Skip to content
AWS for Query.Farm

aws.sagemaker.describe_endpoint_config

Example SQL Queries

SELECT * FROM
aws.sagemaker.describe_endpoint_config
WHERE
"endpoint_config_name" = 'VALUE';

Description

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

Table Definition

Column NameColumn Data Type
endpoint_config_name Required Input Column

Name of the SageMaker endpoint configuration.

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.

async_inference_config

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

STRUCT(
    "client_config" STRUCT(
        "max_concurrent_invocations_per_instance" BIGINT
    ),
    "output_config" STRUCT(
        "kms_key_id" VARCHAR,
        "s3_output_path" VARCHAR,
        "notification_config" STRUCT(
            "success_topic" VARCHAR,
            "error_topic" VARCHAR,
            "include_inference_response_in" VARCHAR[]
        ),
        "s3_failure_path" VARCHAR
    )
)
Show child fields
async_inference_config.client_config

Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

Show child fields
async_inference_config.client_config.max_concurrent_invocations_per_instance

The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.

async_inference_config.output_config

Specifies the configuration for asynchronous inference invocation outputs.

Show child fields
async_inference_config.output_config.kms_key_id

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.

async_inference_config.output_config.notification_config

Specifies the configuration for notifications of inference results for asynchronous inference.

Show child fields
async_inference_config.output_config.notification_config.error_topic

Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.

async_inference_config.output_config.notification_config.include_inference_response_in[]
async_inference_config.output_config.notification_config.success_topic

Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.

async_inference_config.output_config.s3_failure_path

The Amazon S3 location to upload failure inference responses to.

async_inference_config.output_config.s3_output_path

The Amazon S3 location to upload inference responses to.

creation_time

A timestamp that shows when the endpoint configuration was created.

TIMESTAMP_S
data_capture_config

Configuration to control how SageMaker captures inference data.

STRUCT(
    "enable_capture" BOOLEAN,
    "initial_sampling_percentage" BIGINT,
    "destination_s3_uri" VARCHAR,
    "kms_key_id" VARCHAR,
    "capture_options" STRUCT(
        "capture_mode" VARCHAR
    )[],
    "capture_content_type_header" STRUCT(
        "csv_content_types" VARCHAR[],
        "json_content_types" VARCHAR[]
    )
)
Show child fields
data_capture_config.capture_content_type_header

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

Show child fields
data_capture_config.capture_content_type_header.csv_content_types[]
data_capture_config.capture_content_type_header.json_content_types[]
data_capture_config.capture_options[]
Show child fields
data_capture_config.capture_options[].capture_mode

Specify the boundary of data to capture.

data_capture_config.destination_s3_uri

The Amazon S3 location used to capture the data.

data_capture_config.enable_capture

Whether data capture should be enabled or disabled (defaults to enabled).

data_capture_config.initial_sampling_percentage

The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.

data_capture_config.kms_key_id

The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker uses to encrypt the captured data at rest using Amazon S3 server-side encryption.

The KmsKeyId can be any of the following formats:

  • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

  • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

  • Alias name: alias/ExampleAlias

  • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

enable_network_isolation

Indicates whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.

BOOLEAN
endpoint_config_arn

The Amazon Resource Name (ARN) of the endpoint configuration.

VARCHAR
execution_role_arn

The Amazon Resource Name (ARN) of the IAM role that you assigned to the endpoint configuration.

VARCHAR
explainer_config

The configuration parameters for an explainer.

STRUCT(
    "clarify_explainer_config" STRUCT(
        "enable_explanations" VARCHAR,
        "inference_config" STRUCT(
            "features_attribute" VARCHAR,
            "content_template" VARCHAR,
            "max_record_count" BIGINT,
            "max_payload_in_mb" BIGINT,
            "probability_index" BIGINT,
            "label_index" BIGINT,
            "probability_attribute" VARCHAR,
            "label_attribute" VARCHAR,
            "label_headers" VARCHAR[],
            "feature_headers" VARCHAR[],
            "feature_types" VARCHAR[]
        ),
        "shap_config" STRUCT(
            "shap_baseline_config" STRUCT(
                "mime_type" VARCHAR,
                "shap_baseline" VARCHAR,
                "shap_baseline_uri" VARCHAR
            ),
            "number_of_samples" BIGINT,
            "use_logit" BOOLEAN,
            "seed" BIGINT,
            "text_config" STRUCT(
                "language" VARCHAR,
                "granularity" VARCHAR
            )
        )
    )
)
Show child fields
explainer_config.clarify_explainer_config

A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.

Show child fields
explainer_config.clarify_explainer_config.enable_explanations

A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See EnableExplanations for additional information.

explainer_config.clarify_explainer_config.inference_config

The inference configuration parameter for the model container.

Show child fields
explainer_config.clarify_explainer_config.inference_config.content_template

A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}'. Required only when the model container input is in JSON Lines format.

explainer_config.clarify_explainer_config.inference_config.feature_headers[]
explainer_config.clarify_explainer_config.inference_config.feature_types[]
explainer_config.clarify_explainer_config.inference_config.features_attribute

Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures', it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}'.

explainer_config.clarify_explainer_config.inference_config.label_attribute

A JMESPath expression used to locate the list of label headers in the model container output.

