:doc:`SageMaker <../../sagemaker>` / Client / stop_training_job

*****************
stop_training_job
*****************



.. py:method:: SageMaker.Client.stop_training_job(**kwargs)

  

  Stops a training job. To stop a job, SageMaker sends the algorithm the ``SIGTERM`` signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.

   

  When it receives a ``StopTrainingJob`` request, SageMaker changes the status of the job to ``Stopping``. After SageMaker stops the job, it sets the status to ``Stopped``.

  

  See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopTrainingJob>`_  


  **Request Syntax**
  ::

    response = client.stop_training_job(
        TrainingJobName='string'
    )
    
  :type TrainingJobName: string
  :param TrainingJobName: **[REQUIRED]** 

    The name of the training job to stop.

    

  
  
  :returns: None
  **Exceptions**
  
  *   :py:class:`SageMaker.Client.exceptions.ResourceNotFound`

  