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

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

  

  Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see `Create an MLflow Tracking Server <https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server.html>`__.

  

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


  **Request Syntax**
  ::

    response = client.create_mlflow_tracking_server(
        TrackingServerName='string',
        ArtifactStoreUri='string',
        TrackingServerSize='Small'|'Medium'|'Large',
        MlflowVersion='string',
        RoleArn='string',
        AutomaticModelRegistration=True|False,
        WeeklyMaintenanceWindowStart='string',
        Tags=[
            {
                'Key': 'string',
                'Value': 'string'
            },
        ]
    )
    
  :type TrackingServerName: string
  :param TrackingServerName: **[REQUIRED]** 

    A unique string identifying the tracking server name. This string is part of the tracking server ARN.

    

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

    The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.

    

  
  :type TrackingServerSize: string
  :param TrackingServerSize: 

    The size of the tracking server you want to create. You can choose between ``"Small"``, ``"Medium"``, and ``"Large"``. The default MLflow Tracking Server configuration size is ``"Small"``. You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.

     

    We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.

    

  
  :type MlflowVersion: string
  :param MlflowVersion: 

    The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see `How it works <https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html#mlflow-create-tracking-server-how-it-works>`__.

    

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

    The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have ``AmazonS3FullAccess`` permissions. For more information on IAM permissions for tracking server creation, see `Set up IAM permissions for MLflow <https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server-iam.html>`__.

    

  
  :type AutomaticModelRegistration: boolean
  :param AutomaticModelRegistration: 

    Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to ``True``. To disable automatic model registration, set this value to ``False``. If not specified, ``AutomaticModelRegistration`` defaults to ``False``.

    

  
  :type WeeklyMaintenanceWindowStart: string
  :param WeeklyMaintenanceWindowStart: 

    The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.

    

  
  :type Tags: list
  :param Tags: 

    Tags consisting of key-value pairs used to manage metadata for the tracking server.

    

  
    - *(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**

    
    ::

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

    

    - *(dict) --* 
      

      - **TrackingServerArn** *(string) --* 

        The ARN of the tracking server.

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

  