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AWS for Query.Farm

aws.sagemaker.describe_inference_experiment

Example SQL Queries

SELECT * FROM
aws.sagemaker.describe_inference_experiment
WHERE
"name" = 'VALUE';

Description

Returns details about an inference experiment.

Table Definition

Column NameColumn Data Type
name Required Input Column

The name of the inference experiment.

VARCHAR
_aws_profile Input Column

The AWS profile defines the AWS identity used. It can be defined via credentials or by assuming a IAM role.

STRUCT(
    "type" VARCHAR,
    "name" VARCHAR,
    "account_id" VARCHAR,
    "via_profile_name" VARCHAR,
    "assumed_role_arn" VARCHAR,
    "organization" STRUCT(
        "account_name" VARCHAR,
        "id" VARCHAR,
        "tags" STRUCT(
            "key" VARCHAR,
            "value" VARCHAR
        )[],
        "master_account" STRUCT(
            "id" VARCHAR,
            "email" VARCHAR
        ),
        "parents" STRUCT(
            "type" VARCHAR,
            "id" VARCHAR,
            "name" VARCHAR,
            "tags" STRUCT(
                "key" VARCHAR,
                "value" VARCHAR
            )[]
        )[]
    )
)
Show child fields
_aws_profile.account_id

The AWS account id

_aws_profile.assumed_role_arn

The ARN of the assumed role

_aws_profile.name

The unique name of the profile.

_aws_profile.organization

Information about this profile's membership in the AWS organization.

Show child fields
_aws_profile.organization.account_name

The name of account speciifed by the organization

_aws_profile.organization.id

The organization id

_aws_profile.organization.master_account
Show child fields
_aws_profile.organization.master_account.email

The organization master account email address

_aws_profile.organization.master_account.id

The organization master account id

_aws_profile.organization.parents[]
Show child fields
_aws_profile.organization.parents[].id

The id of the parent

_aws_profile.organization.parents[].name

The name of the parent

_aws_profile.organization.parents[].tags[]
Show child fields
_aws_profile.organization.parents[].tags[].key
_aws_profile.organization.parents[].tags[].value
_aws_profile.organization.parents[].type

The type of parent can be an organization unit or a root

_aws_profile.organization.tags[]
Show child fields
_aws_profile.organization.tags[].key
_aws_profile.organization.tags[].value
_aws_profile.type

The type of profile, either 'credentials' or 'assumed_role'

_aws_profile.via_profile_name

This IAM role for this profile is assumed by first utilizing another profile with this name to obtain credentials.

arn

The ARN of the inference experiment being described.

VARCHAR
completion_time

The timestamp at which the inference experiment was completed. 

TIMESTAMP_S
creation_time

The timestamp at which you created the inference experiment.

TIMESTAMP_S
data_storage_config

The Amazon S3 location and configuration for storing inference request and response data.

STRUCT(
    "destination" VARCHAR,
    "kms_key" VARCHAR,
    "content_type" STRUCT(
        "csv_content_types" VARCHAR[],
        "json_content_types" VARCHAR[]
    )
)
Show child fields
data_storage_config.content_type

Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker will by default base64 encode when capturing the data.

Show child fields
data_storage_config.content_type.csv_content_types[]
data_storage_config.content_type.json_content_types[]
data_storage_config.destination

The Amazon S3 bucket where the inference request and response data is stored.

data_storage_config.kms_key

The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.

description

The description of the inference experiment.

VARCHAR
endpoint_metadata

The metadata of the endpoint on which the inference experiment ran.

STRUCT(
    "endpoint_name" VARCHAR,
    "endpoint_config_name" VARCHAR,
    "endpoint_status" VARCHAR,
    "failure_reason" VARCHAR
)
Show child fields
endpoint_metadata.endpoint_config_name

The name of the endpoint configuration.

endpoint_metadata.endpoint_name

The name of the endpoint.

endpoint_metadata.endpoint_status

The status of the endpoint. For possible values of the status of an endpoint, see EndpointSummary.

endpoint_metadata.failure_reason

If the status of the endpoint is Failed, or the status is InService but update operation fails, this provides the reason why it failed.

kms_key

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. For more information, see CreateInferenceExperiment. 

