:doc:`Personalize <../../personalize>` / Client / create_solution

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create_solution
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.. py:method:: Personalize.Client.create_solution(**kwargs)

  

  .. warning::

    

    By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can `update the solution <https://docs.aws.amazon.com/personalize/latest/dg/API_UpdateSolution.html>`__ to turn off automatic training. For information about training costs, see `Amazon Personalize pricing <https://aws.amazon.com/personalize/pricing/>`__.

     

   

  Creates the configuration for training a model (creating a solution version). This configuration includes the recipe to use for model training and optional training configuration, such as columns to use in training and feature transformation parameters. For more information about configuring a solution, see `Creating and configuring a solution <https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html>`__.

   

  By default, new solutions use automatic training to create solution versions every 7 days. You can change the training frequency. Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information, see `Configuring automatic training <https://docs.aws.amazon.com/personalize/latest/dg/solution-config-auto-training.html>`__.

   

  To turn off automatic training, set ``performAutoTraining`` to false. If you turn off automatic training, you must manually create a solution version by calling the `CreateSolutionVersion <https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolutionVersion.html>`__ operation.

   

  After training starts, you can get the solution version's Amazon Resource Name (ARN) with the `ListSolutionVersions <https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html>`__ API operation. To get its status, use the `DescribeSolutionVersion <https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html>`__.

   

  After training completes you can evaluate model accuracy by calling `GetSolutionMetrics <https://docs.aws.amazon.com/personalize/latest/dg/API_GetSolutionMetrics.html>`__. When you are satisfied with the solution version, you deploy it using `CreateCampaign <https://docs.aws.amazon.com/personalize/latest/dg/API_CreateCampaign.html>`__. The campaign provides recommendations to a client through the `GetRecommendations <https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html>`__ API.

   

  .. note::

    

    Amazon Personalize doesn't support configuring the ``hpoObjective`` for solution hyperparameter optimization at this time.

    

   

  **Status**

   

  A solution can be in one of the following states:

   

  
  * CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
   
  * DELETE PENDING > DELETE IN_PROGRESS
  

   

  To get the status of the solution, call `DescribeSolution <https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html>`__. If you use manual training, the status must be ACTIVE before you call ``CreateSolutionVersion``.

   

  **Related APIs**

   

  
  * `UpdateSolution <https://docs.aws.amazon.com/personalize/latest/dg/API_UpdateSolution.html>`__
   
  * `ListSolutions <https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutions.html>`__
   
  * `CreateSolutionVersion <https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolutionVersion.html>`__
   
  * `DescribeSolution <https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html>`__
   
  * `DeleteSolution <https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteSolution.html>`__
  

   

  
  * `ListSolutionVersions <https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html>`__
   
  * `DescribeSolutionVersion <https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html>`__
  

  

  See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/personalize-2018-05-22/CreateSolution>`_  


  **Request Syntax**
  ::

    response = client.create_solution(
        name='string',
        performHPO=True|False,
        performAutoML=True|False,
        performAutoTraining=True|False,
        performIncrementalUpdate=True|False,
        recipeArn='string',
        datasetGroupArn='string',
        eventType='string',
        solutionConfig={
            'eventValueThreshold': 'string',
            'hpoConfig': {
                'hpoObjective': {
                    'type': 'string',
                    'metricName': 'string',
                    'metricRegex': 'string'
                },
                'hpoResourceConfig': {
                    'maxNumberOfTrainingJobs': 'string',
                    'maxParallelTrainingJobs': 'string'
                },
                'algorithmHyperParameterRanges': {
                    'integerHyperParameterRanges': [
                        {
                            'name': 'string',
                            'minValue': 123,
                            'maxValue': 123
                        },
                    ],
                    'continuousHyperParameterRanges': [
                        {
                            'name': 'string',
                            'minValue': 123.0,
                            'maxValue': 123.0
                        },
                    ],
                    'categoricalHyperParameterRanges': [
                        {
                            'name': 'string',
                            'values': [
                                'string',
                            ]
                        },
                    ]
                }
            },
            'algorithmHyperParameters': {
                'string': 'string'
            },
            'featureTransformationParameters': {
                'string': 'string'
            },
            'autoMLConfig': {
                'metricName': 'string',
                'recipeList': [
                    'string',
                ]
            },
            'eventsConfig': {
                'eventParametersList': [
                    {
                        'eventType': 'string',
                        'eventValueThreshold': 123.0,
                        'weight': 123.0
                    },
                ]
            },
            'optimizationObjective': {
                'itemAttribute': 'string',
                'objectiveSensitivity': 'LOW'|'MEDIUM'|'HIGH'|'OFF'
            },
            'trainingDataConfig': {
                'excludedDatasetColumns': {
                    'string': [
                        'string',
                    ]
                },
                'includedDatasetColumns': {
                    'string': [
                        'string',
                    ]
                }
            },
            'autoTrainingConfig': {
                'schedulingExpression': 'string'
            }
        },
        tags=[
            {
                'tagKey': 'string',
                'tagValue': 'string'
            },
        ]
    )
    
  :type name: string
  :param name: **[REQUIRED]** 

    The name for the solution.

