SageMaker / Client / update_inference_experiment
update_inference_experiment#
- SageMaker.Client.update_inference_experiment(**kwargs)#
Updates an inference experiment that you created. The status of the inference experiment has to be either
Created
,Running
. For more information on the status of an inference experiment, see DescribeInferenceExperiment.See also: AWS API Documentation
Request Syntax
response = client.update_inference_experiment( Name='string', Schedule={ 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1) }, Description='string', ModelVariants=[ { 'ModelName': 'string', 'VariantName': 'string', 'InfrastructureConfig': { 'InfrastructureType': 'RealTimeInference', 'RealTimeInferenceConfig': { 'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge', 'InstanceCount': 123 } } }, ], DataStorageConfig={ 'Destination': 'string', 'KmsKey': 'string', 'ContentType': { 'CsvContentTypes': [ 'string', ], 'JsonContentTypes': [ 'string', ] } }, ShadowModeConfig={ 'SourceModelVariantName': 'string', 'ShadowModelVariants': [ { 'ShadowModelVariantName': 'string', 'SamplingPercentage': 123 }, ] } )
- Parameters:
Name (string) –
[REQUIRED]
The name of the inference experiment to be updated.
Schedule (dict) –
The duration for which the inference experiment will run. If the status of the inference experiment is
Created
, then you can update both the start and end dates. If the status of the inference experiment isRunning
, then you can update only the end date.StartTime (datetime) –
The timestamp at which the inference experiment started or will start.
EndTime (datetime) –
The timestamp at which the inference experiment ended or will end.
Description (string) – The description of the inference experiment.
ModelVariants (list) –
An array of
ModelVariantConfig
objects. There is one for each variant, whose infrastructure configuration you want to update.(dict) –
Contains information about the deployment options of a model.
ModelName (string) – [REQUIRED]
The name of the Amazon SageMaker Model entity.
VariantName (string) – [REQUIRED]
The name of the variant.
InfrastructureConfig (dict) – [REQUIRED]
The configuration for the infrastructure that the model will be deployed to.
InfrastructureType (string) – [REQUIRED]
The inference option to which to deploy your model. Possible values are the following:
RealTime
: Deploy to real-time inference.
RealTimeInferenceConfig (dict) – [REQUIRED]
The infrastructure configuration for deploying the model to real-time inference.
InstanceType (string) – [REQUIRED]
The instance type the model is deployed to.
InstanceCount (integer) – [REQUIRED]
The number of instances of the type specified by
InstanceType
.
DataStorageConfig (dict) –
The Amazon S3 location and configuration for storing inference request and response data.
Destination (string) – [REQUIRED]
The Amazon S3 bucket where the inference request and response data is stored.
KmsKey (string) –
The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.
ContentType (dict) –
Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker AI will by default base64 encode when capturing the data.
CsvContentTypes (list) –
The list of all content type headers that Amazon SageMaker AI will treat as CSV and capture accordingly.
(string) –
JsonContentTypes (list) –
The list of all content type headers that SageMaker AI will treat as JSON and capture accordingly.
(string) –
ShadowModeConfig (dict) –
The configuration of
ShadowMode
inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.SourceModelVariantName (string) – [REQUIRED]
The name of the production variant, which takes all the inference requests.
ShadowModelVariants (list) – [REQUIRED]
List of shadow variant configurations.
(dict) –
The name and sampling percentage of a shadow variant.
ShadowModelVariantName (string) – [REQUIRED]
The name of the shadow variant.
SamplingPercentage (integer) – [REQUIRED]
The percentage of inference requests that Amazon SageMaker replicates from the production variant to the shadow variant.
- Return type:
dict
- Returns:
Response Syntax
{ 'InferenceExperimentArn': 'string' }
Response Structure
(dict) –
InferenceExperimentArn (string) –
The ARN of the updated inference experiment.
Exceptions
SageMaker.Client.exceptions.ConflictException
SageMaker.Client.exceptions.ResourceNotFound