eunij-peanut
windows部署报错： > python web_service.py --config=config.yml args config: {'rpc_port': 18091, 'http_port': 9998, 'worker_num': 1, 'build_dag_each_worker': False, 'dag': {'is_thread_op': True, 'retry': 3, 'use_profile': False, 'tracer': {'interval_s': -1}}, 'op': {'det': {'concurrency': 1, 'local_service_conf': {'client_type': 'local_predictor', 'model_config': './ppocr_det_v4_serving', 'devices': '', 'ir_optim': False}}, 'rec': {'concurrency': 1, 'timeout': 3000, 'retry': 1, 'local_service_conf': {'client_type': 'local_predictor', 'model_config': './ppocr_rec_v4_serving', 'devices': '', 'ir_optim': False}}}} [DAG] Succ init I0113 11:20:45.652944 10512 analysis_predictor.cc:1626] MKLDNN is enabled I0113 11:20:45.652944 10512 analysis_predictor.cc:1740] Ir optimization is turned off, no ir pass will be executed. e[1me[35m--- Running analysis [ir_graph_build_pass]e[0m I0113 11:20:45.652944 10512 executor.cc:187] Old Executor is Running. e[1me[35m--- Running analysis [ir_analysis_pass]e[0m e[1me[35m--- Running analysis [save_optimized_model_pass]e[0m e[1me[35m--- Running analysis [ir_params_sync_among_devices_pass]e[0m e[1me[35m--- Running analysis [adjust_cudnn_workspace_size_pass]e[0m e[1me[35m--- Running analysis [inference_op_replace_pass]e[0m e[1me[35m--- Running analysis [memory_optimize_pass]e[0m I0113 11:20:45.668565 10512 memory_optimize_pass.cc:118] The persistable params in main graph are : 10.2689MB I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : linear_170.tmp_1 size: 26500 I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : shape_5.tmp_0_slice_1 size: 4 I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : conv2d_198.tmp_1 size: 11520 I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : linear_170.tmp_0 size: 26500 I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : pool2d_3.tmp_0_clone_0 size: 1920 I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : batch_norm_5.tmp_0 size: 1920 I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : x size: 576 I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : transpose_44.tmp_0_slice_2 size: 480 I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : fill_constant_17.tmp_0 size: 4 I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : fill_constant_19.tmp_0 size: 4 I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : shape_3.tmp_0_slice_1 size: 4 I0113 11:20:45.684191 10512 memory_optimize_pass.cc:246] Cluster name : reshape2_27.tmp_1 size: 0 e[1me[35m--- Running analysis [ir_graph_to_program_pass]e[0m I0113 11:20:45.736836 10512 analysis_predictor.cc:1838] ======= optimize end ======= I0113 11:20:45.736836 10512 naive_executor.cc:200] --- skip [feed], feed -> x I0113 11:20:45.736836 10512 naive_executor.cc:200] --- skip [softmax_11.tmp_0], fetch -> fetch [OP Object] init success I0113 11:20:45.762605 10512 analysis_predictor.cc:1626] MKLDNN is enabled I0113 11:20:45.762605 10512 analysis_predictor.cc:1740] Ir optimization is turned off, no ir pass will be executed. e[1me[35m--- Running analysis [ir_graph_build_pass]e[0m e[1me[35m--- Running analysis [ir_analysis_pass]e[0m e[1me[35m--- Running analysis [save_optimized_model_pass]e[0m e[1me[35m--- Running analysis [ir_params_sync_among_devices_pass]e[0m e[1me[35m--- Running analysis [adjust_cudnn_workspace_size_pass]e[0m e[1me[35m--- Running analysis [inference_op_replace_pass]e[0m e[1me[35m--- Running analysis [memory_optimize_pass]e[0m I0113 11:20:45.782716 10512 memory_optimize_pass.cc:118] The persistable params in main graph are : 4.47025MB I0113 11:20:45.782716 10512 memory_optimize_pass.cc:246] Cluster name : tmp_85 size: 1536 I0113 11:20:45.782716 10512 memory_optimize_pass.cc:246] Cluster name : tmp_21 size: 192 I0113 11:20:45.782716 10512 memory_optimize_pass.cc:246] Cluster name : tmp_94 size: 1536 I0113 11:20:45.782716 10512 memory_optimize_pass.cc:246] Cluster name : tmp_118 size: 96 I0113 11:20:45.782716 10512 memory_optimize_pass.cc:246] Cluster name : relu_1.tmp_0 size: 384 I0113 11:20:45.782716 10512 memory_optimize_pass.cc:246] Cluster name : hardswish_79.tmp_0 size: 1536 I0113 11:20:45.782716 10512 memory_optimize_pass.cc:246] Cluster name : tmp_35 size: 384 I0113 11:20:45.782716 10512 memory_optimize_pass.cc:246] Cluster name : tmp_73 size: 768 I0113 11:20:45.782716 10512 memory_optimize_pass.cc:246] Cluster name : x size: 12 e[1me[35m--- Running analysis [ir_graph_to_program_pass]e[0m I0113 11:20:45.830435 10512 analysis_predictor.cc:1838] ======= optimize end ======= I0113 11:20:45.830435 10512 naive_executor.cc:200] --- skip [feed], feed -> x I0113 11:20:45.830435 10512 naive_executor.cc:200] --- skip [sigmoid_0.tmp_0], fetch -> fetch [OP Object] init success [PipelineServicer] succ init Error running service: cannot pickle '_thread.lock' object 当前环境为：windows的anaconda环境下使用pip命令 python 3.8 Name: paddlepaddle Version: 2.6.2 Name: paddleocr Version: 2.9.1 使用线程，config.yml如下： #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 rpc_port: 18091 #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 # 原来没有注释掉：http_port: 9998 http_port: 9998 #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 #原来为：worker_num: 10 worker_num: 1 #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 #原来为：build_dag_each_worker: False build_dag_each_worker: False dag: #op资源类型, True, 为线程模型；False，为进程模型 #原来为：is_thread_op: False is_thread_op: False #重试次数 #原来为：retry: 10 retry: 3 #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 #原来为：use_profile: True use_profile: False tracer: #原来为：interval_s: 10 interval_s: -1 op: det: #并发数，is_thread_op=True时，为线程并发；否则为进程并发 #原来为：concurrency: 8 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: #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 #det模型路径 #model_config: ./ppocr_det_v3_serving model_config: ./ppocr_det_v4_serving #Fetch结果列表，以client_config中fetch_var的alias_name为准，不设置默认取全部输出变量 #fetch_list: ["sigmoid_0.tmp_0"] #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 #原来为：devices: "0" devices: "" #原来为：ir_optim: True ir_optim: False rec: #并发数，is_thread_op=True时，为线程并发；否则为进程并发 #原来为：concurrency: 4 concurrency: 1 #超时时间, 单位ms #原来为：timeout: -1 timeout: 3000 #Serving交互重试次数，默认不重试 retry: 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: #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 #rec模型路径 #model_config: ./ppocr_rec_v3_serving model_config: ./ppocr_rec_v4_serving #Fetch结果列表，以client_config中fetch_var的alias_name为准, 不设置默认取全部输出变量 #fetch_list: #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 #原来为：devices: "0" devices: "" #原来为：ir_optim: True ir_optim: False 部署命令运行后报错