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[ aws . sagemaker ]



Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.


Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.

In the request, you define a ProductionVariant , for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.

If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).


When you call CreateEndpoint , a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting ` Eventually Consistent Reads`__ , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

See also: AWS API Documentation

See 'aws help' for descriptions of global parameters.


--endpoint-config-name <value>
--production-variants <value>
[--data-capture-config <value>]
[--tags <value>]
[--kms-key-id <value>]
[--cli-input-json <value>]
[--generate-cli-skeleton <value>]


--endpoint-config-name (string)

The name of the endpoint configuration. You specify this name in a CreateEndpoint request.

--production-variants (list)

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


Identifies a model that you want to host and the resources to deploy for hosting it. If you are deploying multiple models, tell Amazon SageMaker how to distribute traffic among the models by specifying variant weights.

VariantName -> (string)

The name of the production variant.

ModelName -> (string)

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

InitialInstanceCount -> (integer)

Number of instances to launch initially.

InstanceType -> (string)

The ML compute instance type.

InitialVariantWeight -> (float)

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.

AcceleratorType -> (string)

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 .

Shorthand Syntax:

VariantName=string,ModelName=string,InitialInstanceCount=integer,InstanceType=string,InitialVariantWeight=float,AcceleratorType=string ...

JSON Syntax:

    "VariantName": "string",
    "ModelName": "string",
    "InitialInstanceCount": integer,
    "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",
    "InitialVariantWeight": float,
    "AcceleratorType": "ml.eia1.medium"|"ml.eia1.large"|"ml.eia1.xlarge"|"ml.eia2.medium"|"ml.eia2.large"|"ml.eia2.xlarge"

--data-capture-config (structure)

EnableCapture -> (boolean)

InitialSamplingPercentage -> (integer)

DestinationS3Uri -> (string)

KmsKeyId -> (string)

CaptureOptions -> (list)


CaptureMode -> (string)

CaptureContentTypeHeader -> (structure)

CsvContentTypes -> (list)


JsonContentTypes -> (list)


Shorthand Syntax:


JSON Syntax:

  "EnableCapture": true|false,
  "InitialSamplingPercentage": integer,
  "DestinationS3Uri": "string",
  "KmsKeyId": "string",
  "CaptureOptions": [
      "CaptureMode": "Input"|"Output"
  "CaptureContentTypeHeader": {
    "CsvContentTypes": ["string", ...],
    "JsonContentTypes": ["string", ...]

--tags (list)

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources .


Describes a tag.

Key -> (string)

The tag key.

Value -> (string)

The tag value.

Shorthand Syntax:

Key=string,Value=string ...

JSON Syntax:

    "Key": "string",
    "Value": "string"

--kms-key-id (string)

The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.

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

The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint , UpdateEndpoint requests. For more information, refer to the AWS Key Management Service section`Using Key Policies in AWS KMS <>`__


Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a KmsKeyId when using an instance type with local storage. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local storage, the call to CreateEndpointConfig fails.

For a list of instance types that support local instance storage, see Instance Store Volumes .

For more information about local instance storage encryption, see SSD Instance Store Volumes .

--cli-input-json (string) Performs service operation based on the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, the CLI values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See 'aws help' for descriptions of global parameters.


EndpointConfigArn -> (string)

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