CfnSolution

class aws_cdk.aws_personalize.CfnSolution(scope, id, *, dataset_group_arn, name, event_type=None, perform_auto_ml=None, perform_hpo=None, recipe_arn=None, solution_config=None)

Bases: CfnResource

After you create a solution, you can’t change its configuration.

By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. You can’t stop automatic training for a solution. To avoid unnecessary costs, make sure to delete the solution when you are finished. For information about training costs, see Amazon Personalize pricing .

An object that provides information about a solution. A solution includes the custom recipe, customized parameters, and trained models (Solution Versions) that Amazon Personalize uses to generate recommendations.

After you create a solution, you can’t change its configuration. If you need to make changes, you can clone the solution with the Amazon Personalize console or create a new one.

see:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-personalize-solution.html

cloudformationResource:

AWS::Personalize::Solution

exampleMetadata:

fixture=_generated

Example:
# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_personalize as personalize

# auto_ml_config: Any
# hpo_config: Any

cfn_solution = personalize.CfnSolution(self, "MyCfnSolution",
    dataset_group_arn="datasetGroupArn",
    name="name",

    # the properties below are optional
    event_type="eventType",
    perform_auto_ml=False,
    perform_hpo=False,
    recipe_arn="recipeArn",
    solution_config=personalize.CfnSolution.SolutionConfigProperty(
        algorithm_hyper_parameters={
            "algorithm_hyper_parameters_key": "algorithmHyperParameters"
        },
        auto_ml_config=auto_ml_config,
        event_value_threshold="eventValueThreshold",
        feature_transformation_parameters={
            "feature_transformation_parameters_key": "featureTransformationParameters"
        },
        hpo_config=hpo_config
    )
)

Parameters:

scope (Construct) – Scope in which this resource is defined.
id (str) – Construct identifier for this resource (unique in its scope).
dataset_group_arn (str) – The Amazon Resource Name (ARN) of the dataset group that provides the training data.
name (str) – The name of the solution.
event_type (Optional[str]) – The event type (for example, ‘click’ or ‘like’) that is used for training the model. If no eventType is provided, Amazon Personalize uses all interactions for training with equal weight regardless of type.
perform_auto_ml (Union[bool, IResolvable, None]) –

We don’t recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see Determining your use case. When true, Amazon Personalize performs a search for the best USER_PERSONALIZATION recipe from the list specified in the solution configuration ( recipeArn must not be specified). When false (the default), Amazon Personalize uses recipeArn for training.
perform_hpo (Union[bool, IResolvable, None]) – Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is false .
recipe_arn (Optional[str]) – The ARN of the recipe used to create the solution. This is required when performAutoML is false.
solution_config (Union[IResolvable, SolutionConfigProperty, Dict[str, Any], None]) – Describes the configuration properties for the solution.

Methods

add_deletion_override(path)

Syntactic sugar for addOverride(path, undefined).

Parameters:: path (str) – The path of the value to delete.
Return type:: None

add_dependency(target)

Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.

Parameters:

target (CfnResource) –

Return type:

None

add_depends_on(target)

(deprecated) Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

Parameters:

target (CfnResource) –

Deprecated:

use addDependency

Stability:

deprecated

Return type:

None

add_metadata(key, value)

Add a value to the CloudFormation Resource Metadata.

Parameters:

key (str) –
value (Any) –

See:

Return type:

None

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

add_override(path, value)

Adds an override to the synthesized CloudFormation resource.

To add a property override, either use addPropertyOverride or prefix path with “Properties.” (i.e. Properties.TopicName).

If the override is nested, separate each nested level using a dot (.) in the path parameter. If there is an array as part of the nesting, specify the index in the path.

To include a literal . in the property name, prefix with a \. In most programming languages you will need to write this as "\\." because the \ itself will need to be escaped.