VARCHAR
last_modified_time

The timestamp at which you last modified the inference experiment.

TIMESTAMP_S
model_variants

An array of ModelVariantConfigSummary objects. There is one for each variant in the inference experiment. Each ModelVariantConfigSummary object in the array describes the infrastructure configuration for deploying the corresponding variant. 

STRUCT(
    "model_name" VARCHAR,
    "variant_name" VARCHAR,
    "infrastructure_config" STRUCT(
        "infrastructure_type" VARCHAR,
        "real_time_inference_config" STRUCT(
            "instance_type" VARCHAR,
            "instance_count" BIGINT
        )
    ),
    "status" VARCHAR
)[]
Show child fields
model_variants[]
Show child fields
model_variants[].infrastructure_config

The configuration of the infrastructure that the model has been deployed to.

Show child fields
model_variants[].infrastructure_config.infrastructure_type

The inference option to which to deploy your model. Possible values are the following:

  • RealTime: Deploy to real-time inference.

model_variants[].infrastructure_config.real_time_inference_config

The infrastructure configuration for deploying the model to real-time inference.

Show child fields
model_variants[].infrastructure_config.real_time_inference_config.instance_count

The number of instances of the type specified by InstanceType.

model_variants[].infrastructure_config.real_time_inference_config.instance_type

The instance type the model is deployed to.

model_variants[].model_name

The name of the Amazon SageMaker Model entity.

model_variants[].status

The status of deployment for the model variant on the hosted inference endpoint.

  • Creating - Amazon SageMaker is preparing the model variant on the hosted inference endpoint.

  • InService - The model variant is running on the hosted inference endpoint.

  • Updating - Amazon SageMaker is updating the model variant on the hosted inference endpoint.

  • Deleting - Amazon SageMaker is deleting the model variant on the hosted inference endpoint.

  • Deleted - The model variant has been deleted on the hosted inference endpoint. This can only happen after stopping the experiment.

model_variants[].variant_name

The name of the variant.

role_arn

The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment. 

VARCHAR
schedule

The duration for which the inference experiment ran or will run.

STRUCT(
    "start_time" TIMESTAMP_S,
    "end_time" TIMESTAMP_S
)
Show child fields
schedule.end_time

The timestamp at which the inference experiment ended or will end.

schedule.start_time

The timestamp at which the inference experiment started or will start.

shadow_mode_config

The configuration of ShadowMode inference experiment type, which shows the production variant that takes all the inference requests, and the shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant it also shows the percentage of requests that Amazon SageMaker replicates. 

STRUCT(
    "source_model_variant_name" VARCHAR,
    "shadow_model_variants" STRUCT(
        "shadow_model_variant_name" VARCHAR,
        "sampling_percentage" BIGINT
    )[]
)
Show child fields
shadow_mode_config.shadow_model_variants[]
Show child fields
shadow_mode_config.shadow_model_variants[].sampling_percentage

The percentage of inference requests that Amazon SageMaker replicates from the production variant to the shadow variant.

shadow_mode_config.shadow_model_variants[].shadow_model_variant_name

The name of the shadow variant.

shadow_mode_config.source_model_variant_name

The name of the production variant, which takes all the inference requests.

status

The status of the inference experiment. The following are the possible statuses for an inference experiment:

  • Creating - Amazon SageMaker is creating your experiment.

  • Created - Amazon SageMaker has finished the creation of your experiment and will begin the experiment at the scheduled time.

  • Updating - When you make changes to your experiment, your experiment shows as updating.

  • Starting - Amazon SageMaker is beginning your experiment.

  • Running - Your experiment is in progress.

  • Stopping - Amazon SageMaker is stopping your experiment.

  • Completed - Your experiment has completed.

  • Cancelled - When you conclude your experiment early using the StopInferenceExperiment API, or if any operation fails with an unexpected error, it shows as cancelled. 

VARCHAR
status_reason

The error message or client-specified Reason from the StopInferenceExperiment API, that explains the status of the inference experiment. 

VARCHAR
type

The type of the inference experiment.

VARCHAR