recommend-emr-dataprocess/ad/pai_flow_operator_v5_1.py

# -*- coding: utf-8 -*-
"""PAI 广告模型更新工作流 v5_1（piaoquan_ad_rank_dnn_v15_1）：训练用 7 天数据，评估用 1 天数据。"""
import functools
import os
import re
import sys
import time
import json
from alibabacloud_paistudio20210202.client import Client as PaiStudio20210202Client
from alibabacloud_tea_openapi import models as open_api_models
from alibabacloud_paistudio20210202 import models as pai_studio_20210202_models
from alibabacloud_tea_util import models as util_models
from alibabacloud_tea_util.client import Client as UtilClient
from alibabacloud_eas20210701.client import Client as eas20210701Client
from alibabacloud_paiflow20210202 import models as paiflow_20210202_models
from alibabacloud_paiflow20210202.client import Client as PAIFlow20210202Client
from datetime import datetime, timedelta
from odps import ODPS
from ad_monitor_util import _monitor
import alibabacloud_oss_v2 as oss

target_names = {
    '样本shuffle',
    '生成CID文件',
    '模型训练-样本shufle',
    '模型导出-2',
    '更新EAS服务(Beta)-1',
    '虚拟起始节点',
    '二分类评估-1',
    '二分类评估-2',
    '预测结果对比'
}

WORKFLOW_NAME = "piaoquan_ad_rank_dnn_v15_1"
EXPERIMENT_ID = "draft-lg9zywreopsjmq78m4"
ACCESS_KEY_ID = "LTAI5tFGqgC8f3mh1fRCrAEy"
ACCESS_KEY_SECRET = "XhOjK9XmTYRhVAtf6yii4s4kZwWzvV"

MAX_RETRIES = 3
TRAIN_DAYS = 7
EVAL_OFFSET_DAYS = 1
ONLINE_SERVICE_NAME = 'ad_rank_dnn_v11_easyrec_v5'


def retry(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        retries = 0
        while retries < MAX_RETRIES:
            try:
                result = func(*args, **kwargs)
                if result is not False:
                    return result
            except Exception as e:
                print(f"函数 {func.__name__} 执行时发生异常: {e}，重试第 {retries + 1} 次")
            retries += 1
        print(f"函数 {func.__name__} 重试 {MAX_RETRIES} 次后仍失败。")
        return False

    return wrapper


def get_odps_instance(project):
    odps = ODPS(
        access_id=ACCESS_KEY_ID,
        secret_access_key=ACCESS_KEY_SECRET,
        project=project,
        endpoint='http://service.cn.maxcompute.aliyun.com/api',
    )
    return odps


def get_data_from_odps(project, table, num):
    odps = get_odps_instance(project)
    try:
        sql = f'select * from {table} limit {num}'
        with odps.execute_sql(sql).open_reader() as reader:
            df = reader.to_pandas()
            if len(df) < num:
                return None
            return df
    except Exception as e:
        print(f"发生错误: {e}")


def get_dict_from_odps(project, table):
    odps = get_odps_instance(project)
    try:
        sql = f'select * from {table}'
        with odps.execute_sql(sql).open_reader() as reader:
            data = {}
            for record in reader:
                record_list = list(record)
                key = record_list[0][1]
                value = record_list[1][1]
                data[key] = value
            return data
    except Exception as e:
        print(f"发生错误: {e}")


def load_holiday_dates():
    current_dir = os.getcwd()
    file_path = os.path.join(current_dir, 'ad', 'holidays.txt')
    try:
        with open(file_path, 'r', encoding='utf-8') as file:
            dates = set()
            for line in file:
                token = line.strip()
                if re.fullmatch(r'\d{8}', token):
                    dates.add(token)
            return dates
    except FileNotFoundError:
        raise Exception(f"错误：未找到 {file_path} 文件。")
    except Exception as e:
        raise Exception(f"错误：读取节假日文件失败: {e}")


def yyyymmdd_ago(days):
    return (datetime.now() - timedelta(days=days)).strftime('%Y%m%d')


def get_eval_date():
    """评估日期：昨天 1 天。"""
    return yyyymmdd_ago(EVAL_OFFSET_DAYS)


