PersonalizeClient

Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.

Installation

NPM
npm install @aws-sdk/client-personalize
Yarn
yarn add @aws-sdk/client-personalize
pnpm
pnpm add @aws-sdk/client-personalize

PersonalizeClient Operations

Command
Summary
CreateBatchInferenceJobCommand

Generates batch recommendations based on a list of items or users stored in Amazon S3 and exports the recommendations to an Amazon S3 bucket.

To generate batch recommendations, specify the ARN of a solution version and an Amazon S3 URI for the input and output data. For user personalization, popular items, and personalized ranking solutions, the batch inference job generates a list of recommended items for each user ID in the input file. For related items solutions, the job generates a list of recommended items for each item ID in the input file.

For more information, see Creating a batch inference job  .

If you use the Similar-Items recipe, Amazon Personalize can add descriptive themes to batch recommendations. To generate themes, set the job's mode to THEME_GENERATION and specify the name of the field that contains item names in the input data.

For more information about generating themes, see Batch recommendations with themes from Content Generator  .

You can't get batch recommendations with the Trending-Now or Next-Best-Action recipes.

CreateBatchSegmentJobCommand

Creates a batch segment job. The operation can handle up to 50 million records and the input file must be in JSON format. For more information, see Getting batch recommendations and user segments .

CreateCampaignCommand

You incur campaign costs while it is active. To avoid unnecessary costs, make sure to delete the campaign when you are finished. For information about campaign costs, see Amazon Personalize pricing .

Creates a campaign that deploys a solution version. When a client calls the GetRecommendations  and GetPersonalizedRanking  APIs, a campaign is specified in the request.

Minimum Provisioned TPS and Auto-Scaling

A high minProvisionedTPS will increase your cost. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary.

When you create an Amazon Personalize campaign, you can specify the minimum provisioned transactions per second (minProvisionedTPS) for the campaign. This is the baseline transaction throughput for the campaign provisioned by Amazon Personalize. It sets the minimum billing charge for the campaign while it is active. A transaction is a single GetRecommendations or GetPersonalizedRanking request. The default minProvisionedTPS is 1.

If your TPS increases beyond the minProvisionedTPS, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minProvisionedTPS. There's a short time delay while the capacity is increased that might cause loss of transactions. When your traffic reduces, capacity returns to the minProvisionedTPS.

You are charged for the the minimum provisioned TPS or, if your requests exceed the minProvisionedTPS, the actual TPS. The actual TPS is the total number of recommendation requests you make. We recommend starting with a low minProvisionedTPS, track your usage using Amazon CloudWatch metrics, and then increase the minProvisionedTPS as necessary.

For more information about campaign costs, see Amazon Personalize pricing .

Status

A campaign 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 campaign status, call DescribeCampaign .

Wait until the status of the campaign is ACTIVE before asking the campaign for recommendations.

Related APIs

CreateDataDeletionJobCommand

Creates a batch job that deletes all references to specific users from an Amazon Personalize dataset group in batches. You specify the users to delete in a CSV file of userIds in an Amazon S3 bucket. After a job completes, Amazon Personalize no longer trains on the users’ data and no longer considers the users when generating user segments. For more information about creating a data deletion job, see Deleting users .

  • Your input file must be a CSV file with a single USER_ID column that lists the users IDs. For more information about preparing the CSV file, see Preparing your data deletion file and uploading it to Amazon S3 .

  • To give Amazon Personalize permission to access your input CSV file of userIds, you must specify an IAM service role that has permission to read from the data source. This role needs GetObject and ListBucket permissions for the bucket and its content. These permissions are the same as importing data. For information on granting access to your Amazon S3 bucket, see Giving Amazon Personalize Access to Amazon S3 Resources .

After you create a job, it can take up to a day to delete all references to the users from datasets and models. Until the job completes, Amazon Personalize continues to use the data when training. And if you use a User Segmentation recipe, the users might appear in user segments.

Status

A data deletion job can have one of the following statuses:

  • PENDING IN_PROGRESS COMPLETED -or- FAILED

To get the status of the data deletion job, call DescribeDataDeletionJob  API operation and specify the Amazon Resource Name (ARN) of the job. If the status is FAILED, the response includes a failureReason key, which describes why the job failed.