    

  
  :type performHPO: boolean
  :param performHPO: 

    Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is ``false``.

     

    When performing AutoML, this parameter is always ``true`` and you should not set it to ``false``.

    

  
  :type performAutoML: boolean
  :param performAutoML: 

    .. warning::

      

      We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see `Choosing a recipe <https://docs.aws.amazon.com/personalize/latest/dg/working-with-predefined-recipes.html>`__.

       

     

    Whether to perform automated machine learning (AutoML). The default is ``false``. For this case, you must specify ``recipeArn``.

     

    When set to ``true``, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit ``recipeArn``. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.

    

  
  :type performAutoTraining: boolean
  :param performAutoTraining: 

    Whether the solution uses automatic training to create new solution versions (trained models). The default is ``True`` and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a ``schedulingExpression`` in the ``AutoTrainingConfig`` as part of solution configuration. For more information about automatic training, see `Configuring automatic training <https://docs.aws.amazon.com/personalize/latest/dg/solution-config-auto-training.html>`__.

     

    Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.

     

    After training starts, you can get the solution version's Amazon Resource Name (ARN) with the `ListSolutionVersions <https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html>`__ API operation. To get its status, use the `DescribeSolutionVersion <https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html>`__.

    

  
  :type performIncrementalUpdate: boolean
  :param performIncrementalUpdate: 

    Whether to perform incremental training updates on your model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe.

    

  
  :type recipeArn: string
  :param recipeArn: 

    The Amazon Resource Name (ARN) of the recipe to use for model training. This is required when ``performAutoML`` is false. For information about different Amazon Personalize recipes and their ARNs, see `Choosing a recipe <https://docs.aws.amazon.com/personalize/latest/dg/working-with-predefined-recipes.html>`__.

    

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

    The Amazon Resource Name (ARN) of the dataset group that provides the training data.

    

  
  :type eventType: string
  :param eventType: 

    When your have multiple event types (using an ``EVENT_TYPE`` schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.

     

    If you do not provide an ``eventType``, Amazon Personalize will use all interactions for training with equal weight regardless of type.

    

  
  :type solutionConfig: dict
  :param solutionConfig: 

    The configuration properties for the solution. When ``performAutoML`` is set to true, Amazon Personalize only evaluates the ``autoMLConfig`` section of the solution configuration.

     

    .. note::

      

      Amazon Personalize doesn't support configuring the ``hpoObjective`` at this time.

      

    

  
    - **eventValueThreshold** *(string) --* 

      Only events with a value greater than or equal to this threshold are used for training a model.

      

    
    - **hpoConfig** *(dict) --* 

      Describes the properties for hyperparameter optimization (HPO).

      

    
      - **hpoObjective** *(dict) --* 

        The metric to optimize during HPO.

         

        .. note::

          

          Amazon Personalize doesn't support configuring the ``hpoObjective`` at this time.

          

        

      
        - **type** *(string) --* 

          The type of the metric. Valid values are ``Maximize`` and ``Minimize``.

          

        
        - **metricName** *(string) --* 

          The name of the metric.

          

        
        - **metricRegex** *(string) --* 

          A regular expression for finding the metric in the training job logs.

          

        
      
      - **hpoResourceConfig** *(dict) --* 

        Describes the resource configuration for HPO.

        

      
        - **maxNumberOfTrainingJobs** *(string) --* 

          The maximum number of training jobs when you create a solution version. The maximum value for ``maxNumberOfTrainingJobs`` is ``40``.

          

        
        - **maxParallelTrainingJobs** *(string) --* 

          The maximum number of parallel training jobs when you create a solution version. The maximum value for ``maxParallelTrainingJobs`` is ``10``.

          

        
      
      - **algorithmHyperParameterRanges** *(dict) --* 

        The hyperparameters and their allowable ranges.

        

      
        - **integerHyperParameterRanges** *(list) --* 

          The integer-valued hyperparameters and their ranges.

          

        
          - *(dict) --* 

            Provides the name and range of an integer-valued hyperparameter.

            

          
            - **name** *(string) --* 

              The name of the hyperparameter.

              

            
            - **minValue** *(integer) --* 

              The minimum allowable value for the hyperparameter.

              

            
            - **maxValue** *(integer) --* 

              The maximum allowable value for the hyperparameter.

              

            
          
      
        - **continuousHyperParameterRanges** *(list) --* 

          The continuous hyperparameters and their ranges.

          

        
          - *(dict) --* 

            Provides the name and range of a continuous hyperparameter.

            

          
            - **name** *(string) --* 

              The name of the hyperparameter.

              

            
            - **minValue** *(float) --* 

              The minimum allowable value for the hyperparameter.

