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AWS for Query.Farm

aws.sagemaker.describe_optimization_job

Example SQL Queries

SELECT * FROM
aws.sagemaker.describe_optimization_job
WHERE
"optimization_job_name" = 'VALUE';

Description

Provides the properties of the specified optimization job.

Table Definition

Column NameColumn Data Type
optimization_job_name Required Input Column

The name that you assigned to the optimization job.

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.

creation_time

The time when you created the optimization job.

TIMESTAMP_S
deployment_instance_type

The type of instance that hosts the optimized model that you create with the optimization job.

VARCHAR
failure_reason

If the optimization job status is FAILED, the reason for the failure.

VARCHAR
last_modified_time

The time when the optimization job was last updated.

TIMESTAMP_S
model_source

The location of the source model to optimize with an optimization job.

STRUCT(
    "s3" STRUCT(
        "s3_uri" VARCHAR,
        "model_access_config" STRUCT(
            "accept_eula" BOOLEAN
        )
    )
)
Show child fields
model_source.s3

The Amazon S3 location of a source model to optimize with an optimization job.

Show child fields
model_source.s3.model_access_config

The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).

Show child fields
model_source.s3.model_access_config.accept_eula

Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

model_source.s3.s3_uri

An Amazon S3 URI that locates a source model to optimize with an optimization job.

optimization_configs

Settings for each of the optimization techniques that the job applies.

STRUCT(
    "model_quantization_config" STRUCT(
        "image" VARCHAR,
        "override_environment" MAP(VARCHAR, VARCHAR)
    ),
    "model_compilation_config" STRUCT(
        "image" VARCHAR,
        "override_environment" MAP(VARCHAR, VARCHAR)
    )
)[]
Show child fields
optimization_configs[]
Show child fields
optimization_configs[].model_compilation_config

Settings for the model compilation technique that's applied by a model optimization job.

Show child fields
optimization_configs[].model_compilation_config.image

The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.

optimization_configs[].model_compilation_config.override_environment

Environment variables that override the default ones in the model container.

optimization_configs[].model_quantization_config

Settings for the model quantization technique that's applied by a model optimization job.

Show child fields
optimization_configs[].model_quantization_config.image

The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.

optimization_configs[].model_quantization_config.override_environment

Environment variables that override the default ones in the model container.

optimization_end_time

The time when the optimization job finished processing.

TIMESTAMP_S
optimization_environment

The environment variables to set in the model container.

MAP(VARCHAR, VARCHAR)
optimization_job_arn

The Amazon Resource Name (ARN) of the optimization job.

VARCHAR
optimization_job_status

The current status of the optimization job.

VARCHAR
optimization_output

Output values produced by an optimization job.

STRUCT(
    "recommended_inference_image" VARCHAR
)
Show child fields
optimization_output.recommended_inference_image

The image that SageMaker recommends that you use to host the optimized model that you created with an optimization job.

optimization_start_time

The time when the optimization job started.

TIMESTAMP_S
output_config

Details for where to store the optimized model that you create with the optimization job.

STRUCT(
    "kms_key_id" VARCHAR,
    "s3_output_location" VARCHAR
)
Show child fields
output_config.kms_key_id

The Amazon Resource Name (ARN) of a key in Amazon Web Services KMS. SageMaker uses they key to encrypt the artifacts of the optimized model when SageMaker uploads the model to Amazon S3.

output_config.s3_output_location

The Amazon S3 URI for where to store the optimized model that you create with an optimization job.

role_arn

The ARN of the IAM role that you assigned to the optimization job.

VARCHAR
stopping_condition

Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.

To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel.

The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

STRUCT(
    "max_runtime_in_seconds" BIGINT,
    "max_wait_time_in_seconds" BIGINT,
    "max_pending_time_in_seconds" BIGINT
)
Show child fields
stopping_condition.max_pending_time_in_seconds

The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.

stopping_condition.max_runtime_in_seconds

The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

stopping_condition.max_wait_time_in_seconds

The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds. If the job does not complete during this time, SageMaker ends the job.

When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

vpc_config

A VPC in Amazon VPC that your optimized model has access to.

STRUCT(
    "security_group_ids" VARCHAR[],
    "subnets" VARCHAR[]
)
Show child fields
vpc_config.security_group_ids[]
vpc_config.subnets[]