Related APIs

CreateDatasetCommand

Creates an empty dataset and adds it to the specified dataset group. Use CreateDatasetImportJob  to import your training data to a dataset.

There are 5 types of datasets:

  • Item interactions

  • Items

  • Users

  • Action interactions

  • Actions

Each dataset type has an associated schema with required field types. Only the Item interactions dataset is required in order to train a model (also referred to as creating a solution).

A dataset 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 dataset, call DescribeDataset .

Related APIs

CreateDatasetExportJobCommand

Creates a job that exports data from your dataset to an Amazon S3 bucket. To allow Amazon Personalize to export the training data, you must specify an service-linked IAM role that gives Amazon Personalize PutObject permissions for your Amazon S3 bucket. For information, see Exporting a dataset  in the Amazon Personalize developer guide.

Status

A dataset export job can be in one of the following states:

  • CREATE PENDING CREATE IN_PROGRESS ACTIVE -or- CREATE FAILED

To get the status of the export job, call DescribeDatasetExportJob , and specify the Amazon Resource Name (ARN) of the dataset export job. The dataset export is complete when the status shows as ACTIVE. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed.

CreateDatasetGroupCommand

Creates an empty dataset group. A dataset group is a container for Amazon Personalize resources. A dataset group can contain at most three datasets, one for each type of dataset:

  • Item interactions

  • Items

  • Users

  • Actions

  • Action interactions

A dataset group can be a Domain dataset group, where you specify a domain and use pre-configured resources like recommenders, or a Custom dataset group, where you use custom resources, such as a solution with a solution version, that you deploy with a campaign. If you start with a Domain dataset group, you can still add custom resources such as solutions and solution versions trained with recipes for custom use cases and deployed with campaigns.

A dataset group can be in one of the following states:

  • CREATE PENDING CREATE IN_PROGRESS ACTIVE -or- CREATE FAILED

  • DELETE PENDING

To get the status of the dataset group, call DescribeDatasetGroup . If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the creation failed.

You must wait until the status of the dataset group is ACTIVE before adding a dataset to the group.

You can specify an Key Management Service (KMS) key to encrypt the datasets in the group. If you specify a KMS key, you must also include an Identity and Access Management (IAM) role that has permission to access the key.

APIs that require a dataset group ARN in the request

Related APIs

CreateDatasetImportJobCommand

Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset. To allow Amazon Personalize to import the training data, you must specify an IAM service role that has permission to read from the data source, as Amazon Personalize makes a copy of your data and processes it internally. For information on granting access to your Amazon S3 bucket, see Giving Amazon Personalize Access to Amazon S3 Resources .

If you already created a recommender or deployed a custom solution version with a campaign, how new bulk records influence recommendations depends on the domain use case or recipe that you use. For more information, see How new data influences real-time recommendations .

By default, a dataset import job replaces any existing data in the dataset that you imported in bulk. To add new records without replacing existing data, specify INCREMENTAL for the import mode in the CreateDatasetImportJob operation.

Status

A dataset import job can be in one of the following states:

  • CREATE PENDING CREATE IN_PROGRESS ACTIVE -or- CREATE FAILED

To get the status of the import job, call DescribeDatasetImportJob , providing the Amazon Resource Name (ARN) of the dataset import job. The dataset import is complete when the status shows as ACTIVE. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed.

Importing takes time. You must wait until the status shows as ACTIVE before training a model using the dataset.

Related APIs

CreateEventTrackerCommand

Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents  API.

Only one event tracker can be associated with a dataset group. You will get an error if you call CreateEventTracker using the same dataset group as an existing event tracker.

When you create an event tracker, the response includes a tracking ID, which you pass as a parameter when you use the PutEvents  operation. Amazon Personalize then appends the event data to the Item interactions dataset of the dataset group you specify in your event tracker.

The event tracker 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 event tracker, call DescribeEventTracker .

The event tracker must be in the ACTIVE state before using the tracking ID.

