Created by: 333caowei

def infer(use_cuda, model_path=None):
    if model_path is None:
        print(str(model_path) + " cannot be found")
        return

    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
    exe = fluid.Executor(place)

    main = fluid.Program()
    startup = fluid.Program()
    #inference_scope = fluid.core.Scope()
    #with fluid.scope_guard(inference_scope):
    with fluid.program_guard(main, startup):
        model = Model(feature_size_dict=feature_size_dict, is_sparse=True)
        model.build_model()

        [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(model_path, exe)

        feeder = fluid.DataFeeder(feed_list=feed_target_names, place=place)

        feature_list     = list()
        feature_str_list = list()
        for line in sys.stdin:
            line = line.strip().strip("\r\n")
            line = line.split('\t')

            query       = line[1]
            query_char_id  =eval(line[1].decode('utf-8'))
            pos             = int(line[5])
            query_history_clicked_book_list   = eval(line[13])
            query_history_cate1_ctr_str_list  =  eval(line[14])
            query_history_cate2_ctr_str_list =  eval(line[15])
            query_history_cate3_ctr_str_list =  eval(line[16])
            query_history_clicked_author_lis =  eval(line[17])


            feature = [ \
                [query_id], \
                query_char_id_list, \
                [pos], \
                query_history_clicked_book_id_list, \
                query_history_clicked_author_id_list, \
                query_history_cate1_ctr_list, \
                query_history_cate2_ctr_list, \
                query_history_cate3_ctr_list, \
                [1]
                ]

            feature_str = [query_str, book_str]

            feature_list.append(feature)
            feature_str_list.append(feature_str)
            print feature_list
            if len(feature_list) == batch_size:
                output_list  = exe.run(inference_program,
                                feed=feeder.feed(feature_list),
                                fetch_list=fetch_targets,
                                return_numpy=True)

                for i, j in zip(feature_str_list, output_list[0]):
                    print '%s\t%s\t%s' % (i[0], i[1], float(j))
                feature_list = []
                feature_str_list = []

        if len(feature_list) > 0:
            output_list  = exe.run(inference_program,
                            feed=feeder.feed(feature_list),
                            fetch_list=fetch_targets,
                            return_numpy=True)

            for i, j, k, l in zip(feature_str_list, output_list[0], output_list[1], output_list[2]):
                print i[0]
                print j
                print k
                print l
            feature_list = []
            feature_str_list = []

if __name__ == "__main__":

    epoch_path = 'data/model3.pass1'
    infer(use_cuda=False, model_path=epoch_path)
r_speed.py

代码上面所示，很简单，载入模型后输入数据进行预测，但是却提示grpc错误，模型是mpi训练得到的，但是我现在这是本地预测，和集群毫无关系：

F1022 23:47:17.269440 29550 grpc_client.cc:295] Send name:[nce_w@GRAD.block12], ep:[10.109.92.18:8000] meets grpc error:OS Error
*** Check failure stack trace: ***
    @     0x7f0b938fef2d  google::LogMessage::Fail()
    @     0x7f0b939029dc  google::LogMessage::SendToLog()
    @     0x7f0b938fea53  google::LogMessage::Flush()
    @     0x7f0b93903eee  google::LogMessageFatal::~LogMessageFatal()
    @     0x7f0b947a71e8  paddle::operators::distributed::GRPCClient::Proceed()
    @     0x7f0bcb7bb8a0  execute_native_thread_routine
    @     0x7f0bd77a4d14  start_thread
    @     0x7f0bd6dd4bfd  clone
    @              (nil)  (unknown)
Aborted