def get_train_dates():
    """训练日期：从昨天往前取 TRAIN_DAYS 个非节假日。"""
    holidays = load_holiday_dates()
    dates = []
    offset = EVAL_OFFSET_DAYS
    while len(dates) < TRAIN_DAYS:
        day = yyyymmdd_ago(offset)
        if day not in holidays:
            dates.append(day)
        offset += 1
        if offset > 90:
            raise Exception(f"无法凑齐 {TRAIN_DAYS} 天训练日期，请检查 holidays.txt")
    dates.sort()
    print(f"v5_1 训练日期({TRAIN_DAYS}天): {dates}")
    print(f"v5_1 评估日期(1天): {get_eval_date()}")
    return dates


def replace_sql_dt_in(sql, dates):
    """用指定日期列表替换 SQL 中第一次出现的 where dt in (...)。"""
    quoted = ','.join(f"'{d}'" for d in dates)
    marker = 'where dt in ('
    start_index = sql.find(marker)
    if start_index == -1:
        return None
    value_start = start_index + len(marker)
    value_end = sql.find(')', value_start)
    if value_end == -1:
        return None
    return sql[:value_start] + quoted + sql[value_end:]


def is_created_today(time_str):
    time_obj = datetime.fromisoformat(time_str)
    today_start = datetime.combine(datetime.now().date(), datetime.min.time())
    return time_obj.timestamp() > today_start.timestamp()


def replace_train_tables(cmd, table):
    train_table = 'odps://pai_algo/tables/' + table
    marker = '-Dtrain_tables="'
    start_index = cmd.find(marker)
    if start_index == -1:
        return None
    value_start = start_index + len(marker)
    value_end = cmd.find('"', value_start)
    if value_end == -1:
        return None
    return cmd[:value_start] + train_table + cmd[value_end:]


class PAIClient:
    def __init__(self):
        pass

    @staticmethod
    def create_client() -> PaiStudio20210202Client:
        config = open_api_models.Config(
            access_key_id=ACCESS_KEY_ID,
            access_key_secret=ACCESS_KEY_SECRET
        )
        config.endpoint = f'pai.cn-hangzhou.aliyuncs.com'
        return PaiStudio20210202Client(config)

    @staticmethod
    def create_eas_client() -> eas20210701Client:
        config = open_api_models.Config(
            access_key_id=ACCESS_KEY_ID,
            access_key_secret=ACCESS_KEY_SECRET
        )
        config.endpoint = f'pai-eas.cn-hangzhou.aliyuncs.com'
        return eas20210701Client(config)

    @staticmethod
    def create_flow_client() -> PAIFlow20210202Client:
        config = open_api_models.Config(
            access_key_id=ACCESS_KEY_ID,
            access_key_secret=ACCESS_KEY_SECRET
        )
        config.endpoint = f'paiflow.cn-hangzhou.aliyuncs.com'
        return PAIFlow20210202Client(config)

    @staticmethod
    def get_work_flow_draft(experiment_id: str):
        client = PAIClient.create_client()
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            resp = client.get_experiment_with_options(experiment_id, headers, runtime)
            return resp.body.to_map()
        except Exception as error:
            raise Exception(f"get_work_flow_draft error {error}")

    @staticmethod
    def get_describe_service(service_name: str):
        client = PAIClient.create_eas_client()
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            resp = client.describe_service_with_options('cn-hangzhou', service_name, headers, runtime)
            return resp.body.to_map()
        except Exception as error:
            raise Exception(f"get_describe_service error {error}")

    @staticmethod
    def update_experiment_content(experiment_id: str, content: str, version: int):
        client = PAIClient.create_client()
        update_experiment_content_request = pai_studio_20210202_models.UpdateExperimentContentRequest(
            content=content, version=version)
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            resp = client.update_experiment_content_with_options(
                experiment_id, update_experiment_content_request, headers, runtime)
            print(resp.body.to_map())
        except Exception as error:
            raise Exception(f"update_experiment_content error {error}")

    @staticmethod
    def create_job(experiment_id: str, node_id: str, execute_type: str):
        client = PAIClient.create_client()
        create_job_request = pai_studio_20210202_models.CreateJobRequest()
        create_job_request.experiment_id = experiment_id
        create_job_request.node_id = node_id
        create_job_request.execute_type = execute_type
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            resp = client.create_job_with_options(create_job_request, headers, runtime)
            return resp.body.to_map()
        except Exception as error:
            raise Exception(f"create_job error {error}")

