SageMaker.Paginator.ListInferenceRecommendationsJobSteps¶paginator = client.get_paginator('list_inference_recommendations_job_steps')
paginate(**kwargs)¶Creates an iterator that will paginate through responses from SageMaker.Client.list_inference_recommendations_job_steps().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
JobName='string',
Status='PENDING'|'IN_PROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED',
StepType='BENCHMARK',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The name for the Inference Recommender job.
A filter to return details about the specified type of subtask.
BENCHMARK: Evaluate the performance of your model on different instance types.
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Steps': [
{
'StepType': 'BENCHMARK',
'JobName': 'string',
'Status': 'PENDING'|'IN_PROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED',
'InferenceBenchmark': {
'Metrics': {
'CostPerHour': ...,
'CostPerInference': ...,
'MaxInvocations': 123,
'ModelLatency': 123,
'CpuUtilization': ...,
'MemoryUtilization': ...
},
'EndpointConfiguration': {
'EndpointName': 'string',
'VariantName': 'string',
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'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.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'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.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'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.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge',
'InitialInstanceCount': 123
},
'ModelConfiguration': {
'InferenceSpecificationName': 'string',
'EnvironmentParameters': [
{
'Key': 'string',
'ValueType': 'string',
'Value': 'string'
},
],
'CompilationJobName': 'string'
},
'FailureReason': 'string',
'EndpointMetrics': {
'MaxInvocations': 123,
'ModelLatency': 123
}
}
},
],
}
Response Structure
(dict) --
Steps (list) --
A list of all subtask details in Inference Recommender.
(dict) --
A returned array object for the Steps response field in the ListInferenceRecommendationsJobSteps API command.
StepType (string) --
The type of the subtask.
BENCHMARK: Evaluate the performance of your model on different instance types.
JobName (string) --
The name of the Inference Recommender job.
Status (string) --
The current status of the benchmark.
InferenceBenchmark (dict) --
The details for a specific benchmark.
Metrics (dict) --
The metrics of recommendations.
CostPerHour (float) --
Defines the cost per hour for the instance.
CostPerInference (float) --
Defines the cost per inference for the instance .
MaxInvocations (integer) --
The expected maximum number of requests per minute for the instance.
ModelLatency (integer) --
The expected model latency at maximum invocation per minute for the instance.
CpuUtilization (float) --
The expected CPU utilization at maximum invocations per minute for the instance.
NaNindicates that the value is not available.
MemoryUtilization (float) --
The expected memory utilization at maximum invocations per minute for the instance.
NaNindicates that the value is not available.
EndpointConfiguration (dict) --
The endpoint configuration made by Inference Recommender during a recommendation job.
EndpointName (string) --
The name of the endpoint made during a recommendation job.
VariantName (string) --
The name of the production variant (deployed model) made during a recommendation job.
InstanceType (string) --
The instance type recommended by Amazon SageMaker Inference Recommender.
InitialInstanceCount (integer) --
The number of instances recommended to launch initially.
ModelConfiguration (dict) --
Defines the model configuration. Includes the specification name and environment parameters.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
EnvironmentParameters (list) --
Defines the environment parameters that includes key, value types, and values.
(dict) --
A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.
Key (string) --
The environment key suggested by the Amazon SageMaker Inference Recommender.
ValueType (string) --
The value type suggested by the Amazon SageMaker Inference Recommender.
Value (string) --
The value suggested by the Amazon SageMaker Inference Recommender.
CompilationJobName (string) --
The name of the compilation job used to create the recommended model artifacts.
FailureReason (string) --
The reason why a benchmark failed.
EndpointMetrics (dict) --
The metrics for an existing endpoint compared in an Inference Recommender job.
MaxInvocations (integer) --
The expected maximum number of requests per minute for the instance.
ModelLatency (integer) --
The expected model latency at maximum invocations per minute for the instance.