create-solution¶

Description¶

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.

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

Synopsis¶

  create-solution
--name <value>
[--perform-hpo | --no-perform-hpo]
[--perform-auto-ml | --no-perform-auto-ml]
[--recipe-arn <value>]
--dataset-group-arn <value>
[--event-type <value>]
[--solution-config <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--cli-auto-prompt <value>]

Options¶

--name (string)

The name for the solution.

--perform-hpo | --no-perform-hpo (boolean)

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 | --no-perform-auto-ml (boolean)

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.

--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.

eventValueThreshold -> (string)

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

hpoConfig -> (structure)

Describes the properties for hyperparameter optimization (HPO).

hpoObjective -> (structure)

The metric to optimize during HPO.

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 -> (structure)

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 -> (structure)

The hyperparameters and their allowable ranges.

integerHyperParameterRanges -> (list)

The integer-valued hyperparameters and their ranges.

(structure)

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.

(structure)

Provides the name and range of a continuous hyperparameter.

name -> (string)

The name of the hyperparameter.

minValue -> (double)

The minimum allowable value for the hyperparameter.

maxValue -> (double)

The maximum allowable value for the hyperparameter.

categoricalHyperParameterRanges -> (list)

The categorical hyperparameters and their ranges.

(structure)

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 -> (map)

Lists the hyperparameter names and ranges.

key -> (string)

value -> (string)

featureTransformationParameters -> (map)

Lists the feature transformation parameters.

key -> (string)

value -> (string)

autoMLConfig -> (structure)

The AutoMLConfig 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)

JSON Syntax:

{
  "eventValueThreshold": "string",
  "hpoConfig": {
    "hpoObjective": {
      "type": "string",
      "metricName": "string",
      "metricRegex": "string"
    },
    "hpoResourceConfig": {
      "maxNumberOfTrainingJobs": "string",
      "maxParallelTrainingJobs": "string"
    },
    "algorithmHyperParameterRanges": {
      "integerHyperParameterRanges": [
        {
          "name": "string",
          "minValue": integer,
          "maxValue": integer
        }
        ...
      ],
      "continuousHyperParameterRanges": [
        {
          "name": "string",
          "minValue": double,
          "maxValue": double
        }
        ...
      ],
      "categoricalHyperParameterRanges": [
        {
          "name": "string",
          "values": ["string", ...]
        }
        ...
      ]
    }
  },
  "algorithmHyperParameters": {"string": "string"
    ...},
  "featureTransformationParameters": {"string": "string"
    ...},
  "autoMLConfig": {
    "metricName": "string",
    "recipeList": ["string", ...]
  }
}

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those 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. This may not be specified along with --cli-input-yaml.

--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. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

--cli-auto-prompt (boolean) Automatically prompt for CLI input parameters.

See ‘aws help’ for descriptions of global parameters.

Output¶

solutionArn -> (string)

The ARN of the solution.

Table of Contents

Feedback

User Guide

create-solution¶

Description¶

Synopsis¶

Options¶

Output¶