For example:

cfn_resource.add_override("Properties.GlobalSecondaryIndexes.0.Projection.NonKeyAttributes", ["myattribute"])
cfn_resource.add_override("Properties.GlobalSecondaryIndexes.1.ProjectionType", "INCLUDE")

would add the overrides Example:

"Properties": {
  "GlobalSecondaryIndexes": [
    {
      "Projection": {
        "NonKeyAttributes": [ "myattribute" ]
        ...
      }
      ...
    },
    {
      "ProjectionType": "INCLUDE"
      ...
    },
  ]
  ...
}

The value argument to addOverride will not be processed or translated in any way. Pass raw JSON values in here with the correct capitalization for CloudFormation. If you pass CDK classes or structs, they will be rendered with lowercased key names, and CloudFormation will reject the template.

Parameters:

path (str) –
- The path of the property, you can use dot notation to override values in complex types. Any intermediate keys will be created as needed.
value (Any) –
- The value. Could be primitive or complex.

Return type:

None

add_property_deletion_override(property_path)

Adds an override that deletes the value of a property from the resource definition.

Parameters:: property_path (str) – The path to the property.
Return type:: None

add_property_override(property_path, value)

Adds an override to a resource property.

Syntactic sugar for addOverride("Properties.<...>", value).

Parameters:

property_path (str) – The path of the property.
value (Any) – The value.

Return type:

None

apply_removal_policy(policy=None, *, apply_to_update_replace_policy=None, default=None)

Sets the deletion policy of the resource based on the removal policy specified.

The Removal Policy controls what happens to this resource when it stops being managed by CloudFormation, either because you’ve removed it from the CDK application or because you’ve made a change that requires the resource to be replaced.

The resource can be deleted (RemovalPolicy.DESTROY), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN). In some cases, a snapshot can be taken of the resource prior to deletion (RemovalPolicy.SNAPSHOT). A list of resources that support this policy can be found in the following link:

Parameters:

policy (Optional[RemovalPolicy]) –
apply_to_update_replace_policy (Optional[bool]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: true
default (Optional[RemovalPolicy]) – The default policy to apply in case the removal policy is not defined. Default: - Default value is resource specific. To determine the default value for a resource, please consult that specific resource’s documentation.

See:

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-attribute-deletionpolicy.html#aws-attribute-deletionpolicy-options

Return type:

None

get_att(attribute_name, type_hint=None)

Returns a token for an runtime attribute of this resource.

Ideally, use generated attribute accessors (e.g. resource.arn), but this can be used for future compatibility in case there is no generated attribute.

Parameters:

attribute_name (str) – The name of the attribute.
type_hint (Optional[ResolutionTypeHint]) –

Return type:

Reference

get_metadata(key)

Retrieve a value value from the CloudFormation Resource Metadata.

Parameters:

key (str) –

See:

Return type:

Any

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

inspect(inspector)

Examines the CloudFormation resource and discloses attributes.

Parameters:: inspector (TreeInspector) – tree inspector to collect and process attributes.
Return type:: None

obtain_dependencies()

Retrieves an array of resources this resource depends on.

This assembles dependencies on resources across stacks (including nested stacks) automatically.

Return type:: List[Union[Stack, CfnResource]]

obtain_resource_dependencies()

Get a shallow copy of dependencies between this resource and other resources in the same stack.

Return type:: List[CfnResource]

override_logical_id(new_logical_id)

Overrides the auto-generated logical ID with a specific ID.

Parameters:: new_logical_id (str) – The new logical ID to use for this stack element.
Return type:: None

remove_dependency(target)

Indicates that this resource no longer depends on another resource.

This can be used for resources across stacks (including nested stacks) and the dependency will automatically be removed from the relevant scope.

Parameters:

target (CfnResource) –

Return type:

None

replace_dependency(target, new_target)

Replaces one dependency with another.

Parameters:

target (CfnResource) – The dependency to replace.
new_target (CfnResource) – The new dependency to add.

Return type:

None

to_string()

Returns a string representation of this construct.