Related APIs

CreateFilterCommand

Creates a recommendation filter. For more information, see Filtering recommendations and user segments .

CreateMetricAttributionCommand

Creates a metric attribution. A metric attribution creates reports on the data that you import into Amazon Personalize. Depending on how you imported the data, you can view reports in Amazon CloudWatch or Amazon S3. For more information, see Measuring impact of recommendations .

CreateRecommenderCommand

Creates a recommender with the recipe (a Domain dataset group use case) you specify. You create recommenders for a Domain dataset group and specify the recommender's Amazon Resource Name (ARN) when you make a GetRecommendations  request.

Minimum recommendation requests per second

A high minRecommendationRequestsPerSecond will increase your bill. We recommend starting with 1 for minRecommendationRequestsPerSecond (the default). Track your usage using Amazon CloudWatch metrics, and increase the minRecommendationRequestsPerSecond as necessary.

When you create a recommender, you can configure the recommender's minimum recommendation requests per second. The minimum recommendation requests per second (minRecommendationRequestsPerSecond) specifies the baseline recommendation request throughput provisioned by Amazon Personalize. The default minRecommendationRequestsPerSecond is 1. A recommendation request is a single GetRecommendations operation. Request throughput is measured in requests per second and Amazon Personalize uses your requests per second to derive your requests per hour and the price of your recommender usage.

If your requests per second increases beyond minRecommendationRequestsPerSecond, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minRecommendationRequestsPerSecond. There's a short time delay while the capacity is increased that might cause loss of requests.

Your bill is the greater of either the minimum requests per hour (based on minRecommendationRequestsPerSecond) or the actual number of requests. The actual request throughput used is calculated as the average requests/second within a one-hour window.We recommend starting with the default minRecommendationRequestsPerSecond, track your usage using Amazon CloudWatch metrics, and then increase the minRecommendationRequestsPerSecond as necessary.

Status

A recommender can be in one of the following states:

  • CREATE PENDING CREATE IN_PROGRESS ACTIVE -or- CREATE FAILED

  • STOP PENDING STOP IN_PROGRESS INACTIVE START PENDING START IN_PROGRESS ACTIVE

  • DELETE PENDING DELETE IN_PROGRESS

To get the recommender status, call DescribeRecommender .

Wait until the status of the recommender is ACTIVE before asking the recommender for recommendations.

Related APIs

CreateSchemaCommand

Creates an Amazon Personalize schema from the specified schema string. The schema you create must be in Avro JSON format.

Amazon Personalize recognizes three schema variants. Each schema is associated with a dataset type and has a set of required field and keywords. If you are creating a schema for a dataset in a Domain dataset group, you provide the domain of the Domain dataset group. You specify a schema when you call CreateDataset .

Related APIs

CreateSolutionCommand

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  to turn off automatic training. For information about training costs, see Amazon 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 .

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 .

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  operation.

After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions  API operation. To get its status, use the DescribeSolutionVersion .

After training completes you can evaluate model accuracy by calling GetSolutionMetrics . When you are satisfied with the solution version, you deploy it using CreateCampaign . The campaign provides recommendations to a client through the GetRecommendations  API.

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 . If you use manual training, the status must be ACTIVE before you call CreateSolutionVersion.

Related APIs

CreateSolutionVersionCommand

Trains or retrains an active solution in a Custom dataset group. A solution is created using the CreateSolution  operation and must be in the ACTIVE state before calling CreateSolutionVersion. A new version of the solution is created every time you call this operation.

Status

A solution version can be in one of the following states:

  • CREATE PENDING

  • CREATE IN_PROGRESS

  • ACTIVE

  • CREATE FAILED

  • CREATE STOPPING

  • CREATE STOPPED

To get the status of the version, call DescribeSolutionVersion . Wait until the status shows as ACTIVE before calling CreateCampaign.

If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed.

Related APIs

DeleteCampaignCommand

Removes a campaign by deleting the solution deployment. The solution that the campaign is based on is not deleted and can be redeployed when needed. A deleted campaign can no longer be specified in a GetRecommendations  request. For information on creating campaigns, see CreateCampaign .