    @staticmethod
    def get_jobs_list(experiment_id: str, order='DESC'):
        client = PAIClient.create_client()
        list_jobs_request = pai_studio_20210202_models.ListJobsRequest(
            experiment_id=experiment_id,
            order=order
        )
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            resp = client.list_jobs_with_options(list_jobs_request, headers, runtime)
            return resp.body.to_map()
        except Exception as error:
            raise Exception(f"get_jobs_list error {error}")

    @staticmethod
    def get_job_detail(job_id: str, verbose=False):
        client = PAIClient.create_client()
        get_job_request = pai_studio_20210202_models.GetJobRequest(
            verbose=verbose
        )
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            resp = client.get_job_with_options(job_id, get_job_request, headers, runtime)
            return resp.body.to_map()
        except Exception as error:
            print(error.message)
            print(error.data.get("Recommend"))
            UtilClient.assert_as_string(error.message)

    @staticmethod
    def get_flow_out_put(pipeline_run_id: str, node_id: str, depth: int):
        client = PAIClient.create_flow_client()
        list_pipeline_run_node_outputs_request = paiflow_20210202_models.ListPipelineRunNodeOutputsRequest(
            depth=depth
        )
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            resp = client.list_pipeline_run_node_outputs_with_options(
                pipeline_run_id, node_id, list_pipeline_run_node_outputs_request, headers, runtime)
            return resp.body.to_map()
        except Exception as error:
            print(error.message)
            print(error.data.get("Recommend"))
            UtilClient.assert_as_string(error.message)


def extract_date_yyyymmdd(input_string):
    pattern = r'\d{8}'
    matches = re.findall(pattern, input_string)
    if matches:
        return matches[0]
    return None


def get_online_model_config(service_name: str):
    model_config = {}
    model_detail = PAIClient.get_describe_service(service_name)
    service_config_str = model_detail['ServiceConfig']
    service_config = json.loads(service_config_str)
    model_path = service_config['model_path']
    model_config['model_path'] = model_path
    model_config['online_date'] = extract_date_yyyymmdd(model_path)
    return model_config


def bind_shuffle_train_table(table):
    draft = PAIClient.get_work_flow_draft(EXPERIMENT_ID)
    print(json.dumps(draft, ensure_ascii=False))
    content = draft['Content']
    version = draft['Version']
    content_json = json.loads(content)
    nodes = content_json.get('nodes')
    for node in nodes:
        if node['name'] != '模型训练-样本shufle':
            continue
        for property in node['properties']:
            if property['name'] != 'sql':
                continue
            new_value = replace_train_tables(property['value'], table)
            if new_value is None:
                print("error")
            property['value'] = new_value
    new_content = json.dumps(content_json, ensure_ascii=False)
    PAIClient.update_experiment_content(EXPERIMENT_ID, new_content, version)


def wait_job_end(job_id: str, check_interval=300):
    while True:
        job_detail = PAIClient.get_job_detail(job_id)
        print(job_detail)
        statue = job_detail['Status']
        if (statue == 'Initialized' or statue == 'Starting' or statue == 'WorkflowServiceStarting'
                or statue == 'Running' or statue == 'ReadyToSchedule'):
            time.sleep(check_interval)
            continue
        if statue == 'Failed' or statue == 'Terminating' or statue == 'Unknown' or statue == 'Skipped' or statue == 'Succeeded':
            return job_detail


def get_node_dict():
    draft = PAIClient.get_work_flow_draft(EXPERIMENT_ID)
    content = draft['Content']
    content_json = json.loads(content)
    nodes = content_json.get('nodes')
    node_dict = {}
    for node in nodes:
        name = node['name']
        if name in target_names:
            node_dict[name] = node['id']
    return node_dict


def get_job_dict():
    job_dict = {}
    jobs_list = PAIClient.get_jobs_list(EXPERIMENT_ID)
    for job in jobs_list['Jobs']:
        if not is_created_today(job['GmtCreateTime']):
            break
        job_id = job['JobId']
        job_detail = PAIClient.get_job_detail(job_id, verbose=True)
        for name in target_names:
            if job_detail['Status'] != 'Succeeded':
                continue
            if name in job_dict:
                continue
            if name in job_detail['RunInfo']:
                job_dict[name] = job_detail['JobId']
    return job_dict


