获取帮助

    输出帮助信息结果如下：

    2.         python main.py start [--role {{agent,server,monitor}}]
    3.         # start local service.
    4.         python main.py stop [--role {{agent,server,monitor}}]
    5.         # stop local service.
    6.         python main.py start [--user USER] [--host HOST] [--project-path PROJECT_PATH] [--role {{agent,server,monitor}}]
    7.         # start the remote service.
    8.         python main.py stop [--user USER] [--host HOST] [--project-path PROJECT_PATH] [--role {{agent,server,monitor}}]
    9.         # stop the remote service.
    10.         python main.py deploy [--user USER] [--host HOST] [--project-path PROJECT_PATH]
    11.         # deploy project in remote host.
    12.         python main.py show_metrics
    13.         # display all monitored metrics(can only be executed on 'detector' machine).
    14.         python main.py forecast [--metric-name METRIC_NAME] [--forecast-periods FORECAST_PERIODS] [--forecast-method {{auto_arima, fbprophet}}] [--save-path SAVE_PATH]
    15.         # forecast future trend of metric(can only be executed on 'detector' machine).
    18. abnomaly_detection: a timeseries forecast and anomaly detection tool.
    19. positional arguments:
    20.   {start,stop,deploy,show_metrics,forecast}
    22. optional arguments:
    23.   -h, --help            show this help message and exit
    24.   --user USER           User of remote server.
    25.   --host HOST           IP of remote server.
    26.   --project-path PROJECT_PATH
    27.                         Project location in remote server.
    28.   --role {agent,server,monitor}
    29.                         Run as 'agent', 'server', 'monitor'. notes: ensure the
    30.                         normal operation of the openGauss in agent.
    31.   --metric-name METRIC_NAME
    32.                         Metric name to be predicted, if this parameter is not
    33.                         provided, all metric in database will be predicted.
    34.   --forecast-periods FORECAST_PERIODS
    35.                         Forecast periods of metric, it should be integer plus
    37.                         Day, W: Week. notes: the specific value should be
    38.                         determined to the trainnig data.if this parameter is
    39.                         not provided, the default value '100S' will be used.
    40.   --forecast-method FORECAST_METHOD
    41.                         Forecast method, default method is 'auto_arima',if
    42.                         want to use 'fbprophet', you should install fbprophet
    43.                         first.
    44.   --save-path SAVE_PATH
    45.                         Save the results to this path using csv format, if
    46.                         this parameter is not provided,, the result wil not be
    47.                         saved.
    48.   -v, --version         show program's version number and exit
    50. epilog:
    51.      the 'a-detection.conf' and 'metric_task.conf' will be read when the program is running,
    52.      the location of them is:
    54.      metric_config: xxx/metric_task.conf.