export interface CustomModelTrainingParameters {
    /** The path to the Amazon S3 location where the Python module implementing your model is located. This must point to a valid existing Amazon S3 location that contains, at a minimum, a training script, a  */
    sourceS3DirectoryPath: string;
    /** The name of the entry point in your module of a script that performs model training and takes hyperparameters as command-line arguments, including fixed hyperparameters. The default is training.py. */
    trainingEntryPointScript?: string;
    /** The name of the entry point in your module of a script that should be run after the best model from the hyperparameter search has been identified, to compute the model artifacts necessary for model de */
    transformEntryPointScript?: string;
}
export interface CustomModelTransformParameters {
    /** The path to the Amazon S3 location where the Python module implementing your model is located. This must point to a valid existing Amazon S3 location that contains, at a minimum, a training script, a  */
    sourceS3DirectoryPath: string;
    /** The name of the entry point in your module of a script that should be run after the best model from the hyperparameter search has been identified, to compute the model artifacts necessary for model de */
    transformEntryPointScript?: string;
}
export interface CancelGremlinQueryInput {
    /** The unique identifier that identifies the query to be canceled. */
    queryId: string;
}
export interface CancelLoaderJobInput {
    /** The ID of the load job to be deleted. */
    loadId: string;
}
export interface CancelMLDataProcessingJobInput {
    /** The unique identifier of the data-processing job. */
    id: string;
    /** If set to TRUE, this flag specifies that all Neptune ML S3 artifacts should be deleted when the job is stopped. The default is FALSE. */
    clean?: boolean;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
}
export interface CancelMLModelTrainingJobInput {
    /** The unique identifier of the model-training job to be canceled. */
    id: string;
    /** If set to TRUE, this flag specifies that all Amazon S3 artifacts should be deleted when the job is stopped. The default is FALSE. */
    clean?: boolean;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
}
export interface CancelMLModelTransformJobInput {
    /** The unique ID of the model transform job to be canceled. */
    id: string;
    /** If this flag is set to TRUE, all Neptune ML S3 artifacts should be deleted when the job is stopped. The default is FALSE. */
    clean?: boolean;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
}
export interface CancelOpenCypherQueryInput {
    /** The unique ID of the openCypher query to cancel. */
    queryId: string;
    /** If set to TRUE, causes the cancelation of the openCypher query to happen silently. */
    silent?: boolean;
}
export interface CreateMLEndpointInput {
    /** A unique identifier for the new inference endpoint. The default is an autogenerated timestamped name. */
    id?: string;
    /** The minimum number of Amazon EC2 instances to deploy to an endpoint for prediction. The default is 1 */
    instanceCount?: number;
    /** The type of Neptune ML instance to use for online servicing. The default is ml.m5.xlarge. Choosing the ML instance for an inference endpoint depends on the task type, the graph size, and your budget. */
    instanceType?: string;
    /** The job Id of the completed model-training job that has created the model that the inference endpoint will point to. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId. */
    mlModelTrainingJobId?: string;
    /** The job Id of the completed model-transform job. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId. */
    mlModelTransformJobId?: string;
    /** Model type for training. By default the Neptune ML model is automatically based on the modelType used in data processing, but you can specify a different model type here. The default is rgcn for heter */
    modelName?: string;
    /** The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will be thrown. */
    neptuneIamRoleArn?: string;
    /** If set to true, update indicates that this is an update request. The default is false. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId. */
    update?: boolean;
    /** The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None. */
    volumeEncryptionKMSKey?: string;
}
export interface DeleteMLEndpointInput {
    /** The unique identifier of the inference endpoint. */
    id: string;
    /** If this flag is set to TRUE, all Neptune ML S3 artifacts should be deleted when the job is stopped. The default is FALSE. */
