Skip to content

LookoutEquipmentClient#

Index > LookoutEquipment > LookoutEquipmentClient

Auto-generated documentation for LookoutEquipment type annotations stubs module mypy-boto3-lookoutequipment.

LookoutEquipmentClient#

Type annotations and code completion for boto3.client("lookoutequipment"). boto3 documentation

# LookoutEquipmentClient usage example

from boto3.session import Session
from mypy_boto3_lookoutequipment.client import LookoutEquipmentClient

def get_lookoutequipment_client() -> LookoutEquipmentClient:
    return Session().client("lookoutequipment")

Exceptions#

boto3 client exceptions are generated in runtime. This class provides code completion for boto3.client("lookoutequipment").exceptions structure.

# Exceptions.exceptions usage example

client = boto3.client("lookoutequipment")

try:
    do_something(client)
except (
    client.exceptions.AccessDeniedException,
    client.exceptions.ClientError,
    client.exceptions.ConflictException,
    client.exceptions.InternalServerException,
    client.exceptions.ResourceNotFoundException,
    client.exceptions.ServiceQuotaExceededException,
    client.exceptions.ThrottlingException,
    client.exceptions.ValidationException,
) as e:
    print(e)
# Exceptions.exceptions type checking example

from mypy_boto3_lookoutequipment.client import Exceptions

def handle_error(exc: Exceptions.AccessDeniedException) -> None:
    ...

Methods#

can_paginate#

Check if an operation can be paginated.

Type annotations and code completion for boto3.client("lookoutequipment").can_paginate method. boto3 documentation

# can_paginate method definition

def can_paginate(
    self,
    operation_name: str,
) -> bool:
    ...

close#

Closes underlying endpoint connections.

Type annotations and code completion for boto3.client("lookoutequipment").close method. boto3 documentation

# close method definition

def close(
    self,
) -> None:
    ...

create_dataset#

Creates a container for a collection of data being ingested for analysis.

Type annotations and code completion for boto3.client("lookoutequipment").create_dataset method. boto3 documentation

# create_dataset method definition

def create_dataset(
    self,
    *,
    DatasetName: str,
    ClientToken: str,
    DatasetSchema: DatasetSchemaTypeDef = ...,  # (1)
    ServerSideKmsKeyId: str = ...,
    Tags: Sequence[TagTypeDef] = ...,  # (2)
) -> CreateDatasetResponseTypeDef:  # (3)
    ...
  1. See DatasetSchemaTypeDef
  2. See TagTypeDef
  3. See CreateDatasetResponseTypeDef 
# create_dataset method usage example with argument unpacking

kwargs: CreateDatasetRequestRequestTypeDef = {  # (1)
    "DatasetName": ...,
    "ClientToken": ...,
}

parent.create_dataset(**kwargs)
  1. See CreateDatasetRequestRequestTypeDef

create_inference_scheduler#

Creates a scheduled inference.

Type annotations and code completion for boto3.client("lookoutequipment").create_inference_scheduler method. boto3 documentation

# create_inference_scheduler method definition

def create_inference_scheduler(
    self,
    *,
    ModelName: str,
    InferenceSchedulerName: str,
    DataUploadFrequency: DataUploadFrequencyType,  # (1)
    DataInputConfiguration: InferenceInputConfigurationTypeDef,  # (2)
    DataOutputConfiguration: InferenceOutputConfigurationTypeDef,  # (3)
    RoleArn: str,
    ClientToken: str,
    DataDelayOffsetInMinutes: int = ...,
    ServerSideKmsKeyId: str = ...,
    Tags: Sequence[TagTypeDef] = ...,  # (4)
) -> CreateInferenceSchedulerResponseTypeDef:  # (5)
    ...
  1. See DataUploadFrequencyType
  2. See InferenceInputConfigurationTypeDef
  3. See InferenceOutputConfigurationTypeDef
  4. See TagTypeDef
  5. See CreateInferenceSchedulerResponseTypeDef 
# create_inference_scheduler method usage example with argument unpacking

kwargs: CreateInferenceSchedulerRequestRequestTypeDef = {  # (1)
    "ModelName": ...,
    "InferenceSchedulerName": ...,
    "DataUploadFrequency": ...,
    "DataInputConfiguration": ...,
    "DataOutputConfiguration": ...,
    "RoleArn": ...,
    "ClientToken": ...,
}

parent.create_inference_scheduler(**kwargs)
  1. See CreateInferenceSchedulerRequestRequestTypeDef

create_label#

Creates a label for an event.

