72 lines
2.8 KiB
YAML
72 lines
2.8 KiB
YAML
#rpc端口, rpc_port和http_port不允许同时为空。当rpc_port为空且http_port不为空时，会自动将rpc_port设置为http_port+1
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rpc_port: 18090
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#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
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http_port: 9999
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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
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##当build_dag_each_worker=False时，框架会设置主线程grpc线程池的max_workers=worker_num
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worker_num: 20
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#build_dag_each_worker, False，框架在进程内创建一条DAG；True，框架会每个进程内创建多个独立的DAG
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build_dag_each_worker: false
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dag:
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#op资源类型, True, 为线程模型；False，为进程模型
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is_thread_op: False
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#重试次数
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retry: 1
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#使用性能分析, True，生成Timeline性能数据，对性能有一定影响；False为不使用
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use_profile: False
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tracer:
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interval_s: 10
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op:
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det:
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#并发数，is_thread_op=True时，为线程并发；否则为进程并发
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concurrency: 4
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#当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
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local_service_conf:
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#client类型，包括brpc, grpc和local_predictor.local_predictor不启动Serving服务，进程内预测
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client_type: local_predictor
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#det模型路径
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model_config: /paddle/serving/models/det_serving_server/ #ocr_det_model
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#Fetch结果列表，以client_config中fetch_var的alias_name为准
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fetch_list: ["save_infer_model/scale_0.tmp_1"]
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#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
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devices: "2"
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ir_optim: True
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rec:
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#并发数，is_thread_op=True时，为线程并发；否则为进程并发
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concurrency: 1
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#超时时间, 单位ms
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timeout: -1
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#Serving交互重试次数，默认不重试
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retry: 1
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#当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
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local_service_conf:
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#client类型，包括brpc, grpc和local_predictor。local_predictor不启动Serving服务，进程内预测
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client_type: local_predictor
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#rec模型路径
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model_config: /paddle/serving/models/rec_serving_server/ #ocr_rec_model
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#Fetch结果列表，以client_config中fetch_var的alias_name为准
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fetch_list: ["save_infer_model/scale_0.tmp_1"] #["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
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#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
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devices: "2"
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ir_optim: True
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