    clean?: boolean;
    /** The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will be thrown. */
    neptuneIamRoleArn?: string;
}
export interface ExecuteFastResetInput {
    /** The fast reset action. One of the following values: initiateDatabaseReset – This action generates a unique token needed to actually perform the fast reset. performDatabaseReset – This action uses the  */
    action: 'initiateDatabaseReset' | 'performDatabaseReset';
    /** The fast-reset token to initiate the reset. */
    token?: string;
}
export interface ExecuteGremlinExplainQueryInput {
    /** The Gremlin explain query string. */
    gremlinQuery: string;
}
export interface ExecuteGremlinProfileQueryInput {
    /** The Gremlin query string to profile. */
    gremlinQuery: string;
    /** If non-zero, causes the results string to be truncated at that number of characters. If set to zero, the string contains all the results. */
    chop?: number;
    /** If this flag is set to TRUE, the results include a detailed report of all index operations that took place during query execution and serialization. */
    indexOps?: boolean;
    /** If this flag is set to TRUE, the query results are gathered and displayed as part of the profile report. If FALSE, only the result count is displayed. */
    results?: boolean;
    /** If non-null, the gathered results are returned in a serialized response message in the format specified by this parameter. See Gremlin profile API in Neptune for more information. */
    serializer?: string;
}
export interface ExecuteGremlinQueryInput {
    /** Using this API, you can run Gremlin queries in string format much as you can using the HTTP endpoint. The interface is compatible with whatever Gremlin version your DB cluster is using (see the Tinker */
    gremlinQuery: string;
    /** If non-null, the query results are returned in a serialized response message in the format specified by this parameter. See the GraphSON section in the TinkerPop documentation for a list of the format */
    serializer?: string;
}
export interface ExecuteOpenCypherExplainQueryInput {
    /** The openCypher explain mode. Can be one of: static, dynamic, or details. */
    explainMode: 'static' | 'dynamic' | 'details';
    /** The openCypher query string. */
    openCypherQuery: string;
    /** The openCypher query parameters. */
    parameters?: string;
}
export interface ExecuteOpenCypherQueryInput {
    /** The openCypher query string to be executed. */
    openCypherQuery: string;
    /** The openCypher query parameters for query execution. See Examples of openCypher parameterized queries for more information. */
    parameters?: string;
}
export interface GetGremlinQueryStatusInput {
    /** The unique identifier that identifies the Gremlin query. */
    queryId: string;
}
export interface GetLoaderJobStatusInput {
    /** The load ID of the load job to get the status of. */
    loadId: string;
    /** Flag indicating whether or not to include details beyond the overall status (TRUE or FALSE; the default is FALSE). */
    details?: boolean;
    /** Flag indicating whether or not to include a list of errors encountered (TRUE or FALSE; the default is FALSE). The list of errors is paged. The page and errorsPerPage parameters allow you to page throu */
    errors?: boolean;
    /** The number of errors returned in each page (a positive integer; the default is 10). Only valid when the errors parameter set to TRUE. */
    errorsPerPage?: number;
    /** The error page number (a positive integer; the default is 1). Only valid when the errors parameter is set to TRUE. */
    page?: number;
}
export interface GetMLDataProcessingJobInput {
    /** The unique identifier of the data-processing job to be retrieved. */
    id: string;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
}
export interface GetMLEndpointInput {
    /** The unique identifier of the inference endpoint. */
    id: string;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
}
export interface GetMLModelTrainingJobInput {
    /** The unique identifier of the model-training job to retrieve. */
    id: string;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
}
export interface GetMLModelTransformJobInput {
    /** The unique identifier of the model-transform job to be reetrieved. */
    id: string;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
}
export interface GetOpenCypherQueryStatusInput {
    /** The unique ID of the openCypher query for which to retrieve the query status. */
    queryId: string;
}
export interface GetPropertygraphStreamInput {