Type annotations and code completion for boto3.client("lookoutequipment").create_label method. boto3 documentation

# create_label method definition

def create_label(
    self,
    *,
    LabelGroupName: str,
    StartTime: Union[datetime, str],
    EndTime: Union[datetime, str],
    Rating: LabelRatingType,  # (1)
    ClientToken: str,
    FaultCode: str = ...,
    Notes: str = ...,
    Equipment: str = ...,
) -> CreateLabelResponseTypeDef:  # (2)
    ...
  1. See LabelRatingType
  2. See CreateLabelResponseTypeDef 
# create_label method usage example with argument unpacking

kwargs: CreateLabelRequestRequestTypeDef = {  # (1)
    "LabelGroupName": ...,
    "StartTime": ...,
    "EndTime": ...,
    "Rating": ...,
    "ClientToken": ...,
}

parent.create_label(**kwargs)
  1. See CreateLabelRequestRequestTypeDef

create_label_group#

Creates a group of labels.

Type annotations and code completion for boto3.client("lookoutequipment").create_label_group method. boto3 documentation

# create_label_group method definition

def create_label_group(
    self,
    *,
    LabelGroupName: str,
    ClientToken: str,
    FaultCodes: Sequence[str] = ...,
    Tags: Sequence[TagTypeDef] = ...,  # (1)
) -> CreateLabelGroupResponseTypeDef:  # (2)
    ...
  1. See TagTypeDef
  2. See CreateLabelGroupResponseTypeDef 
# create_label_group method usage example with argument unpacking

kwargs: CreateLabelGroupRequestRequestTypeDef = {  # (1)
    "LabelGroupName": ...,
    "ClientToken": ...,
}

parent.create_label_group(**kwargs)
  1. See CreateLabelGroupRequestRequestTypeDef

create_model#

Creates a machine learning model for data inference.

Type annotations and code completion for boto3.client("lookoutequipment").create_model method. boto3 documentation

# create_model method definition

def create_model(
    self,
    *,
    ModelName: str,
    DatasetName: str,
    ClientToken: str,
    DatasetSchema: DatasetSchemaTypeDef = ...,  # (1)
    LabelsInputConfiguration: LabelsInputConfigurationTypeDef = ...,  # (2)
    TrainingDataStartTime: Union[datetime, str] = ...,
    TrainingDataEndTime: Union[datetime, str] = ...,
    EvaluationDataStartTime: Union[datetime, str] = ...,
    EvaluationDataEndTime: Union[datetime, str] = ...,
    RoleArn: str = ...,
    DataPreProcessingConfiguration: DataPreProcessingConfigurationTypeDef = ...,  # (3)
    ServerSideKmsKeyId: str = ...,
    Tags: Sequence[TagTypeDef] = ...,  # (4)
    OffCondition: str = ...,
) -> CreateModelResponseTypeDef:  # (5)
    ...
  1. See DatasetSchemaTypeDef
  2. See LabelsInputConfigurationTypeDef
  3. See DataPreProcessingConfigurationTypeDef
  4. See TagTypeDef
  5. See CreateModelResponseTypeDef 
# create_model method usage example with argument unpacking

kwargs: CreateModelRequestRequestTypeDef = {  # (1)
    "ModelName": ...,
    "DatasetName": ...,
    "ClientToken": ...,
}

parent.create_model(**kwargs)
  1. See CreateModelRequestRequestTypeDef

create_retraining_scheduler#

Creates a retraining scheduler on the specified model.

Type annotations and code completion for boto3.client("lookoutequipment").create_retraining_scheduler method. boto3 documentation

# create_retraining_scheduler method definition

def create_retraining_scheduler(
    self,
    *,
    ModelName: str,
    RetrainingFrequency: str,
    LookbackWindow: str,
    ClientToken: str,
    RetrainingStartDate: Union[datetime, str] = ...,
    PromoteMode: ModelPromoteModeType = ...,  # (1)
) -> CreateRetrainingSchedulerResponseTypeDef:  # (2)
    ...
  1. See ModelPromoteModeType
  2. See CreateRetrainingSchedulerResponseTypeDef 
# create_retraining_scheduler method usage example with argument unpacking

kwargs: CreateRetrainingSchedulerRequestRequestTypeDef = {  # (1)
    "ModelName": ...,
    "RetrainingFrequency": ...,
    "LookbackWindow": ...,
    "ClientToken": ...,
}

parent.create_retraining_scheduler(**kwargs)
  1. See CreateRetrainingSchedulerRequestRequestTypeDef

delete_dataset#

Deletes a dataset and associated artifacts.

