:doc:`LookoutEquipment <../../lookoutequipment>` / Client / create_retraining_scheduler

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create_retraining_scheduler
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.. py:method:: LookoutEquipment.Client.create_retraining_scheduler(**kwargs)

  

  Creates a retraining scheduler on the specified model.

  

  See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lookoutequipment-2020-12-15/CreateRetrainingScheduler>`_  


  **Request Syntax**
  ::

    response = client.create_retraining_scheduler(
        ModelName='string',
        RetrainingStartDate=datetime(2015, 1, 1),
        RetrainingFrequency='string',
        LookbackWindow='string',
        PromoteMode='MANAGED'|'MANUAL',
        ClientToken='string'
    )
    
  :type ModelName: string
  :param ModelName: **[REQUIRED]** 

    The name of the model to add the retraining scheduler to.

    

  
  :type RetrainingStartDate: datetime
  :param RetrainingStartDate: 

    The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.

    

  
  :type RetrainingFrequency: string
  :param RetrainingFrequency: **[REQUIRED]** 

    This parameter uses the `ISO 8601 <https://en.wikipedia.org/wiki/ISO_8601#Durations>`__ standard to set the frequency at which you want retraining to occur in terms of Years, Months, and/or Days (note: other parameters like Time are not currently supported). The minimum value is 30 days (P30D) and the maximum value is 1 year (P1Y). For example, the following values are valid:

     

    
    * P3M15D – Every 3 months and 15 days
     
    * P2M – Every 2 months
     
    * P150D – Every 150 days
    

    

  
  :type LookbackWindow: string
  :param LookbackWindow: **[REQUIRED]** 

    The number of past days of data that will be used for retraining.

    

  
  :type PromoteMode: string
  :param PromoteMode: 

    Indicates how the service will use new models. In ``MANAGED`` mode, new models will automatically be used for inference if they have better performance than the current model. In ``MANUAL`` mode, the new models will not be used `until they are manually activated <https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/versioning-model.html#model-activation>`__.

    

  
  :type ClientToken: string
  :param ClientToken: **[REQUIRED]** 

    A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

    This field is autopopulated if not provided.

  
  
  :rtype: dict
  :returns: 
    
    **Response Syntax**

    
    ::

      {
          'ModelName': 'string',
          'ModelArn': 'string',
          'Status': 'PENDING'|'RUNNING'|'STOPPING'|'STOPPED'
      }
      
    **Response Structure**

    

    - *(dict) --* 
      

      - **ModelName** *(string) --* 

        The name of the model that you added the retraining scheduler to.

        
      

      - **ModelArn** *(string) --* 

        The ARN of the model that you added the retraining scheduler to.

        
      

      - **Status** *(string) --* 

        The status of the retraining scheduler.

        
  
  **Exceptions**
  
  *   :py:class:`LookoutEquipment.Client.exceptions.ValidationException`

  
  *   :py:class:`LookoutEquipment.Client.exceptions.ResourceNotFoundException`

  
  *   :py:class:`LookoutEquipment.Client.exceptions.ConflictException`

  
  *   :py:class:`LookoutEquipment.Client.exceptions.ThrottlingException`

  
  *   :py:class:`LookoutEquipment.Client.exceptions.AccessDeniedException`

  
  *   :py:class:`LookoutEquipment.Client.exceptions.InternalServerException`

  