Return type:: str
Returns:: a string representation of this resource

Attributes

CFN_RESOURCE_TYPE_NAME = 'AWS::Personalize::Solution'

attr_solution_arn

The Amazon Resource Name (ARN) of the solution.

CloudformationAttribute:: SolutionArn

cfn_options: Options for this resource, such as condition, update policy etc.

cfn_resource_type: AWS resource type.

creation_stack

return:

the stack trace of the point where this Resource was created from, sourced from the +metadata+ entry typed +aws:cdk:logicalId+, and with the bottom-most node +internal+ entries filtered.

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

event_type: The event type (for example, ‘click’ or ‘like’) that is used for training the model.

logical_id

The logical ID for this CloudFormation stack element.

The logical ID of the element is calculated from the path of the resource node in the construct tree.

To override this value, use overrideLogicalId(newLogicalId).

Returns:

the logical ID as a stringified token. This value will only get resolved during synthesis.

name: The name of the solution.

node: The tree node.

perform_auto_ml: We don’t recommend enabling automated machine learning.

perform_hpo: Whether to perform hyperparameter optimization (HPO) on the chosen recipe.

recipe_arn: The ARN of the recipe used to create the solution.

ref

Return a string that will be resolved to a CloudFormation { Ref } for this element.

If, by any chance, the intrinsic reference of a resource is not a string, you could coerce it to an IResolvable through Lazy.any({ produce: resource.ref }).

solution_config: Describes the configuration properties for the solution.

stack

The stack in which this element is defined.

CfnElements must be defined within a stack scope (directly or indirectly).

Static Methods

classmethod is_cfn_element(x)

Returns true if a construct is a stack element (i.e. part of the synthesized cloudformation template).

Uses duck-typing instead of instanceof to allow stack elements from different versions of this library to be included in the same stack.

Parameters:

x (Any) –

Return type:

bool

Returns:

The construct as a stack element or undefined if it is not a stack element.

classmethod is_cfn_resource(x)

Check whether the given object is a CfnResource.

Parameters:

x (Any) –

Return type:

bool

classmethod is_construct(x)

Checks if x is a construct.

Use this method instead of instanceof to properly detect Construct instances, even when the construct library is symlinked.

Explanation: in JavaScript, multiple copies of the constructs library on disk are seen as independent, completely different libraries. As a consequence, the class Construct in each copy of the constructs library is seen as a different class, and an instance of one class will not test as instanceof the other class. npm install will not create installations like this, but users may manually symlink construct libraries together or use a monorepo tool: in those cases, multiple copies of the constructs library can be accidentally installed, and instanceof will behave unpredictably. It is safest to avoid using instanceof, and using this type-testing method instead.

Parameters:: x (Any) – Any object.
Return type:: bool
Returns:: true if x is an object created from a class which extends Construct.

AlgorithmHyperParameterRangesProperty

class CfnSolution.AlgorithmHyperParameterRangesProperty(*, categorical_hyper_parameter_ranges=None, continuous_hyper_parameter_ranges=None, integer_hyper_parameter_ranges=None)

Bases: object

Specifies the hyperparameters and their ranges.

Hyperparameters can be categorical, continuous, or integer-valued.

Parameters:

categorical_hyper_parameter_ranges (Union[IResolvable, Sequence[Union[IResolvable, CategoricalHyperParameterRangeProperty, Dict[str, Any]]], None]) – Provides the name and range of a categorical hyperparameter.
continuous_hyper_parameter_ranges (Union[IResolvable, Sequence[Union[IResolvable, ContinuousHyperParameterRangeProperty, Dict[str, Any]]], None]) – Provides the name and range of a continuous hyperparameter.
integer_hyper_parameter_ranges (Union[IResolvable, Sequence[Union[IResolvable, IntegerHyperParameterRangeProperty, Dict[str, Any]]], None]) – Provides the name and range of an integer-valued hyperparameter.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-algorithmhyperparameterranges.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_personalize as personalize