Type annotations and code completion for boto3.client("lookoutequipment").delete_dataset method. boto3 documentation

# delete_dataset method definition

def delete_dataset(
    self,
    *,
    DatasetName: str,
) -> EmptyResponseMetadataTypeDef:  # (1)
    ...
  1. See EmptyResponseMetadataTypeDef 
# delete_dataset method usage example with argument unpacking

kwargs: DeleteDatasetRequestRequestTypeDef = {  # (1)
    "DatasetName": ...,
}

parent.delete_dataset(**kwargs)
  1. See DeleteDatasetRequestRequestTypeDef

delete_inference_scheduler#

Deletes an inference scheduler that has been set up.

Type annotations and code completion for boto3.client("lookoutequipment").delete_inference_scheduler method. boto3 documentation

# delete_inference_scheduler method definition

def delete_inference_scheduler(
    self,
    *,
    InferenceSchedulerName: str,
) -> EmptyResponseMetadataTypeDef:  # (1)
    ...
  1. See EmptyResponseMetadataTypeDef 
# delete_inference_scheduler method usage example with argument unpacking

kwargs: DeleteInferenceSchedulerRequestRequestTypeDef = {  # (1)
    "InferenceSchedulerName": ...,
}

parent.delete_inference_scheduler(**kwargs)
  1. See DeleteInferenceSchedulerRequestRequestTypeDef

delete_label#

Deletes a label.

Type annotations and code completion for boto3.client("lookoutequipment").delete_label method. boto3 documentation

# delete_label method definition

def delete_label(
    self,
    *,
    LabelGroupName: str,
    LabelId: str,
) -> EmptyResponseMetadataTypeDef:  # (1)
    ...
  1. See EmptyResponseMetadataTypeDef 
# delete_label method usage example with argument unpacking

kwargs: DeleteLabelRequestRequestTypeDef = {  # (1)
    "LabelGroupName": ...,
    "LabelId": ...,
}

parent.delete_label(**kwargs)
  1. See DeleteLabelRequestRequestTypeDef

delete_label_group#

Deletes a group of labels.

Type annotations and code completion for boto3.client("lookoutequipment").delete_label_group method. boto3 documentation

# delete_label_group method definition

def delete_label_group(
    self,
    *,
    LabelGroupName: str,
) -> EmptyResponseMetadataTypeDef:  # (1)
    ...
  1. See EmptyResponseMetadataTypeDef 
# delete_label_group method usage example with argument unpacking

kwargs: DeleteLabelGroupRequestRequestTypeDef = {  # (1)
    "LabelGroupName": ...,
}

parent.delete_label_group(**kwargs)
  1. See DeleteLabelGroupRequestRequestTypeDef

delete_model#

Deletes a machine learning model currently available for Amazon Lookout for Equipment.

Type annotations and code completion for boto3.client("lookoutequipment").delete_model method. boto3 documentation

# delete_model method definition

def delete_model(
    self,
    *,
    ModelName: str,
) -> EmptyResponseMetadataTypeDef:  # (1)
    ...
  1. See EmptyResponseMetadataTypeDef 
# delete_model method usage example with argument unpacking

kwargs: DeleteModelRequestRequestTypeDef = {  # (1)
    "ModelName": ...,
}

parent.delete_model(**kwargs)
  1. See DeleteModelRequestRequestTypeDef

delete_resource_policy#

Deletes the resource policy attached to the resource.

Type annotations and code completion for boto3.client("lookoutequipment").delete_resource_policy method. boto3 documentation

# delete_resource_policy method definition

def delete_resource_policy(
    self,
    *,
    ResourceArn: str,
) -> EmptyResponseMetadataTypeDef:  # (1)
    ...
  1. See EmptyResponseMetadataTypeDef 
# delete_resource_policy method usage example with argument unpacking

kwargs: DeleteResourcePolicyRequestRequestTypeDef = {  # (1)
    "ResourceArn": ...,
}

parent.delete_resource_policy(**kwargs)
  1. See DeleteResourcePolicyRequestRequestTypeDef

delete_retraining_scheduler#

Deletes a retraining scheduler from a model.

Type annotations and code completion for boto3.client("lookoutequipment").delete_retraining_scheduler method. boto3 documentation

# delete_retraining_scheduler method definition

def delete_retraining_scheduler(
    self,
    *,
    ModelName: str,
) -> EmptyResponseMetadataTypeDef:  # (1)
    ...
  1. See EmptyResponseMetadataTypeDef 
# delete_retraining_scheduler method usage example with argument unpacking

kwargs: DeleteRetrainingSchedulerRequestRequestTypeDef = {  # (1)
    "ModelName": ...,
}

parent.delete_retraining_scheduler(**kwargs)
  1. See DeleteRetrainingSchedulerRequestRequestTypeDef

describe_data_ingestion_job#

Provides information on a specific data ingestion job such as creation time, dataset ARN, and status.