DeleteDatasetCommand

Deletes a dataset. You can't delete a dataset if an associated DatasetImportJob or SolutionVersion is in the CREATE PENDING or IN PROGRESS state. For more information on datasets, see CreateDataset .

DeleteDatasetGroupCommand

Deletes a dataset group. Before you delete a dataset group, you must delete the following:

  • All associated event trackers.

  • All associated solutions.

  • All datasets in the dataset group.

DeleteEventTrackerCommand

Deletes the event tracker. Does not delete the dataset from the dataset group. For more information on event trackers, see CreateEventTracker .

DeleteFilterCommand

Deletes a filter.

DeleteMetricAttributionCommand

Deletes a metric attribution.

DeleteRecommenderCommand

Deactivates and removes a recommender. A deleted recommender can no longer be specified in a GetRecommendations  request.

DeleteSchemaCommand

Deletes a schema. Before deleting a schema, you must delete all datasets referencing the schema. For more information on schemas, see CreateSchema .

DeleteSolutionCommand

Deletes all versions of a solution and the Solution object itself. Before deleting a solution, you must delete all campaigns based on the solution. To determine what campaigns are using the solution, call ListCampaigns  and supply the Amazon Resource Name (ARN) of the solution. You can't delete a solution if an associated SolutionVersion is in the CREATE PENDING or IN PROGRESS state. For more information on solutions, see CreateSolution .

DescribeAlgorithmCommand

Describes the given algorithm.

DescribeBatchInferenceJobCommand

Gets the properties of a batch inference job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate the recommendations.

DescribeBatchSegmentJobCommand

Gets the properties of a batch segment job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate segments.

DescribeCampaignCommand

Describes the given campaign, including its status.

A campaign can be in one of the following states:

  • CREATE PENDING CREATE IN_PROGRESS ACTIVE -or- CREATE FAILED

  • DELETE PENDING DELETE IN_PROGRESS

When the status is CREATE FAILED, the response includes the failureReason key, which describes why.

For more information on campaigns, see CreateCampaign .

DescribeDataDeletionJobCommand

Describes the data deletion job created by CreateDataDeletionJob , including the job status.

DescribeDatasetCommand

Describes the given dataset. For more information on datasets, see CreateDataset .

DescribeDatasetExportJobCommand

Describes the dataset export job created by CreateDatasetExportJob , including the export job status.

DescribeDatasetGroupCommand

Describes the given dataset group. For more information on dataset groups, see CreateDatasetGroup .

DescribeDatasetImportJobCommand

Describes the dataset import job created by CreateDatasetImportJob , including the import job status.

DescribeEventTrackerCommand

Describes an event tracker. The response includes the trackingId and status of the event tracker. For more information on event trackers, see CreateEventTracker .

DescribeFeatureTransformationCommand

Describes the given feature transformation.

DescribeFilterCommand

Describes a filter's properties.

DescribeMetricAttributionCommand

Describes a metric attribution.

DescribeRecipeCommand

Describes a recipe.

A recipe contains three items:

  • An algorithm that trains a model.

  • Hyperparameters that govern the training.

  • Feature transformation information for modifying the input data before training.

Amazon Personalize provides a set of predefined recipes. You specify a recipe when you create a solution with the CreateSolution  API. CreateSolution trains a model by using the algorithm in the specified recipe and a training dataset. The solution, when deployed as a campaign, can provide recommendations using the GetRecommendations  API.

DescribeRecommenderCommand

Describes the given recommender, including its status.

A recommender can be in one of the following states:

  • CREATE PENDING CREATE IN_PROGRESS ACTIVE -or- CREATE FAILED

  • STOP PENDING STOP IN_PROGRESS INACTIVE START PENDING START IN_PROGRESS ACTIVE

  • DELETE PENDING DELETE IN_PROGRESS

When the status is CREATE FAILED, the response includes the failureReason key, which describes why.

The modelMetrics key is null when the recommender is being created or deleted.

For more information on recommenders, see CreateRecommender .

DescribeSchemaCommand

Describes a schema. For more information on schemas, see CreateSchema .

DescribeSolutionCommand

Describes a solution. For more information on solutions, see CreateSolution .