algorithm_hyper_parameter_ranges_property = personalize.CfnSolution.AlgorithmHyperParameterRangesProperty(
    categorical_hyper_parameter_ranges=[personalize.CfnSolution.CategoricalHyperParameterRangeProperty(
        name="name",
        values=["values"]
    )],
    continuous_hyper_parameter_ranges=[personalize.CfnSolution.ContinuousHyperParameterRangeProperty(
        max_value=123,
        min_value=123,
        name="name"
    )],
    integer_hyper_parameter_ranges=[personalize.CfnSolution.IntegerHyperParameterRangeProperty(
        max_value=123,
        min_value=123,
        name="name"
    )]
)

Attributes

categorical_hyper_parameter_ranges

Provides the name and range of a categorical hyperparameter.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-algorithmhyperparameterranges.html#cfn-personalize-solution-algorithmhyperparameterranges-categoricalhyperparameterranges

continuous_hyper_parameter_ranges

Provides the name and range of a continuous hyperparameter.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-algorithmhyperparameterranges.html#cfn-personalize-solution-algorithmhyperparameterranges-continuoushyperparameterranges

integer_hyper_parameter_ranges

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

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-algorithmhyperparameterranges.html#cfn-personalize-solution-algorithmhyperparameterranges-integerhyperparameterranges

AutoMLConfigProperty

class CfnSolution.AutoMLConfigProperty(*, metric_name=None, recipe_list=None)

Bases: object

When the solution performs AutoML ( performAutoML is true in CreateSolution ), Amazon Personalize determines which recipe, from the specified list, optimizes the given metric. Amazon Personalize then uses that recipe for the solution.

Parameters:

metric_name (Optional[str]) – The metric to optimize.
recipe_list (Optional[Sequence[str]]) – The list of candidate recipes.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-automlconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_personalize as personalize

auto_mLConfig_property = personalize.CfnSolution.AutoMLConfigProperty(
    metric_name="metricName",
    recipe_list=["recipeList"]
)

Attributes

metric_name

The metric to optimize.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-automlconfig.html#cfn-personalize-solution-automlconfig-metricname

recipe_list

The list of candidate recipes.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-automlconfig.html#cfn-personalize-solution-automlconfig-recipelist

CategoricalHyperParameterRangeProperty

class CfnSolution.CategoricalHyperParameterRangeProperty(*, name=None, values=None)

Bases: object

Provides the name and range of a categorical hyperparameter.

Parameters:

name (Optional[str]) – The name of the hyperparameter.
values (Optional[Sequence[str]]) – A list of the categories for the hyperparameter.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-categoricalhyperparameterrange.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_personalize as personalize

categorical_hyper_parameter_range_property = personalize.CfnSolution.CategoricalHyperParameterRangeProperty(
    name="name",
    values=["values"]
)

Attributes

name

The name of the hyperparameter.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-categoricalhyperparameterrange.html#cfn-personalize-solution-categoricalhyperparameterrange-name

values

A list of the categories for the hyperparameter.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-categoricalhyperparameterrange.html#cfn-personalize-solution-categoricalhyperparameterrange-values

ContinuousHyperParameterRangeProperty

class CfnSolution.ContinuousHyperParameterRangeProperty(*, max_value=None, min_value=None, name=None)

Bases: object

Provides the name and range of a continuous hyperparameter.