    /** The commit number of the starting record to read from the change-log stream. This parameter is required when iteratorType isAT_SEQUENCE_NUMBER or AFTER_SEQUENCE_NUMBER, and ignored when iteratorType i */
    commitNum?: number;
    /** If set to TRUE, Neptune compresses the response using gzip encoding. */
    encoding?: 'gzip';
    /** Can be one of: AT_SEQUENCE_NUMBER – Indicates that reading should start from the event sequence number specified jointly by the commitNum and opNum parameters. AFTER_SEQUENCE_NUMBER – Indicates that r */
    iteratorType?: 'AT_SEQUENCE_NUMBER' | 'AFTER_SEQUENCE_NUMBER' | 'TRIM_HORIZON' | 'LATEST';
    /** Specifies the maximum number of records to return. There is also a size limit of 10 MB on the response that can't be modified and that takes precedence over the number of records specified in the limi */
    limit?: number;
    /** The operation sequence number within the specified commit to start reading from in the change-log stream data. The default is 1. */
    opNum?: number;
}
export interface GetPropertygraphSummaryInput {
    /** Mode can take one of two values: BASIC (the default), and DETAILED. */
    mode?: 'basic' | 'detailed';
}
export interface GetRDFGraphSummaryInput {
    /** Mode can take one of two values: BASIC (the default), and DETAILED. */
    mode?: 'basic' | 'detailed';
}
export interface GetSparqlStreamInput {
    /** The commit number of the starting record to read from the change-log stream. This parameter is required when iteratorType isAT_SEQUENCE_NUMBER or AFTER_SEQUENCE_NUMBER, and ignored when iteratorType i */
    commitNum?: number;
    /** If set to TRUE, Neptune compresses the response using gzip encoding. */
    encoding?: 'gzip';
    /** Can be one of: AT_SEQUENCE_NUMBER – Indicates that reading should start from the event sequence number specified jointly by the commitNum and opNum parameters. AFTER_SEQUENCE_NUMBER – Indicates that r */
    iteratorType?: 'AT_SEQUENCE_NUMBER' | 'AFTER_SEQUENCE_NUMBER' | 'TRIM_HORIZON' | 'LATEST';
    /** Specifies the maximum number of records to return. There is also a size limit of 10 MB on the response that can't be modified and that takes precedence over the number of records specified in the limi */
    limit?: number;
    /** The operation sequence number within the specified commit to start reading from in the change-log stream data. The default is 1. */
    opNum?: number;
}
export interface ListGremlinQueriesInput {
    /** If set to TRUE, the list returned includes waiting queries. The default is FALSE; */
    includeWaiting?: boolean;
}
export interface ListLoaderJobsInput {
    /** An optional parameter that can be used to exclude the load IDs of queued load requests when requesting a list of load IDs by setting the parameter to FALSE. The default value is TRUE. */
    includeQueuedLoads?: boolean;
    /** The number of load IDs to list. Must be a positive integer greater than zero and not more than 100 (which is the default). */
    limit?: number;
}
export interface ListMLDataProcessingJobsInput {
    /** The maximum number of items to return (from 1 to 1024; the default is 10). */
    maxItems?: number;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
}
export interface ListMLEndpointsInput {
    /** The maximum number of items to return (from 1 to 1024; the default is 10. */
    maxItems?: number;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
}
export interface ListMLModelTrainingJobsInput {
    /** The maximum number of items to return (from 1 to 1024; the default is 10). */
    maxItems?: number;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
}
export interface ListMLModelTransformJobsInput {
    /** The maximum number of items to return (from 1 to 1024; the default is 10). */
    maxItems?: number;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
}
export interface ListOpenCypherQueriesInput {
    /** When set to TRUE and other parameters are not present, causes status information to be returned for waiting queries as well as for running queries. */
    includeWaiting?: boolean;
}
export interface ManagePropertygraphStatisticsInput {
    /** The statistics generation mode. One of: DISABLE_AUTOCOMPUTE, ENABLE_AUTOCOMPUTE, or REFRESH, the last of which manually triggers DFE statistics generation. */
    mode?: 'disableAutoCompute' | 'enableAutoCompute' | 'refresh';
}
export interface ManageSparqlStatisticsInput {
    /** The statistics generation mode. One of: DISABLE_AUTOCOMPUTE, ENABLE_AUTOCOMPUTE, or REFRESH, the last of which manually triggers DFE statistics generation. */
    mode?: 'disableAutoCompute' | 'enableAutoCompute' | 'refresh';