DescribeSolutionVersionCommand

Describes a specific version of a solution. For more information on solutions, see CreateSolution 

GetSolutionMetricsCommand

Gets the metrics for the specified solution version.

ListBatchInferenceJobsCommand

Gets a list of the batch inference jobs that have been performed off of a solution version.

ListBatchSegmentJobsCommand

Gets a list of the batch segment jobs that have been performed off of a solution version that you specify.

ListCampaignsCommand

Returns a list of campaigns that use the given solution. When a solution is not specified, all the campaigns associated with the account are listed. The response provides the properties for each campaign, including the Amazon Resource Name (ARN). For more information on campaigns, see CreateCampaign .

ListDataDeletionJobsCommand

Returns a list of data deletion jobs for a dataset group ordered by creation time, with the most recent first. When a dataset group is not specified, all the data deletion jobs associated with the account are listed. The response provides the properties for each job, including the Amazon Resource Name (ARN). For more information on data deletion jobs, see Deleting users .

ListDatasetExportJobsCommand

Returns a list of dataset export jobs that use the given dataset. When a dataset is not specified, all the dataset export jobs associated with the account are listed. The response provides the properties for each dataset export job, including the Amazon Resource Name (ARN). For more information on dataset export jobs, see CreateDatasetExportJob . For more information on datasets, see CreateDataset .

ListDatasetGroupsCommand

Returns a list of dataset groups. The response provides the properties for each dataset group, including the Amazon Resource Name (ARN). For more information on dataset groups, see CreateDatasetGroup .

ListDatasetImportJobsCommand

Returns a list of dataset import jobs that use the given dataset. When a dataset is not specified, all the dataset import jobs associated with the account are listed. The response provides the properties for each dataset import job, including the Amazon Resource Name (ARN). For more information on dataset import jobs, see CreateDatasetImportJob . For more information on datasets, see CreateDataset .

ListDatasetsCommand

Returns the list of datasets contained in the given dataset group. The response provides the properties for each dataset, including the Amazon Resource Name (ARN). For more information on datasets, see CreateDataset .

ListEventTrackersCommand

Returns the list of event trackers associated with the account. The response provides the properties for each event tracker, including the Amazon Resource Name (ARN) and tracking ID. For more information on event trackers, see CreateEventTracker .

ListFiltersCommand

Lists all filters that belong to a given dataset group.

ListMetricAttributionMetricsCommand

Lists the metrics for the metric attribution.

ListMetricAttributionsCommand

Lists metric attributions.

ListRecipesCommand

Returns a list of available recipes. The response provides the properties for each recipe, including the recipe's Amazon Resource Name (ARN).

ListRecommendersCommand

Returns a list of recommenders in a given Domain dataset group. When a Domain dataset group is not specified, all the recommenders associated with the account are listed. The response provides the properties for each recommender, including the Amazon Resource Name (ARN). For more information on recommenders, see CreateRecommender .

ListSchemasCommand

Returns the list of schemas associated with the account. The response provides the properties for each schema, including the Amazon Resource Name (ARN). For more information on schemas, see CreateSchema .

ListSolutionVersionsCommand

Returns a list of solution versions for the given solution. When a solution is not specified, all the solution versions associated with the account are listed. The response provides the properties for each solution version, including the Amazon Resource Name (ARN).

ListSolutionsCommand

Returns a list of solutions in a given dataset group. When a dataset group is not specified, all the solutions associated with the account are listed. The response provides the properties for each solution, including the Amazon Resource Name (ARN). For more information on solutions, see CreateSolution .

ListTagsForResourceCommand

Get a list of tags  attached to a resource.

StartRecommenderCommand

Starts a recommender that is INACTIVE. Starting a recommender does not create any new models, but resumes billing and automatic retraining for the recommender.

StopRecommenderCommand

Stops a recommender that is ACTIVE. Stopping a recommender halts billing and automatic retraining for the recommender.

StopSolutionVersionCreationCommand

Stops creating a solution version that is in a state of CREATE_PENDING or CREATE IN_PROGRESS.