Parameters:

max_value (Union[int, float, None]) – The maximum allowable value for the hyperparameter.
min_value (Union[int, float, None]) – The minimum allowable value for the hyperparameter.
name (Optional[str]) – The name of the hyperparameter.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-continuoushyperparameterrange.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_personalize as personalize

continuous_hyper_parameter_range_property = personalize.CfnSolution.ContinuousHyperParameterRangeProperty(
    max_value=123,
    min_value=123,
    name="name"
)

Attributes

max_value

The maximum allowable value for the hyperparameter.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-continuoushyperparameterrange.html#cfn-personalize-solution-continuoushyperparameterrange-maxvalue

min_value

The minimum allowable value for the hyperparameter.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-continuoushyperparameterrange.html#cfn-personalize-solution-continuoushyperparameterrange-minvalue

name

The name of the hyperparameter.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-continuoushyperparameterrange.html#cfn-personalize-solution-continuoushyperparameterrange-name

HpoConfigProperty

class CfnSolution.HpoConfigProperty(*, algorithm_hyper_parameter_ranges=None, hpo_objective=None, hpo_resource_config=None)

Bases: object

Describes the properties for hyperparameter optimization (HPO).

Parameters:

algorithm_hyper_parameter_ranges (Union[IResolvable, AlgorithmHyperParameterRangesProperty, Dict[str, Any], None]) – The hyperparameters and their allowable ranges.
hpo_objective (Union[IResolvable, HpoObjectiveProperty, Dict[str, Any], None]) – The metric to optimize during HPO. .. epigraph:: Amazon Personalize doesn’t support configuring the hpoObjective at this time.
hpo_resource_config (Union[IResolvable, HpoResourceConfigProperty, Dict[str, Any], None]) – Describes the resource configuration for HPO.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-hpoconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_personalize as personalize

hpo_config_property = personalize.CfnSolution.HpoConfigProperty(
    algorithm_hyper_parameter_ranges=personalize.CfnSolution.AlgorithmHyperParameterRangesProperty(
        categorical_hyper_parameter_ranges=[personalize.CfnSolution.CategoricalHyperParameterRangeProperty(
            name="name",
            values=["values"]
        )],
        continuous_hyper_parameter_ranges=[personalize.CfnSolution.ContinuousHyperParameterRangeProperty(
            max_value=123,
            min_value=123,
            name="name"
        )],
        integer_hyper_parameter_ranges=[personalize.CfnSolution.IntegerHyperParameterRangeProperty(
            max_value=123,
            min_value=123,
            name="name"
        )]
    ),
    hpo_objective=personalize.CfnSolution.HpoObjectiveProperty(
        metric_name="metricName",
        metric_regex="metricRegex",
        type="type"
    ),
    hpo_resource_config=personalize.CfnSolution.HpoResourceConfigProperty(
        max_number_of_training_jobs="maxNumberOfTrainingJobs",
        max_parallel_training_jobs="maxParallelTrainingJobs"
    )
)

Attributes

algorithm_hyper_parameter_ranges

The hyperparameters and their allowable ranges.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-hpoconfig.html#cfn-personalize-solution-hpoconfig-algorithmhyperparameterranges

hpo_objective

The metric to optimize during HPO.

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

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-hpoconfig.html#cfn-personalize-solution-hpoconfig-hpoobjective

hpo_resource_config

Describes the resource configuration for HPO.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-hpoconfig.html#cfn-personalize-solution-hpoconfig-hporesourceconfig

HpoObjectiveProperty

class CfnSolution.HpoObjectiveProperty(*, metric_name=None, metric_regex=None, type=None)

Bases: object

The metric to optimize during hyperparameter optimization (HPO).

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

Parameters:

metric_name (Optional[str]) – The name of the metric.
metric_regex (Optional[str]) – A regular expression for finding the metric in the training job logs.
type (Optional[str]) – The type of the metric. Valid values are Maximize and Minimize .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-hpoobjective.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_personalize as personalize

hpo_objective_property = personalize.CfnSolution.HpoObjectiveProperty(
    metric_name="metricName",
    metric_regex="metricRegex",
    type="type"
)

Attributes

metric_name

The name of the metric.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-hpoobjective.html#cfn-personalize-solution-hpoobjective-metricname

metric_regex

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

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-hpoobjective.html#cfn-personalize-solution-hpoobjective-metricregex

type

The type of the metric.