              

            
            - **maxValue** *(float) --* 

              The maximum allowable value for the hyperparameter.

              

            
          
      
        - **categoricalHyperParameterRanges** *(list) --* 

          The categorical hyperparameters and their ranges.

          

        
          - *(dict) --* 

            Provides the name and range of a categorical hyperparameter.

            

          
            - **name** *(string) --* 

              The name of the hyperparameter.

              

            
            - **values** *(list) --* 

              A list of the categories for the hyperparameter.

              

            
              - *(string) --* 

              
          
          
      
      
    
    - **algorithmHyperParameters** *(dict) --* 

      Lists the algorithm hyperparameters and their values.

      

    
      - *(string) --* 

      
        - *(string) --* 

        
  

    - **featureTransformationParameters** *(dict) --* 

      Lists the feature transformation parameters.

      

    
      - *(string) --* 

      
        - *(string) --* 

        
  

    - **autoMLConfig** *(dict) --* 

      The `AutoMLConfig <https://docs.aws.amazon.com/personalize/latest/dg/API_AutoMLConfig.html>`__ object containing a list of recipes to search when AutoML is performed.

      

    
      - **metricName** *(string) --* 

        The metric to optimize.

        

      
      - **recipeList** *(list) --* 

        The list of candidate recipes.

        

      
        - *(string) --* 

        
    
    
    - **eventsConfig** *(dict) --* 

      Describes the configuration of an event, which includes a list of event parameters. You can specify up to 10 event parameters. Events are used in solution creation.

      

    
      - **eventParametersList** *(list) --* 

        A list of event parameters, which includes event types and their event value thresholds and weights.

        

      
        - *(dict) --* 

          Describes the parameters of events, which are used in solution creation.

          

        
          - **eventType** *(string) --* 

            The name of the event type to be considered for solution creation.

            

          
          - **eventValueThreshold** *(float) --* 

            The threshold of the event type. Only events with a value greater or equal to this threshold will be considered for solution creation.

            

          
          - **weight** *(float) --* 

            The weight of the event type. A higher weight means higher importance of the event type for the created solution.

            

          
        
    
    
    - **optimizationObjective** *(dict) --* 

      Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see `Optimizing a solution <https://docs.aws.amazon.com/personalize/latest/dg/optimizing-solution-for-objective.html>`__.

      

    
      - **itemAttribute** *(string) --* 

        The numerical metadata column in an Items dataset related to the optimization objective. For example, VIDEO_LENGTH (to maximize streaming minutes), or PRICE (to maximize revenue).

        

      
      - **objectiveSensitivity** *(string) --* 

        Specifies how Amazon Personalize balances the importance of your optimization objective versus relevance.

        

      
    
    - **trainingDataConfig** *(dict) --* 

      Specifies the training data configuration to use when creating a custom solution version (trained model).

      

    
      - **excludedDatasetColumns** *(dict) --* 

        Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations.

         

        For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.

        

      
        - *(string) --* 

        
          - *(list) --* 

          
            - *(string) --* 

            
        
    
  
      - **includedDatasetColumns** *(dict) --* 

        A map that specifies which columns to include from each dataset during training. The map can contain up to 3 entries, where each key is a dataset name (maximum length of 256 characters, must contain only letters and underscores) and each value is an array of up to 50 column names. Column names can be up to 150 characters long, must start with a letter or underscore, and can contain only letters, numbers, and underscores.

        

      
        - *(string) --* 

        
          - *(list) --* 

          
            - *(string) --* 

            
        
    
  
    
    - **autoTrainingConfig** *(dict) --* 

      Specifies the automatic training configuration to use.

      

    
      - **schedulingExpression** *(string) --* 

        Specifies how often to automatically train new solution versions. Specify a rate expression in rate(*value* *unit*) format. For value, specify a number between 1 and 30. For unit, specify ``day`` or ``days``. For example, to automatically create a new solution version every 5 days, specify ``rate(5 days)``. The default is every 7 days.

         

        For more information about auto training, see `Creating and configuring a solution <https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html>`__.

        

      
    
  
  :type tags: list
  :param tags: 

    A list of `tags <https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html>`__ to apply to the solution.

    

  
    - *(dict) --* 

      The optional metadata that you apply to resources to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define. For more information see `Tagging Amazon Personalize resources <https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html>`__.

      

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

        One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.

        

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

        The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).

        

      
    

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

    
    ::

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

    

    - *(dict) --* 
      

      - **solutionArn** *(string) --* 

        The ARN of the solution.

        
  
  **Exceptions**
  
  *   :py:class:`Personalize.Client.exceptions.InvalidInputException`

  
  *   :py:class:`Personalize.Client.exceptions.ResourceAlreadyExistsException`

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

  
  *   :py:class:`Personalize.Client.exceptions.LimitExceededException`

  
  *   :py:class:`Personalize.Client.exceptions.ResourceInUseException`

  
  *   :py:class:`Personalize.Client.exceptions.TooManyTagsException`

  