aws personalize create-solution
Creates the configuration for training a model. A trained model is known as a solution. After the configuration is created, you train the model (create a solution) by calling the CreateSolutionVersion operation. Every time you call CreateSolutionVersion, a new version of the solution is created. After creating a solution version, you check its accuracy by calling GetSolutionMetrics. When you are satisfied with the version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API. To train a model, Amazon Personalize requires training data and a recipe. The training data comes from the dataset group that you provide in the request. A recipe specifies the training algorithm and a feature transformation. You can specify one of the predefined recipes provided by Amazon Personalize. Alternatively, you can specify performAutoML and Amazon Personalize will analyze your data and select the optimum USER_PERSONALIZATION recipe for you. 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. Wait until the status shows as ACTIVE before calling CreateSolutionVersion. Related APIs ListSolutions CreateSolutionVersion DescribeSolution DeleteSolution ListSolutionVersions DescribeSolutionVersion
Options
| Name | Description |
|---|---|
--name <string> | The name for the solution |
--perform-hpo | 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 |
--no-perform-hpo | 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 |
--perform-auto-ml | 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 |
--no-perform-auto-ml | 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 |
--recipe-arn <string> | The ARN of the recipe to use for model training. Only specified when performAutoML is false |
--dataset-group-arn <string> | The Amazon Resource Name (ARN) of the dataset group that provides the training data |
--event-type <string> | 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 |
--solution-config <structure> | The configuration to use with the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration. Amazon Personalize doesn't support configuring the hpoObjective at this time |
--cli-input-json <string> | Performs service operation based on the JSON string provided. The JSON string follows the format provided by ``--generate-cli-skeleton``. If other arguments are provided on the command line, the CLI values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally |
--generate-cli-skeleton <string> | Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value ``input``, prints a sample input JSON that can be used as an argument for ``--cli-input-json``. If provided with the value ``output``, it validates the command inputs and returns a sample output JSON for that command |