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

A CloudFormation AWS::Personalize::Solution.

An object that provides information about a solution. A solution is a trained model that can be deployed as a campaign.

CloudformationResource:: AWS::Personalize::Solution
Link:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-personalize-solution.html
ExampleMetadata:: fixture=_generated

Example:

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

Create a new AWS::Personalize::Solution.

Parameters:

scope (Construct) –
- scope in which this resource is defined.
id (str) –
- scoped id of the resource.
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.
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_depends_on(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_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 intermdediate 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).

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 resoure, please consult that specific resource’s documentation.

Return type:

None

get_att(attribute_name)

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.
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

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

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.

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

event_type

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.

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

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.

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

node: The construct tree node associated with this construct.

perform_auto_ml

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.

link:

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

perform_hpo

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

The default is false .

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

recipe_arn

The ARN of the recipe used to create the solution.

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

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.

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

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(construct)

Check whether the given construct is a CfnResource.

Parameters:

construct (IConstruct) –

Return type:

bool

classmethod is_construct(x)

Return whether the given object is a Construct.

Parameters:

x (Any) –

Return type:

bool

AlgorithmHyperParameterRangesProperty

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

Bases: object

Parameters:

categorical_hyper_parameter_ranges (Union[IResolvable, Sequence[Union[IResolvable, CategoricalHyperParameterRangeProperty, Dict[str, Any]]], None]) – CfnSolution.AlgorithmHyperParameterRangesProperty.CategoricalHyperParameterRanges.
continuous_hyper_parameter_ranges (Union[IResolvable, Sequence[Union[IResolvable, ContinuousHyperParameterRangeProperty, Dict[str, Any]]], None]) – CfnSolution.AlgorithmHyperParameterRangesProperty.ContinuousHyperParameterRanges.
integer_hyper_parameter_ranges (Union[IResolvable, Sequence[Union[IResolvable, IntegerHyperParameterRangeProperty, Dict[str, Any]]], None]) – CfnSolution.AlgorithmHyperParameterRangesProperty.IntegerHyperParameterRanges.

Link:

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.
import aws_cdk.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

CfnSolution.AlgorithmHyperParameterRangesProperty.CategoricalHyperParameterRanges.

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

continuous_hyper_parameter_ranges

CfnSolution.AlgorithmHyperParameterRangesProperty.ContinuousHyperParameterRanges.

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

integer_hyper_parameter_ranges

CfnSolution.AlgorithmHyperParameterRangesProperty.IntegerHyperParameterRanges.

Link:: 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

Parameters:

metric_name (Optional[str]) – CfnSolution.AutoMLConfigProperty.MetricName.
recipe_list (Optional[Sequence[str]]) – CfnSolution.AutoMLConfigProperty.RecipeList.

Link:

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.
import aws_cdk.aws_personalize as personalize

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

Attributes

metric_name

CfnSolution.AutoMLConfigProperty.MetricName.

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

recipe_list

CfnSolution.AutoMLConfigProperty.RecipeList.

Link:: 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

Parameters:

name (Optional[str]) – CfnSolution.CategoricalHyperParameterRangeProperty.Name.
values (Optional[Sequence[str]]) – CfnSolution.CategoricalHyperParameterRangeProperty.Values.

Link:

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.
import aws_cdk.aws_personalize as personalize

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

Attributes

name

CfnSolution.CategoricalHyperParameterRangeProperty.Name.

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

values

CfnSolution.CategoricalHyperParameterRangeProperty.Values.

Link:: 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

Parameters:

max_value (Union[int, float, None]) – CfnSolution.ContinuousHyperParameterRangeProperty.MaxValue.
min_value (Union[int, float, None]) – CfnSolution.ContinuousHyperParameterRangeProperty.MinValue.
name (Optional[str]) – CfnSolution.ContinuousHyperParameterRangeProperty.Name.

Link:

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.
import aws_cdk.aws_personalize as personalize

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

Attributes

max_value

CfnSolution.ContinuousHyperParameterRangeProperty.MaxValue.

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

min_value

CfnSolution.ContinuousHyperParameterRangeProperty.MinValue.

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

name

CfnSolution.ContinuousHyperParameterRangeProperty.Name.

Link:: 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

Parameters:

algorithm_hyper_parameter_ranges (Union[IResolvable, AlgorithmHyperParameterRangesProperty, Dict[str, Any], None]) – CfnSolution.HpoConfigProperty.AlgorithmHyperParameterRanges.
hpo_objective (Union[IResolvable, HpoObjectiveProperty, Dict[str, Any], None]) – CfnSolution.HpoConfigProperty.HpoObjective.
hpo_resource_config (Union[IResolvable, HpoResourceConfigProperty, Dict[str, Any], None]) – CfnSolution.HpoConfigProperty.HpoResourceConfig.

Link:

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.
import aws_cdk.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

CfnSolution.HpoConfigProperty.AlgorithmHyperParameterRanges.

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

hpo_objective

CfnSolution.HpoConfigProperty.HpoObjective.

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

hpo_resource_config

CfnSolution.HpoConfigProperty.HpoResourceConfig.

Link:: 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

Parameters:

metric_name (Optional[str]) – CfnSolution.HpoObjectiveProperty.MetricName.
metric_regex (Optional[str]) – CfnSolution.HpoObjectiveProperty.MetricRegex.
type (Optional[str]) – CfnSolution.HpoObjectiveProperty.Type.

Link:

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.
import aws_cdk.aws_personalize as personalize

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

Attributes

metric_name

CfnSolution.HpoObjectiveProperty.MetricName.

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

metric_regex

CfnSolution.HpoObjectiveProperty.MetricRegex.

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

type

CfnSolution.HpoObjectiveProperty.Type.

Link:: 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

Parameters:

max_number_of_training_jobs (Optional[str]) – CfnSolution.HpoResourceConfigProperty.MaxNumberOfTrainingJobs.
max_parallel_training_jobs (Optional[str]) – CfnSolution.HpoResourceConfigProperty.MaxParallelTrainingJobs.

Link:

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.
import aws_cdk.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

CfnSolution.HpoResourceConfigProperty.MaxNumberOfTrainingJobs.

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

max_parallel_training_jobs

CfnSolution.HpoResourceConfigProperty.MaxParallelTrainingJobs.

Link:: 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

Parameters:

max_value (Union[int, float, None]) – CfnSolution.IntegerHyperParameterRangeProperty.MaxValue.
min_value (Union[int, float, None]) – CfnSolution.IntegerHyperParameterRangeProperty.MinValue.
name (Optional[str]) – CfnSolution.IntegerHyperParameterRangeProperty.Name.

Link:

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.
import aws_cdk.aws_personalize as personalize

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

Attributes

max_value

CfnSolution.IntegerHyperParameterRangeProperty.MaxValue.

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

min_value

CfnSolution.IntegerHyperParameterRangeProperty.MinValue.

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

name

CfnSolution.IntegerHyperParameterRangeProperty.Name.

Link:: 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 hyperparameter names and ranges.
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).

Link:

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.
import aws_cdk.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
)