Type annotations and code completion for boto3.client("lookoutequipment").describe_data_ingestion_job method. boto3 documentation

# describe_data_ingestion_job method definition

def describe_data_ingestion_job(
    self,
    *,
    JobId: str,
) -> DescribeDataIngestionJobResponseTypeDef:  # (1)
    ...
  1. See DescribeDataIngestionJobResponseTypeDef 
# describe_data_ingestion_job method usage example with argument unpacking

kwargs: DescribeDataIngestionJobRequestRequestTypeDef = {  # (1)
    "JobId": ...,
}

parent.describe_data_ingestion_job(**kwargs)
  1. See DescribeDataIngestionJobRequestRequestTypeDef

describe_dataset#

Provides a JSON description of the data in each time series dataset, including names, column names, and data types.

Type annotations and code completion for boto3.client("lookoutequipment").describe_dataset method. boto3 documentation

# describe_dataset method definition

def describe_dataset(
    self,
    *,
    DatasetName: str,
) -> DescribeDatasetResponseTypeDef:  # (1)
    ...
  1. See DescribeDatasetResponseTypeDef 
# describe_dataset method usage example with argument unpacking

kwargs: DescribeDatasetRequestRequestTypeDef = {  # (1)
    "DatasetName": ...,
}

parent.describe_dataset(**kwargs)
  1. See DescribeDatasetRequestRequestTypeDef

describe_inference_scheduler#

Specifies information about the inference scheduler being used, including name, model, status, and associated metadata See also: AWS API Documentation.

Type annotations and code completion for boto3.client("lookoutequipment").describe_inference_scheduler method. boto3 documentation

# describe_inference_scheduler method definition

def describe_inference_scheduler(
    self,
    *,
    InferenceSchedulerName: str,
) -> DescribeInferenceSchedulerResponseTypeDef:  # (1)
    ...
  1. See DescribeInferenceSchedulerResponseTypeDef 
# describe_inference_scheduler method usage example with argument unpacking

kwargs: DescribeInferenceSchedulerRequestRequestTypeDef = {  # (1)
    "InferenceSchedulerName": ...,
}

parent.describe_inference_scheduler(**kwargs)
  1. See DescribeInferenceSchedulerRequestRequestTypeDef

describe_label#

Returns the name of the label.

Type annotations and code completion for boto3.client("lookoutequipment").describe_label method. boto3 documentation

# describe_label method definition

def describe_label(
    self,
    *,
    LabelGroupName: str,
    LabelId: str,
) -> DescribeLabelResponseTypeDef:  # (1)
    ...
  1. See DescribeLabelResponseTypeDef 
# describe_label method usage example with argument unpacking

kwargs: DescribeLabelRequestRequestTypeDef = {  # (1)
    "LabelGroupName": ...,
    "LabelId": ...,
}

parent.describe_label(**kwargs)
  1. See DescribeLabelRequestRequestTypeDef

describe_label_group#

Returns information about the label group.

Type annotations and code completion for boto3.client("lookoutequipment").describe_label_group method. boto3 documentation

# describe_label_group method definition

def describe_label_group(
    self,
    *,
    LabelGroupName: str,
) -> DescribeLabelGroupResponseTypeDef:  # (1)
    ...
  1. See DescribeLabelGroupResponseTypeDef 
# describe_label_group method usage example with argument unpacking

kwargs: DescribeLabelGroupRequestRequestTypeDef = {  # (1)
    "LabelGroupName": ...,
}

parent.describe_label_group(**kwargs)
  1. See DescribeLabelGroupRequestRequestTypeDef

describe_model#

Provides a JSON containing the overall information about a specific machine learning model, including model name and ARN, dataset, training and evaluation information, status, and so on.

Type annotations and code completion for boto3.client("lookoutequipment").describe_model method. boto3 documentation

# describe_model method definition

def describe_model(
    self,
    *,
    ModelName: str,
) -> DescribeModelResponseTypeDef:  # (1)
    ...
  1. See DescribeModelResponseTypeDef 
# describe_model method usage example with argument unpacking

kwargs: DescribeModelRequestRequestTypeDef = {  # (1)
    "ModelName": ...,
}

parent.describe_model(**kwargs)
  1. See DescribeModelRequestRequestTypeDef

describe_model_version#

Retrieves information about a specific machine learning model version.