}
export interface StartLoaderJobInput {
    /** The format of the data. For more information about data formats for the Neptune Loader command, see Load Data Formats. Allowed values csv for the Gremlin CSV data format. opencypher for the openCypher */
    format: 'csv' | 'opencypher' | 'ntriples' | 'nquads' | 'rdfxml' | 'turtle';
    /** The Amazon Resource Name (ARN) for an IAM role to be assumed by the Neptune DB instance for access to the S3 bucket. The IAM role ARN provided here should be attached to the DB cluster (see Adding the */
    iamRoleArn: string;
    /** The Amazon region of the S3 bucket. This must match the Amazon Region of the DB cluster. */
    s3BucketRegion: 'us-east-1' | 'us-east-2' | 'us-west-1' | 'us-west-2' | 'ca-central-1' | 'sa-east-1' | 'eu-north-1' | 'eu-west-1' | 'eu-west-2' | 'eu-west-3' | 'eu-central-1' | 'me-south-1' | 'af-south-1' | 'ap-east-1' | 'ap-northeast-1' | 'ap-northeast-2' | 'ap-southeast-1' | 'ap-southeast-2' | 'ap-south-1' | 'cn-north-1' | 'cn-northwest-1' | 'us-gov-west-1' | 'us-gov-east-1' | 'ca-west-1' | 'eu-south-2' | 'il-central-1' | 'me-central-1' | 'ap-northeast-3' | 'ap-southeast-3' | 'ap-southeast-4' | 'ap-southeast-5' | 'ap-southeast-7' | 'mx-central-1' | 'ap-east-2' | 'ap-south-2' | 'eu-central-2';
    /** The source parameter accepts an S3 URI that identifies a single file, multiple files, a folder, or multiple folders. Neptune loads every data file in any folder that is specified. The URI can be in an */
    source: string;
    /** This is an optional parameter that can make a queued load request contingent on the successful completion of one or more previous jobs in the queue. Neptune can queue up as many as 64 load requests at */
    dependencies?: string[];
    /** edgeOnlyLoad – A flag that controls file processing order during bulk loading. Allowed values: "TRUE", "FALSE". Default value: "FALSE". When this parameter is set to "FALSE", the loader automatically  */
    edgeOnlyLoad?: boolean;
    /** failOnError – A flag to toggle a complete stop on an error. Allowed values: "TRUE", "FALSE". Default value: "TRUE". When this parameter is set to "FALSE", the loader tries to load all the data in the  */
    failOnError?: boolean;
    /** The load job mode. Allowed values: RESUME, NEW, AUTO. Default value: AUTO. RESUME – In RESUME mode, the loader looks for a previous load from this source, and if it finds one, resumes that load job. I */
    mode?: 'RESUME' | 'NEW' | 'AUTO';
    /** The optional parallelism parameter can be set to reduce the number of threads used by the bulk load process. Allowed values: LOW – The number of threads used is the number of available vCPUs divided b */
    parallelism?: 'LOW' | 'MEDIUM' | 'HIGH' | 'OVERSUBSCRIBE';
    /** parserConfiguration – An optional object with additional parser configuration values. Each of the child parameters is also optional: namedGraphUri – The default graph for all RDF formats when no graph */
    parserConfiguration?: Record<string, string>;
    /** This is an optional flag parameter that indicates whether the load request can be queued up or not. You don't have to wait for one load job to complete before issuing the next one, because Neptune can */
    queueRequest?: boolean;
    /** updateSingleCardinalityProperties is an optional parameter that controls how the bulk loader treats a new value for single-cardinality vertex or edge properties. This is not supported for loading open */
    updateSingleCardinalityProperties?: boolean;
    /** This parameter is required only when loading openCypher data that contains relationship IDs. It must be included and set to True when openCypher relationship IDs are explicitly provided in the load da */
    userProvidedEdgeIds?: boolean;
}
export interface StartMLDataProcessingJobInput {
    /** The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job. */
    inputDataS3Location: string;
    /** The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job. */
    processedDataS3Location: string;
    /** A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration */
    configFileName?: string;
    /** A unique identifier for the new job. The default is an autogenerated UUID. */
    id?: string;
    /** One of the two model types that Neptune ML currently supports: heterogeneous graph models (heterogeneous), and knowledge graph (kge). The default is none. If not specified, Neptune ML chooses the mode */
    modelType?: string;
    /** The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
    /** The job ID of a completed data processing job run on an earlier version of the data. */