@retry
def update_online_flow():
    try:
        online_model_config = get_online_model_config(ONLINE_SERVICE_NAME)
        draft = PAIClient.get_work_flow_draft(EXPERIMENT_ID)
        print(json.dumps(draft, ensure_ascii=False))
        content = draft['Content']
        version = draft['Version']
        print(content)
        content_json = json.loads(content)
        nodes = content_json.get('nodes')
        global_params = content_json.get('globalParams')
        eval_date = get_eval_date()
        train_dates = get_train_dates()
        for global_param in global_params:
            try:
                if global_param['name'] == 'bizdate':
                    global_param['value'] = eval_date
                if global_param['name'] == 'online_version_dt':
                    global_param['value'] = online_model_config['online_date']
                if global_param['name'] == 'eval_date':
                    global_param['value'] = eval_date
                if global_param['name'] == 'online_model_path':
                    global_param['value'] = online_model_config['model_path']
            except KeyError:
                raise Exception("在处理全局参数时，字典中缺少必要的键")
        for node in nodes:
            try:
                if node['name'] != '样本shuffle':
                    continue
                for property in node['properties']:
                    if property['name'] != 'sql':
                        continue
                    new_value = replace_sql_dt_in(property['value'], train_dates)
                    if new_value is None:
                        print("error")
                    property['value'] = new_value
            except KeyError:
                raise Exception("在处理节点属性时，字典中缺少必要的键")
        new_content = json.dumps(content_json, ensure_ascii=False)
        PAIClient.update_experiment_content(EXPERIMENT_ID, new_content, version)
        return True
    except json.JSONDecodeError:
        raise Exception("JSON 解析错误，可能是草稿内容格式不正确")
    except Exception as e:
        raise Exception(f"发生未知错误: {e}")


@retry
def shuffle_table():
    try:
        node_dict = get_node_dict()
        if '生成CID文件' not in node_dict:
            raise Exception("工作流中未找到节点 生成CID文件")
        train_node_id = node_dict['样本shuffle']
        execute_type = 'EXECUTE_FROM_HERE'
        validate_res = PAIClient.create_job(EXPERIMENT_ID, train_node_id, execute_type)
        validate_job_id = validate_res['JobId']
        validate_job_detail = wait_job_end(validate_job_id, 10)
        if validate_job_detail['Status'] != 'Succeeded':
            return False
        job_verbose = PAIClient.get_job_detail(validate_job_id, verbose=True)
        run_info = job_verbose.get('RunInfo') or ''
        if '生成CID文件' not in run_info:
            print(f"样本shuffle 未带上生成CID文件, RunInfo={run_info}")
            return False
        print("样本shuffle 已触发生成CID文件")
        return True
    except Exception as e:
        error_message = f"在执行 shuffle_table 函数时发生异常: {str(e)}"
        print(error_message)
        raise Exception(error_message)


@retry
def shuffle_train_model():
    try:
        node_dict = get_node_dict()
        job_dict = get_job_dict()
        job_id = job_dict['样本shuffle']
        validate_job_detail = wait_job_end(job_id)
        if validate_job_detail['Status'] == 'Succeeded':
            pipeline_run_id = validate_job_detail['RunId']
            node_id = validate_job_detail['PaiflowNodeId']
            flow_out_put_detail = PAIClient.get_flow_out_put(pipeline_run_id, node_id, 2)
            outputs = flow_out_put_detail['Outputs']
            table = None
            for output in outputs:
                if output["Producer"] == node_dict['样本shuffle'] and output["Name"] == "outputTable":
                    value1 = json.loads(output["Info"]['value'])
                    table = value1['location']['table']
            if table is not None:
                bind_shuffle_train_table(table)
                node_dict = get_node_dict()
                train_node_id = node_dict['模型训练-样本shufle']
                execute_type = 'EXECUTE_ONE'
                train_res = PAIClient.create_job(EXPERIMENT_ID, train_node_id, execute_type)
                train_job_id = train_res['JobId']
                train_job_detail = wait_job_end(train_job_id)
                if train_job_detail['Status'] == 'Succeeded':
                    return True
        return False
    except Exception as e:
        error_message = f"在执行 shuffle_train_model 函数时发生异常: {str(e)}"
        print(error_message)
        raise Exception(error_message)