Example: If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}', then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]

explainer_config.clarify_explainer_config.inference_config.label_headers[]
explainer_config.clarify_explainer_config.inference_config.label_index

A zero-based index used to extract a label header or list of label headers from model container output in CSV format.

Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"', set LabelIndex to 0 to select the label headers ['cat','dog','fish'].

explainer_config.clarify_explainer_config.inference_config.max_payload_in_mb

The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

explainer_config.clarify_explainer_config.inference_config.max_record_count

The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.

explainer_config.clarify_explainer_config.inference_config.probability_attribute

A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.

Example: If the model container output of a single request is '{"predicted_label":1,"probability":0.6}', then set ProbabilityAttribute to 'probability'.

explainer_config.clarify_explainer_config.inference_config.probability_index

A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.

Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6', set ProbabilityIndex to 1 to select the probability value 0.6.

Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"', set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3].

explainer_config.clarify_explainer_config.shap_config

The configuration for SHAP analysis.

Show child fields
explainer_config.clarify_explainer_config.shap_config.number_of_samples

The number of samples to be used for analysis by the Kernal SHAP algorithm.

The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint.

explainer_config.clarify_explainer_config.shap_config.seed

The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

explainer_config.clarify_explainer_config.shap_config.shap_baseline_config

The configuration for the SHAP baseline of the Kernal SHAP algorithm.

Show child fields
explainer_config.clarify_explainer_config.shap_config.shap_baseline_config.mime_type

The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines'. Defaults to 'text/csv'.

explainer_config.clarify_explainer_config.shap_config.shap_baseline_config.shap_baseline

The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

explainer_config.clarify_explainer_config.shap_config.shap_baseline_config.shap_baseline_uri

The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.

explainer_config.clarify_explainer_config.shap_config.text_config

A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

Show child fields
explainer_config.clarify_explainer_config.shap_config.text_config.granularity

The unit of granularity for the analysis of text features. For example, if the unit is 'token', then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.

explainer_config.clarify_explainer_config.shap_config.text_config.language

Specifies the language of the text features in ISO 639-1 or ISO 639-3 code of a supported language.

For a mix of multiple languages, use code 'xx'.

explainer_config.clarify_explainer_config.shap_config.use_logit

A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

kms_key_id

Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

VARCHAR
production_variants

An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

STRUCT(
    "variant_name" VARCHAR,
    "model_name" VARCHAR,
    "initial_instance_count" BIGINT,
    "instance_type" VARCHAR,
    "initial_variant_weight" DOUBLE,
    "accelerator_type" VARCHAR,
    "core_dump_config" STRUCT(
        "destination_s3_uri" VARCHAR,
        "kms_key_id" VARCHAR
    ),
    "serverless_config" STRUCT(
        "memory_size_in_mb" BIGINT,
        "max_concurrency" BIGINT,
        "provisioned_concurrency" BIGINT
    ),
    "volume_size_in_gb" BIGINT,
    "model_data_download_timeout_in_seconds" BIGINT,
    "container_startup_health_check_timeout_in_seconds" BIGINT,
    "enable_ssm_access" BOOLEAN,
    "managed_instance_scaling" STRUCT(
        "status" VARCHAR,
        "min_instance_count" BIGINT,
        "max_instance_count" BIGINT
    ),
    "routing_config" STRUCT(
        "routing_strategy" VARCHAR
    ),
    "inference_ami_version" VARCHAR
)[]
Show child fields
production_variants[]
Show child fields
production_variants[].accelerator_type

The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.

production_variants[].container_startup_health_check_timeout_in_seconds

The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.

production_variants[].core_dump_config

Specifies configuration for a core dump from the model container when the process crashes.

Show child fields
production_variants[].core_dump_config.destination_s3_uri

The Amazon S3 bucket to send the core dump to.

production_variants[].core_dump_config.kms_key_id

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

  • // KMS Key Alias

    "alias/ExampleAlias"

  • // Amazon Resource Name (ARN) of a KMS Key Alias

    "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.

production_variants[].enable_ssm_access

You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint.

production_variants[].inference_ami_version

Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions. Amazon Web Services optimizes these configurations for different machine learning workloads.

By selecting an AMI version, you can ensure that your inference environment is compatible with specific software requirements, such as CUDA driver versions, Linux kernel versions, or Amazon Web Services Neuron driver versions.

The AMI version names, and their configurations, are the following:

al2-ami-sagemaker-inference-gpu-2

  • Accelerator: GPU

  • NVIDIA driver version: 535.54.03

  • CUDA driver version: 12.2

  • Supported instance types: ml.g4dn.*, ml.g5.*, ml.g6.*, ml.p3.*, ml.p4d.*, ml.p4de.*, ml.p5.*

production_variants[].initial_instance_count

Number of instances to launch initially.

production_variants[].initial_variant_weight

Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

production_variants[].instance_type

The ML compute instance type.

production_variants[].managed_instance_scaling

Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

Show child fields
production_variants[].managed_instance_scaling.max_instance_count

The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

production_variants[].managed_instance_scaling.min_instance_count

The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

production_variants[].managed_instance_scaling.status

Indicates whether managed instance scaling is enabled.

production_variants[].model_data_download_timeout_in_seconds

The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

production_variants[].model_name

The name of the model that you want to host. This is the name that you specified when creating the model.

production_variants[].routing_config

Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

Show child fields
production_variants[].routing_config.routing_strategy

Sets how the endpoint routes incoming traffic:

  • LEAST_OUTSTANDING_REQUESTS: The endpoint routes requests to the specific instances that have more capacity to process them.