    previousDataProcessingJobId?: string;
    /** The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the s */
    processingInstanceType?: string;
    /** The disk volume size of the processing instance. Both input data and processed data are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specifi */
    processingInstanceVolumeSizeInGB?: number;
    /** Timeout in seconds for the data processing job. The default is 86,400 (1 day). */
    processingTimeOutInSeconds?: number;
    /** The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none. */
    s3OutputEncryptionKMSKey?: string;
    /** The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur. */
    sagemakerIamRoleArn?: string;
    /** The VPC security group IDs. The default is None. */
    securityGroupIds?: string[];
    /** The IDs of the subnets in the Neptune VPC. The default is None. */
    subnets?: string[];
    /** The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None. */
    volumeEncryptionKMSKey?: string;
}
export interface StartMLModelTrainingJobInput {
    /** The job ID of the completed data-processing job that has created the data that the training will work with. */
    dataProcessingJobId: string;
    /** The location in Amazon S3 where the model artifacts are to be stored. */
    trainModelS3Location: string;
    /** The type of ML instance used in preparing and managing training of ML models. This is a CPU instance chosen based on memory requirements for processing the training data and model. */
    baseProcessingInstanceType?: string;
    /** The configuration for custom model training. This is a JSON object. */
    customModelTrainingParameters?: CustomModelTrainingParameters;
    /** Optimizes the cost of training machine-learning models by using Amazon Elastic Compute Cloud spot instances. The default is False. */
    enableManagedSpotTraining?: boolean;
    /** A unique identifier for the new job. The default is An autogenerated UUID. */
    id?: string;
    /** Maximum total number of training jobs to start for the hyperparameter tuning job. The default is 2. Neptune ML automatically tunes the hyperparameters of the machine learning model. To obtain a model  */
    maxHPONumberOfTrainingJobs?: number;
    /** Maximum number of parallel training jobs to start for the hyperparameter tuning job. The default is 2. The number of parallel jobs you can run is limited by the available resources on your training in */
    maxHPOParallelTrainingJobs?: number;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
    /** The job ID of a completed model-training job that you want to update incrementally based on updated data. */
    previousModelTrainingJobId?: string;
    /** The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none. */
    s3OutputEncryptionKMSKey?: string;
    /** The ARN of an IAM role for SageMaker execution.This must be listed in your DB cluster parameter group or an error will occur. */
    sagemakerIamRoleArn?: string;
    /** The VPC security group IDs. The default is None. */
    securityGroupIds?: string[];
    /** The IDs of the subnets in the Neptune VPC. The default is None. */
    subnets?: string[];
    /** The type of ML instance used for model training. All Neptune ML models support CPU, GPU, and multiGPU training. The default is ml.p3.2xlarge. Choosing the right instance type for training depends on t */
    trainingInstanceType?: string;
    /** The disk volume size of the training instance. Both input data and the output model are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specifi */
    trainingInstanceVolumeSizeInGB?: number;
    /** Timeout in seconds for the training job. The default is 86,400 (1 day). */
    trainingTimeOutInSeconds?: number;
    /** The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None. */
    volumeEncryptionKMSKey?: string;
}
export interface StartMLModelTransformJobInput {
    /** The location in Amazon S3 where the model artifacts are to be stored. */
    modelTransformOutputS3Location: string;
    /** The type of ML instance used in preparing and managing training of ML models. This is an ML compute instance chosen based on memory requirements for processing the training data and model. */
    baseProcessingInstanceType?: string;
    /** The disk volume size of the training instance in gigabytes. The default is 0. Both input data and the output model are stored on disk, so the volume size must be large enough to hold both data sets. I */
    baseProcessingInstanceVolumeSizeInGB?: number;
    /** Configuration information for a model transform using a custom model. The customModelTransformParameters object contains the following fields, which must have values compatible with the saved model pa */