@retry
def export_model():
    try:
        node_dict = get_node_dict()
        export_node_id = node_dict['模型导出-2']
        execute_type = 'EXECUTE_ONE'
        export_res = PAIClient.create_job(EXPERIMENT_ID, export_node_id, execute_type)
        export_job_id = export_res['JobId']
        export_job_detail = wait_job_end(export_job_id)
        if export_job_detail['Status'] == 'Succeeded':
            return True
        return False
    except Exception as e:
        error_message = f"在执行 export_model 函数时发生异常: {str(e)}"
        print(error_message)
        raise Exception(error_message)


def update_online_model():
    try:
        node_dict = get_node_dict()
        train_node_id = node_dict['更新EAS服务(Beta)-1']
        execute_type = 'EXECUTE_ONE'
        train_res = PAIClient.create_job(EXPERIMENT_ID, train_node_id, execute_type)
        train_job_id = train_res['JobId']
        train_job_detail = wait_job_end(train_job_id)
        if train_job_detail['Status'] == 'Succeeded':
            return True
        return False
    except Exception as e:
        error_message = f"在执行 update_online_model 函数时发生异常: {str(e)}"
        print(error_message)
        raise Exception(error_message)


@retry
def get_validate_model_data():
    try:
        node_dict = get_node_dict()
        train_node_id = node_dict['虚拟起始节点']
        execute_type = 'EXECUTE_FROM_HERE'
        validate_res = PAIClient.create_job(EXPERIMENT_ID, train_node_id, execute_type)
        validate_job_id = validate_res['JobId']
        validate_job_detail = wait_job_end(validate_job_id)
        if validate_job_detail['Status'] == 'Succeeded':
            return True
        return False
    except Exception as e:
        error_message = f"在执行 get_validate_model_data 函数时出现异常: {e}"
        print(error_message)
        raise Exception(error_message)


def validate_model_data_accuracy():
    try:
        table_dict = {}
        node_dict = get_node_dict()
        job_dict = get_job_dict()
        job_id = job_dict['虚拟起始节点']
        validate_job_detail = wait_job_end(job_id)
        if validate_job_detail['Status'] == 'Succeeded':
            pipeline_run_id = validate_job_detail['RunId']
            node_id = validate_job_detail['PaiflowNodeId']
            flow_out_put_detail = PAIClient.get_flow_out_put(pipeline_run_id, node_id, 3)
            print(flow_out_put_detail)
            outputs = flow_out_put_detail['Outputs']
            for output in outputs:
                if output["Producer"] == node_dict['二分类评估-1'] and output["Name"] == "outputMetricTable":
                    value1 = json.loads(output["Info"]['value'])
                    table_dict['二分类评估-1'] = value1['location']['table']
                if output["Producer"] == node_dict['二分类评估-2'] and output["Name"] == "outputMetricTable":
                    value2 = json.loads(output["Info"]['value'])
                    table_dict['二分类评估-2'] = value2['location']['table']
                if output["Producer"] == node_dict['预测结果对比'] and output["Name"] == "outputTable":
                    value3 = json.loads(output["Info"]['value'])
                    table_dict['预测结果对比'] = value3['location']['table']
        num = 10
        df = get_data_from_odps('pai_algo', table_dict['预测结果对比'], 10)
        old_abs_avg = df['old_error'].abs().sum() / num
        new_abs_avg = df['new_error'].abs().sum() / num
        new_auc = get_dict_from_odps('pai_algo', table_dict['二分类评估-1'])['AUC']
        old_auc = get_dict_from_odps('pai_algo', table_dict['二分类评估-2'])['AUC']
        eval_date = get_eval_date()
        score_diff = abs(old_abs_avg - new_abs_avg)
        msg = ""
        result = False
        if new_abs_avg > 0.1:
            msg += f'{WORKFLOW_NAME}线上模型评估{eval_date}的数据，绝对误差大于0.1，请检查'
            level = 'error'
        elif score_diff > 0.02 and new_abs_avg - old_abs_avg > 0.02:
            msg += f'{WORKFLOW_NAME}两个模型评估{eval_date}的数据，两个模型分数差异为: {score_diff}, 大于0.02, 请检查'
            level = 'error'
        else:
            msg += f'{WORKFLOW_NAME}广告模型更新完成(训练7天/评估1天)'
            level = 'info'
            result = True

