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

******************************
create_studio_lifecycle_config
******************************



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

  

  Creates a new Amazon SageMaker AI Studio Lifecycle Configuration.

  

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


  **Request Syntax**
  ::

    response = client.create_studio_lifecycle_config(
        StudioLifecycleConfigName='string',
        StudioLifecycleConfigContent='string',
        StudioLifecycleConfigAppType='JupyterServer'|'KernelGateway'|'CodeEditor'|'JupyterLab',
        Tags=[
            {
                'Key': 'string',
                'Value': 'string'
            },
        ]
    )
    
  :type StudioLifecycleConfigName: string
  :param StudioLifecycleConfigName: **[REQUIRED]** 

    The name of the Amazon SageMaker AI Studio Lifecycle Configuration to create.

    

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

    The content of your Amazon SageMaker AI Studio Lifecycle Configuration script. This content must be base64 encoded.

    

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

    The App type that the Lifecycle Configuration is attached to.

    

  
  :type Tags: list
  :param Tags: 

    Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.

    

  
    - *(dict) --* 

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

       

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see `AddTags <https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AddTags.html>`__.

       

      For more information on adding metadata to your Amazon Web Services resources with tagging, see `Tagging Amazon Web Services resources <https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html>`__. For advice on best practices for managing Amazon Web Services resources with tagging, see `Tagging Best Practices\: Implement an Effective Amazon Web Services Resource Tagging Strategy <https://d1.awsstatic.com/whitepapers/aws-tagging-best-practices.pdf>`__.

      

    
      - **Key** *(string) --* **[REQUIRED]** 

        The tag key. Tag keys must be unique per resource.

        

      
      - **Value** *(string) --* **[REQUIRED]** 

        The tag value.

        

      
    

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

    
    ::

      {
          'StudioLifecycleConfigArn': 'string'
      }
      
    **Response Structure**

    

    - *(dict) --* 
      

      - **StudioLifecycleConfigArn** *(string) --* 

        The ARN of your created Lifecycle Configuration.

        
  
  **Exceptions**
  
  *   :py:class:`SageMaker.Client.exceptions.ResourceInUse`

  