch_inputs(batch_requests)
        return Batch(batched_inputs, batch_requests)

model_config_list: {
  config: {
    name: "resnet_model",
    base_path: "/models/resnet",
    model_platform: "tensorflow",
    model_version_policy: {
      specific: {
        versions: [123, 124]
      }
    }
  }
}

# 批处理配置
max_batch_size: 32
batch_timeout_micros: 1000
num_batch_threads: 4
max_enqueued_batches: 100

# 性能优化配置
session_config: {
  intra_op_parallelism_threads: 8,
  inter_op_parallelism_threads: 8,
  use_per_session_threads: true
}

# 监控配置
monitoring_config: {
  enable_metrics: true,
  metrics_collection_interval_ms: 30000
}

// 模型版本管理核心逻辑
class VersionPolicy {
public:
    virtual std::vector<Version> GetVersionsToLoad(
        const std::vector<Version>& available_versions) = 0;

    virtual std::vector<Version> GetVersionsToUnload(
        const std::vector<Version>& loaded_versions,
        const std::vector<Version>& new_versions) = 0;
};

// 最新版本策略实现
class LatestVersionPolicy : public VersionPolicy {
public:
    std::vector<Version> GetVersionsToLoad(
        const std::vector<Version>& available_versions) override {

        if (available_versions.empty()) return {};

        // 选择版本号最大的版本
        Version latest = *std::max_element(
            available_versions.begin(), 
            available_versions.end()
        );

        return {latest};
    }
};

class MemoryPool {
private:
    struct MemoryBlock {
        void* ptr;
        size_t size;
        bool in_use;
        std::chrono::steady_clock::time_point allocation_time;
    };

    std::vector<MemoryBlock> blocks_;
    std::mutex mutex_;
    const size_t max_pool_size_;

public:
    void* Allocate(size_t size) {
        std::lock_guard<std::mutex> lock(mutex_);

        // 首先尝试复用已有内存块
        for (auto& block : blocks_) {
            if (!block.in_use && block.size >= size) {
                block.in_use = true;
                block.allocation_time = std::chrono::steady_clock::now();
                return block.ptr;
            }
        }

        // 分配新内存块
        if (blocks_.size() < max_pool_size_) {
            void* new_ptr = malloc(size);
            blocks_.push_back({new_ptr, size, true, 
                             std::chrono::steady_clock::now()});
            return new_ptr;
        }

        throw std::bad_alloc();
    }
};

from fastapi import FastAPI, File, UploadFile
import onnxruntime as ort
import numpy as np

app = FastAPI()

# ONNX Runtime会话管理
session = ort.InferenceSession("model.onnx")

@app.post("/predict")
async def predict(image: UploadFile = File(...)):
    # 图像预处理
    image_data = await image.read()
    processed_input = preprocess_image(image_data)

    # 模型推理
    inputs = {session.get_inputs()[0].name: processed_input}
    outputs = session.run(None, inputs)

    return {"predictions": postprocess_output(outputs)}

// TensorRT优化流水线
class EdgeOptimizer {
public:
    void BuildEngine(const std::string& onnx_path, 
                    const std::string& engine_path) {
        // 构建器配置
        auto builder = TrtUniquePtr<nvinfer1::IBuilder>(
            nvinfer1::createInferBuilder(logger_));

        // 网络定义
        auto network = TrtUniquePtr<nvinfer1::INetworkDefinition>(
            builder->createNetworkV2(0));

        // 解析ONNX模型
        auto parser = TrtUniquePtr<nvonnxparser::IParser>(
            nvonnxparser::createParser(*network, logger_));
        parser->parseFromFile(onnx_path.c_str(), 1);

        // 优化配置
        auto config = TrtUniquePtr<nvinfer1::IBuilderConfig>(
            builder->createBuilderConfig());
        config->setFlag(nvinfer1::BuilderFlag::kFP16);

        // 构建引擎
        auto engine = std::shared_ptr<nvinfer1::ICudaEngine>(
            builder->buildEngineWithConfig(*network, *config));

        SerializeEngine(engine, engine_path);
    }
};

# 系统级性能监控
htop                    # CPU/内存监控
nvidia-smi             # GPU监控
iftop                  # 网络流量监控

# 应用级性能分析
py-spy record -o profile.svg --pid PID    # Python性能分析
perf record -g -p PID                     # 系统级性能分析

# 模型推理分析
TF_CPP_MIN_VLOG_LEVEL=1   # TensorFlow详细日志
ONNXRUNTIME_VERBOSE=1     # ONNX Runtime调试信息