  • RANDOM: The endpoint routes each request to a randomly chosen instance.

production_variants[].serverless_config

The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

Show child fields
production_variants[].serverless_config.max_concurrency

The maximum number of concurrent invocations your serverless endpoint can process.

production_variants[].serverless_config.memory_size_in_mb

The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

production_variants[].serverless_config.provisioned_concurrency

The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency.

This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.

production_variants[].variant_name

The name of the production variant.

production_variants[].volume_size_in_gb

The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

shadow_production_variants

An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants.

STRUCT(
    "variant_name" VARCHAR,
    "model_name" VARCHAR,
    "initial_instance_count" BIGINT,
    "instance_type" VARCHAR,
    "initial_variant_weight" DOUBLE,
    "accelerator_type" VARCHAR,
    "core_dump_config" STRUCT(
        "destination_s3_uri" VARCHAR,
        "kms_key_id" VARCHAR
    ),
    "serverless_config" STRUCT(
        "memory_size_in_mb" BIGINT,
        "max_concurrency" BIGINT,
        "provisioned_concurrency" BIGINT
    ),
    "volume_size_in_gb" BIGINT,
    "model_data_download_timeout_in_seconds" BIGINT,
    "container_startup_health_check_timeout_in_seconds" BIGINT,
    "enable_ssm_access" BOOLEAN,
    "managed_instance_scaling" STRUCT(
        "status" VARCHAR,
        "min_instance_count" BIGINT,
        "max_instance_count" BIGINT
    ),
    "routing_config" STRUCT(
        "routing_strategy" VARCHAR
    ),
    "inference_ami_version" VARCHAR
)[]
Show child fields
shadow_production_variants[]
Show child fields
shadow_production_variants[].accelerator_type

The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.

shadow_production_variants[].container_startup_health_check_timeout_in_seconds

The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.

shadow_production_variants[].core_dump_config

Specifies configuration for a core dump from the model container when the process crashes.

Show child fields
shadow_production_variants[].core_dump_config.destination_s3_uri

The Amazon S3 bucket to send the core dump to.

shadow_production_variants[].core_dump_config.kms_key_id

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

  • // KMS Key Alias

    "alias/ExampleAlias"

  • // Amazon Resource Name (ARN) of a KMS Key Alias

    "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.

shadow_production_variants[].enable_ssm_access

You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint.

shadow_production_variants[].inference_ami_version

Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions. Amazon Web Services optimizes these configurations for different machine learning workloads.

By selecting an AMI version, you can ensure that your inference environment is compatible with specific software requirements, such as CUDA driver versions, Linux kernel versions, or Amazon Web Services Neuron driver versions.

The AMI version names, and their configurations, are the following:

al2-ami-sagemaker-inference-gpu-2

  • Accelerator: GPU

  • NVIDIA driver version: 535.54.03

  • CUDA driver version: 12.2

  • Supported instance types: ml.g4dn.*, ml.g5.*, ml.g6.*, ml.p3.*, ml.p4d.*, ml.p4de.*, ml.p5.*

shadow_production_variants[].initial_instance_count

Number of instances to launch initially.

shadow_production_variants[].initial_variant_weight

Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

shadow_production_variants[].instance_type

The ML compute instance type.

shadow_production_variants[].managed_instance_scaling

Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

Show child fields
shadow_production_variants[].managed_instance_scaling.max_instance_count

The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.

shadow_production_variants[].managed_instance_scaling.min_instance_count

The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.

shadow_production_variants[].managed_instance_scaling.status

Indicates whether managed instance scaling is enabled.

shadow_production_variants[].model_data_download_timeout_in_seconds

The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

shadow_production_variants[].model_name

The name of the model that you want to host. This is the name that you specified when creating the model.

shadow_production_variants[].routing_config

Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

Show child fields
shadow_production_variants[].routing_config.routing_strategy

Sets how the endpoint routes incoming traffic:

  • LEAST_OUTSTANDING_REQUESTS: The endpoint routes requests to the specific instances that have more capacity to process them.

  • RANDOM: The endpoint routes each request to a randomly chosen instance.

shadow_production_variants[].serverless_config

The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

Show child fields
shadow_production_variants[].serverless_config.max_concurrency

The maximum number of concurrent invocations your serverless endpoint can process.

shadow_production_variants[].serverless_config.memory_size_in_mb

The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

shadow_production_variants[].serverless_config.provisioned_concurrency

The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency.

This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.

shadow_production_variants[].variant_name

The name of the production variant.

shadow_production_variants[].volume_size_in_gb

The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

vpc_config

Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC. 

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