    customModelTransformParameters?: CustomModelTransformParameters;
    /** The job ID of a completed data-processing job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName. */
    dataProcessingJobId?: string;
    /** A unique identifier for the new job. The default is an autogenerated UUID. */
    id?: string;
    /** The job ID of a completed model-training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName. */
    mlModelTrainingJobId?: string;
    /** The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur. */
    neptuneIamRoleArn?: string;
    /** The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none. */
    s3OutputEncryptionKMSKey?: string;
    /** The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur. */
    sagemakerIamRoleArn?: string;
    /** The VPC security group IDs. The default is None. */
    securityGroupIds?: string[];
    /** The IDs of the subnets in the Neptune VPC. The default is None. */
    subnets?: string[];
    /** The name of a completed SageMaker training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName. */
    trainingJobName?: string;
    /** The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None. */
    volumeEncryptionKMSKey?: string;
}
/** neptunedata service binding for Step Functions SDK integrations. */
export declare class neptunedata {
    constructor();
    cancelGremlinQuery<T>(params: CancelGremlinQueryInput): Promise<T>;
    cancelLoaderJob<T>(params: CancelLoaderJobInput): Promise<T>;
    cancelMLDataProcessingJob<T>(params: CancelMLDataProcessingJobInput): Promise<T>;
    cancelMLModelTrainingJob<T>(params: CancelMLModelTrainingJobInput): Promise<T>;
    cancelMLModelTransformJob<T>(params: CancelMLModelTransformJobInput): Promise<T>;
    cancelOpenCypherQuery<T>(params: CancelOpenCypherQueryInput): Promise<T>;
    createMLEndpoint<T>(params: CreateMLEndpointInput): Promise<T>;
    deleteMLEndpoint<T>(params: DeleteMLEndpointInput): Promise<T>;
    deletePropertygraphStatistics<T>(): Promise<T>;
    deleteSparqlStatistics<T>(): Promise<T>;
    executeFastReset<T>(params: ExecuteFastResetInput): Promise<T>;
    executeGremlinExplainQuery<T>(params: ExecuteGremlinExplainQueryInput): Promise<T>;
    executeGremlinProfileQuery<T>(params: ExecuteGremlinProfileQueryInput): Promise<T>;
    executeGremlinQuery<T>(params: ExecuteGremlinQueryInput): Promise<T>;
    executeOpenCypherExplainQuery<T>(params: ExecuteOpenCypherExplainQueryInput): Promise<T>;
    executeOpenCypherQuery<T>(params: ExecuteOpenCypherQueryInput): Promise<T>;
    getEngineStatus<T>(): Promise<T>;
    getGremlinQueryStatus<T>(params: GetGremlinQueryStatusInput): Promise<T>;
    getLoaderJobStatus<T>(params: GetLoaderJobStatusInput): Promise<T>;
    getMLDataProcessingJob<T>(params: GetMLDataProcessingJobInput): Promise<T>;
    getMLEndpoint<T>(params: GetMLEndpointInput): Promise<T>;
    getMLModelTrainingJob<T>(params: GetMLModelTrainingJobInput): Promise<T>;
    getMLModelTransformJob<T>(params: GetMLModelTransformJobInput): Promise<T>;
    getOpenCypherQueryStatus<T>(params: GetOpenCypherQueryStatusInput): Promise<T>;
    getPropertygraphStatistics<T>(): Promise<T>;
    getPropertygraphStream<T>(params: GetPropertygraphStreamInput): Promise<T>;
    getPropertygraphSummary<T>(params: GetPropertygraphSummaryInput): Promise<T>;
    getRDFGraphSummary<T>(params: GetRDFGraphSummaryInput): Promise<T>;
    getSparqlStatistics<T>(): Promise<T>;
    getSparqlStream<T>(params: GetSparqlStreamInput): Promise<T>;
    listGremlinQueries<T>(params: ListGremlinQueriesInput): Promise<T>;
    listLoaderJobs<T>(params: ListLoaderJobsInput): Promise<T>;
    listMLDataProcessingJobs<T>(params: ListMLDataProcessingJobsInput): Promise<T>;
    listMLEndpoints<T>(params: ListMLEndpointsInput): Promise<T>;
    listMLModelTrainingJobs<T>(params: ListMLModelTrainingJobsInput): Promise<T>;
    listMLModelTransformJobs<T>(params: ListMLModelTransformJobsInput): Promise<T>;
    listOpenCypherQueries<T>(params: ListOpenCypherQueriesInput): Promise<T>;
    managePropertygraphStatistics<T>(params: ManagePropertygraphStatisticsInput): Promise<T>;
    manageSparqlStatistics<T>(params: ManageSparqlStatisticsInput): Promise<T>;
    startLoaderJob<T>(params: StartLoaderJobInput): Promise<T>;
    startMLDataProcessingJob<T>(params: StartMLDataProcessingJobInput): Promise<T>;
    startMLModelTrainingJob<T>(params: StartMLModelTrainingJobInput): Promise<T>;
    startMLModelTransformJob<T>(params: StartMLModelTransformJobInput): Promise<T>;
}
//# sourceMappingURL=neptunedata.d.ts.map