Type annotations and code completion for boto3.client("lookoutequipment").describe_model_version method. boto3 documentation

# describe_model_version method definition

def describe_model_version(
    self,
    *,
    ModelName: str,
    ModelVersion: int,
) -> DescribeModelVersionResponseTypeDef:  # (1)
    ...
  1. See DescribeModelVersionResponseTypeDef 
# describe_model_version method usage example with argument unpacking

kwargs: DescribeModelVersionRequestRequestTypeDef = {  # (1)
    "ModelName": ...,
    "ModelVersion": ...,
}

parent.describe_model_version(**kwargs)
  1. See DescribeModelVersionRequestRequestTypeDef

describe_resource_policy#

Provides the details of a resource policy attached to a resource.

Type annotations and code completion for boto3.client("lookoutequipment").describe_resource_policy method. boto3 documentation

# describe_resource_policy method definition

def describe_resource_policy(
    self,
    *,
    ResourceArn: str,
) -> DescribeResourcePolicyResponseTypeDef:  # (1)
    ...
  1. See DescribeResourcePolicyResponseTypeDef 
# describe_resource_policy method usage example with argument unpacking

kwargs: DescribeResourcePolicyRequestRequestTypeDef = {  # (1)
    "ResourceArn": ...,
}

parent.describe_resource_policy(**kwargs)
  1. See DescribeResourcePolicyRequestRequestTypeDef

describe_retraining_scheduler#

Provides a description of the retraining scheduler, including information such as the model name and retraining parameters.

Type annotations and code completion for boto3.client("lookoutequipment").describe_retraining_scheduler method. boto3 documentation

# describe_retraining_scheduler method definition

def describe_retraining_scheduler(
    self,
    *,
    ModelName: str,
) -> DescribeRetrainingSchedulerResponseTypeDef:  # (1)
    ...
  1. See DescribeRetrainingSchedulerResponseTypeDef 
# describe_retraining_scheduler method usage example with argument unpacking

kwargs: DescribeRetrainingSchedulerRequestRequestTypeDef = {  # (1)
    "ModelName": ...,
}

parent.describe_retraining_scheduler(**kwargs)
  1. See DescribeRetrainingSchedulerRequestRequestTypeDef

generate_presigned_url#

Generate a presigned url given a client, its method, and arguments.

Type annotations and code completion for boto3.client("lookoutequipment").generate_presigned_url method. boto3 documentation

# generate_presigned_url method definition

def generate_presigned_url(
    self,
    ClientMethod: str,
    Params: Mapping[str, Any] = ...,
    ExpiresIn: int = 3600,
    HttpMethod: str = ...,
) -> str:
    ...

import_dataset#

Imports a dataset.

Type annotations and code completion for boto3.client("lookoutequipment").import_dataset method. boto3 documentation

# import_dataset method definition

def import_dataset(
    self,
    *,
    SourceDatasetArn: str,
    ClientToken: str,
    DatasetName: str = ...,
    ServerSideKmsKeyId: str = ...,
    Tags: Sequence[TagTypeDef] = ...,  # (1)
) -> ImportDatasetResponseTypeDef:  # (2)
    ...
  1. See TagTypeDef
  2. See ImportDatasetResponseTypeDef 
# import_dataset method usage example with argument unpacking

kwargs: ImportDatasetRequestRequestTypeDef = {  # (1)
    "SourceDatasetArn": ...,
    "ClientToken": ...,
}

parent.import_dataset(**kwargs)
  1. See ImportDatasetRequestRequestTypeDef

import_model_version#

Imports a model that has been trained successfully.

Type annotations and code completion for boto3.client("lookoutequipment").import_model_version method. boto3 documentation

# import_model_version method definition

def import_model_version(
    self,
    *,
    SourceModelVersionArn: str,
    DatasetName: str,
    ClientToken: str,
    ModelName: str = ...,
    LabelsInputConfiguration: LabelsInputConfigurationTypeDef = ...,  # (1)
    RoleArn: str = ...,
    ServerSideKmsKeyId: str = ...,
    Tags: Sequence[TagTypeDef] = ...,  # (2)
    InferenceDataImportStrategy: InferenceDataImportStrategyType = ...,  # (3)
) -> ImportModelVersionResponseTypeDef:  # (4)
    ...
  1. See LabelsInputConfigurationTypeDef
  2. See TagTypeDef
  3. See InferenceDataImportStrategyType
  4. See ImportModelVersionResponseTypeDef 
# import_model_version method usage example with argument unpacking

kwargs: ImportModelVersionRequestRequestTypeDef = {  # (1)
    "SourceModelVersionArn": ...,
    "DatasetName": ...,
    "ClientToken": ...,
}

parent.import_model_version(**kwargs)
  1. See