PaddleClas/deploy/paddleserving/config.yml

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#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: 1
#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: 18080
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:
imagenet:
#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: ResNet50_vd_serving
#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: 1
#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
#Fetch结果列表以client_config中fetch_var的alias_name为准
fetch_list: ["prediction"]