Depending on the current state of the solution version, the solution version state changes as follows:

  • CREATE_PENDING CREATE_STOPPED

    or

  • CREATE_IN_PROGRESS CREATE_STOPPING CREATE_STOPPED

You are billed for all of the training completed up until you stop the solution version creation. You cannot resume creating a solution version once it has been stopped.

TagResourceCommand

Add a list of tags to a resource.

UntagResourceCommand

Removes the specified tags that are attached to a resource. For more information, see Removing tags from Amazon Personalize resources .

UpdateCampaignCommand

Updates a campaign to deploy a retrained solution version with an existing campaign, change your campaign's minProvisionedTPS, or modify your campaign's configuration. For example, you can set enableMetadataWithRecommendations to true for an existing campaign.

To update a campaign to start automatically using the latest solution version, specify the following:

  • For the SolutionVersionArn parameter, specify the Amazon Resource Name (ARN) of your solution in SolutionArn/$LATEST format.

  • In the campaignConfig, set syncWithLatestSolutionVersion to true.

To update a campaign, the campaign status must be ACTIVE or CREATE FAILED. Check the campaign status using the DescribeCampaign  operation.

You can still get recommendations from a campaign while an update is in progress. The campaign will use the previous solution version and campaign configuration to generate recommendations until the latest campaign update status is Active.

For more information about updating a campaign, including code samples, see Updating a campaign . For more information about campaigns, see Creating a campaign .

UpdateDatasetCommand

Update a dataset to replace its schema with a new or existing one. For more information, see Replacing a dataset's schema .

UpdateMetricAttributionCommand

Updates a metric attribution.

UpdateRecommenderCommand

Updates the recommender to modify the recommender configuration. If you update the recommender to modify the columns used in training, Amazon Personalize automatically starts a full retraining of the models backing your recommender. While the update completes, you can still get recommendations from the recommender. The recommender uses the previous configuration until the update completes. To track the status of this update, use the latestRecommenderUpdate returned in the DescribeRecommender  operation.

UpdateSolutionCommand

Updates an Amazon Personalize solution to use a different automatic training configuration. When you update a solution, you can change whether the solution uses automatic training, and you can change the training frequency. For more information about updating a solution, see Updating a solution .

A solution update can be in one of the following states:

CREATE PENDING CREATE IN_PROGRESS ACTIVE -or- CREATE FAILED

To get the status of a solution update, call the DescribeSolution  API operation and find the status in the latestSolutionUpdate.

PersonalizeClient Configuration

Parameter
Type
Description
defaultsMode
Optional
DefaultsMode | Provider<DefaultsMode>
The @smithy/smithy-client#DefaultsMode that will be used to determine how certain default configuration options are resolved in the SDK.
disableHostPrefix
Optional
boolean
Disable dynamically changing the endpoint of the client based on the hostPrefix trait of an operation.
extensions
Optional
RuntimeExtension[]
Optional extensions
logger
Optional
Logger
Optional logger for logging debug/info/warn/error.
maxAttempts
Optional
number | Provider<number>
Value for how many times a request will be made at most in case of retry.
profile
Optional
string
Setting a client profile is similar to setting a value for the AWS_PROFILE environment variable. Setting a profile on a client in code only affects the single client instance, unlike AWS_PROFILE.When set, and only for environments where an AWS configuration file exists, fields configurable by this file will be retrieved from the specified profile within that file. Conflicting code configuration and environment variables will still have higher priority.For client credential resolution that involves checking the AWS configuration file, the client's profile (this value) will be used unless a different profile is set in the credential provider options.
region
Optional
string | Provider<string>
The AWS region to which this client will send requests
requestHandler
Optional
__HttpHandlerUserInput
The HTTP handler to use or its constructor options. Fetch in browser and Https in Nodejs.
retryMode
Optional
string | Provider<string>
Specifies which retry algorithm to use.
useDualstackEndpoint
Optional
boolean | Provider<boolean>
Enables IPv6/IPv4 dualstack endpoint.
useFipsEndpoint
Optional
boolean | Provider<boolean>
Enables FIPS compatible endpoints.
Additional config fields are described in the full configuration type: PersonalizeClientConfig