        top10_msg = "| CID  | 老模型相对真实CTCVR的变化 | 新模型相对真实CTCVR的变化 |"
        top10_msg += "\n| ---- | --------- | -------- |"

        for index, row in df.iterrows():
            cid = row['cid']
            old_error = row['old_error']
            new_error = row['new_error']
            top10_msg += f"\n| {int(cid)} | {old_error} | {new_error} | "
        print(top10_msg)
        msg += f"\n\t - 老模型AUC: {old_auc}"
        msg += f"\n\t - 新模型AUC: {new_auc}"
        msg += f"\n\t - 老模型Top10差异平均值: {old_abs_avg}"
        msg += f"\n\t - 新模型Top10差异平均值: {new_abs_avg}"
        return result, msg, level, top10_msg

    except Exception as e:
        error_message = f"在执行 validate_model_data_accuracy 函数时出现异常: {str(e)}"
        print(error_message)
        raise Exception(error_message)


def update_trained_cids_pointer(model_name=None, dt_version=None):
    if not model_name and not dt_version:
        draft = PAIClient.get_work_flow_draft(EXPERIMENT_ID)
        content = draft['Content']
        content_json = json.loads(content)
        global_params = content_json.get('globalParams', [])
        model_name = None
        dt_version = None
        for param in global_params:
            if param.get('name') == 'model_name':
                model_name = param.get('value')
            if param.get('name') == 'bizdate':
                dt_version = param.get('value')
        if not model_name or not dt_version:
            raise Exception("globalParams 中未找到 model_name 或 bizdate")
    elif not (model_name and dt_version):
        raise Exception("model_name 和 dt_version 必须同时提供")
    model_version = {}
    model_version['modelName'] = f"model_name={model_name}"
    model_version['dtVersion'] = f"dt_version={dt_version}"
    model_version['timestamp'] = int(time.time())
    print(json.dumps(model_version, ensure_ascii=False, indent=4).encode('utf-8'))
    bucket_name = "art-recommend"
    object_key = "fengzhoutian/pai_model_trained_cids/model_version_v5_1.json"

    oss_config = oss.config.load_default()
    oss_config.credentials_provider = oss.credentials.StaticCredentialsProvider(
        access_key_id=ACCESS_KEY_ID, access_key_secret=ACCESS_KEY_SECRET
    )
    oss_config.region = "cn-hangzhou"
    client = oss.Client(oss_config)
    ret = client.put_object(oss.PutObjectRequest(
        bucket=bucket_name,
        key=object_key,
        body=json.dumps(model_version, ensure_ascii=False, indent=4).encode('utf-8')
    ))
    print(f'oss put status code: {ret.status_code},'
          f' request id: {ret.request_id},'
          f' content md5: {ret.content_md5},'
          f' etag: {ret.etag},'
          f' hash crc64: {ret.hash_crc64},'
          f' version id: {ret.version_id},'
          f' content: {model_version}'
          )


if __name__ == '__main__':
    start_time = int(time.time())
    functions = [update_online_flow, shuffle_table, shuffle_train_model, export_model, get_validate_model_data]
    function_names = [func.__name__ for func in functions]

    start_function = None
    if len(sys.argv) > 1:
        start_function = sys.argv[1]
        if start_function not in function_names:
            print(f"指定的起始函数 {start_function} 不存在，请选择以下函数之一：{', '.join(function_names)}")
            sys.exit(1)

    start_index = 0
    if start_function:
        start_index = function_names.index(start_function)

    for func in functions[start_index:]:
        if not func():
            print(f"{func.__name__} 执行失败，后续函数不再执行。")
            step_end_time = int(time.time())
            elapsed = step_end_time - start_time
            _monitor('error', f"{WORKFLOW_NAME}模型更新，{func.__name__} 执行失败，后续函数不再执行，请检查", start_time, elapsed, None)
            break
    else:
        print("所有函数都成功执行，可以继续下一步操作。")
        result, msg, level, top10_msg = validate_model_data_accuracy()
        if result:
            update_online_res = update_online_model()
            if update_online_res:
                update_trained_cids_pointer()
                print("success")
        step_end_time = int(time.time())
        elapsed = step_end_time - start_time
        print(level, msg, start_time, elapsed, top10_msg)
        _monitor(level, msg, start_time, elapsed, top10_msg)