Fork自 PaddlePaddle / PaddleDetection
#worker_num, 最大并发数。当build_dag_each_worker=True时, 框架会创建worker_num个进程,每个进程内构建grpcSever和DAG ##当build_dag_each_worker=False时,框架会设置主线程grpc线程池的max_workers=worker_num worker_num: 20 #http端口, rpc_port和http_port不允许同时为空。当rpc_port可用且http_port为空时,不自动生成http_port http_port: 18093 rpc_port: 9993 dag: #op资源类型, True, 为线程模型;False,为进程模型 is_thread_op: False op: #op名称,与web_service中的TIPCExampleService初始化name参数一致 ppdet: #并发数,is_thread_op=True时,为线程并发;否则为进程并发 concurrency: 1 #当op配置没有server_endpoints时,从local_service_conf读取本地服务配置 local_service_conf: #uci模型路径 model_config: "./serving_server" #计算硬件类型: 空缺时由devices决定(CPU/GPU),0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu device_type: #计算硬件ID,当devices为""或不写时为CPU预测;当devices为"0", "0,1,2"时为GPU预测,表示使用的GPU卡 devices: "0" # "0,1" #client类型,包括brpc, grpc和local_predictor.local_predictor不启动Serving服务,进程内预测 client_type: local_predictor