Valid values are Maximize and Minimize .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-hpoobjective.html#cfn-personalize-solution-hpoobjective-type

HpoResourceConfigProperty

class CfnSolution.HpoResourceConfigProperty(*, max_number_of_training_jobs=None, max_parallel_training_jobs=None)

Bases: object

Describes the resource configuration for hyperparameter optimization (HPO).

Parameters:

max_number_of_training_jobs (Optional[str]) – The maximum number of training jobs when you create a solution version. The maximum value for maxNumberOfTrainingJobs is 40 .
max_parallel_training_jobs (Optional[str]) – The maximum number of parallel training jobs when you create a solution version. The maximum value for maxParallelTrainingJobs is 10 .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-hporesourceconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_personalize as personalize

hpo_resource_config_property = personalize.CfnSolution.HpoResourceConfigProperty(
    max_number_of_training_jobs="maxNumberOfTrainingJobs",
    max_parallel_training_jobs="maxParallelTrainingJobs"
)

Attributes

max_number_of_training_jobs

The maximum number of training jobs when you create a solution version.

The maximum value for maxNumberOfTrainingJobs is 40 .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-hporesourceconfig.html#cfn-personalize-solution-hporesourceconfig-maxnumberoftrainingjobs

max_parallel_training_jobs

The maximum number of parallel training jobs when you create a solution version.

The maximum value for maxParallelTrainingJobs is 10 .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-hporesourceconfig.html#cfn-personalize-solution-hporesourceconfig-maxparalleltrainingjobs

IntegerHyperParameterRangeProperty

class CfnSolution.IntegerHyperParameterRangeProperty(*, max_value=None, min_value=None, name=None)

Bases: object

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

Parameters:

max_value (Union[int, float, None]) – The maximum allowable value for the hyperparameter.
min_value (Union[int, float, None]) – The minimum allowable value for the hyperparameter.
name (Optional[str]) – The name of the hyperparameter.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-integerhyperparameterrange.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_personalize as personalize

integer_hyper_parameter_range_property = personalize.CfnSolution.IntegerHyperParameterRangeProperty(
    max_value=123,
    min_value=123,
    name="name"
)

Attributes

max_value

The maximum allowable value for the hyperparameter.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-integerhyperparameterrange.html#cfn-personalize-solution-integerhyperparameterrange-maxvalue

min_value

The minimum allowable value for the hyperparameter.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-integerhyperparameterrange.html#cfn-personalize-solution-integerhyperparameterrange-minvalue

name

The name of the hyperparameter.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-integerhyperparameterrange.html#cfn-personalize-solution-integerhyperparameterrange-name

SolutionConfigProperty

class CfnSolution.SolutionConfigProperty(*, algorithm_hyper_parameters=None, auto_ml_config=None, event_value_threshold=None, feature_transformation_parameters=None, hpo_config=None)

Bases: object

Describes the configuration properties for the solution.

Parameters:

algorithm_hyper_parameters (Union[IResolvable, Mapping[str, str], None]) – Lists the algorithm hyperparameters and their values.
auto_ml_config (Optional[Any]) – The AutoMLConfig object containing a list of recipes to search when AutoML is performed.
event_value_threshold (Optional[str]) – Only events with a value greater than or equal to this threshold are used for training a model.
feature_transformation_parameters (Union[IResolvable, Mapping[str, str], None]) – Lists the feature transformation parameters.
hpo_config (Optional[Any]) – Describes the properties for hyperparameter optimization (HPO).

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-solutionconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_personalize as personalize

# auto_ml_config: Any
# hpo_config: Any

solution_config_property = personalize.CfnSolution.SolutionConfigProperty(
    algorithm_hyper_parameters={
        "algorithm_hyper_parameters_key": "algorithmHyperParameters"
    },
    auto_ml_config=auto_ml_config,
    event_value_threshold="eventValueThreshold",
    feature_transformation_parameters={
        "feature_transformation_parameters_key": "featureTransformationParameters"
    },
    hpo_config=hpo_config
)