Delete Endpoints and Resources - Amazon SageMaker AI
Delete EndpointDelete Endpoint ConfigurationDelete Model

Delete Endpoints and Resources

Delete endpoints to stop incurring charges.

Delete Endpoint

Delete your endpoint programmatically using AWS SDK for Python (Boto3), with the AWS CLI, or interactively using the SageMaker AI console.

SageMaker AI frees up all of the resources that were deployed when the endpoint was created. Deleting an endpoint will not delete the endpoint configuration or the SageMaker AI model. See Delete Endpoint Configuration and Delete Model for information on how to delete your endpoint configuration and SageMaker AI model.

AWS SDK for Python (Boto3)

Use the DeleteEndpoint API to delete your endpoint. Specify the name of your endpoint for the EndpointName field.

import boto3

# Specify your AWS Region
aws_region='<aws_region>'

# Specify the name of your endpoint
endpoint_name='<endpoint_name>'

# Create a low-level SageMaker service client.
sagemaker_client = boto3.client('sagemaker', region_name=aws_region)

# Delete endpoint
sagemaker_client.delete_endpoint(EndpointName=endpoint_name)
AWS CLI

Use the delete-endpoint command to delete your endpoint. Specify the name of your endpoint for the endpoint-name flag.

aws sagemaker delete-endpoint --endpoint-name <endpoint-name>
SageMaker AI Console

Delete your endpoint interactively with the SageMaker AI console.

  1. In the SageMaker AI console at https://console.aws.amazon.com/sagemaker/ navigation menu, choose Inference.

  2. Choose Endpoints from the drop down menu. A list of endpoints created in you AWS account will appear by name, Amazon Resource Name (ARN), creation time, status, and a time stamp of when the endpoint was last updated.

  3. Select the endpoint you want to delete.

  4. Select the Actions dropdown button in the top right corner.

  5. Choose Delete.

Delete Endpoint Configuration

Delete your endpoint configuration programmaticially using AWS SDK for Python (Boto3), with the AWS CLI, or interactively using the SageMaker AI console. Deleting an endpoint configuration does not delete endpoints created using this configuration. See Delete Endpoint for information on how to delete your endpoint.

Do not delete an endpoint configuration in use by an endpoint that is live or while the endpoint is being updated or created. You might lose visibility into the instance type the endpoint is using if you delete the endpoint configuration of an endpoint that is active or being created or updated.

AWS SDK for Python (Boto3)

Use the DeleteEndpointConfig API to delete your endpoint. Specify the name of your endpoint configuration for the EndpointConfigName field.

import boto3

# Specify your AWS Region
aws_region='<aws_region>'

# Specify the name of your endpoint configuration
endpoint_config_name='<endpoint_name>'

# Create a low-level SageMaker service client.
sagemaker_client = boto3.client('sagemaker', region_name=aws_region)

# Delete endpoint configuration
sagemaker_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)

You can optionally use the DescribeEndpointConfig API to return information about the name of the your deployed models (production variants) such as the name of your model and the name of the endpoint configuration associated with that deployed model. Provide the name of your endpoint for the EndpointConfigName field. 

# Specify the name of your endpoint
endpoint_name='<endpoint_name>'

# Create a low-level SageMaker service client.
sagemaker_client = boto3.client('sagemaker', region_name=aws_region)

# Store DescribeEndpointConfig response into a variable that we can index in the next step.
response = sagemaker_client.describe_endpoint_config(EndpointConfigName=endpoint_name)

# Delete endpoint
endpoint_config_name = response['ProductionVariants'][0]['EndpointConfigName']
                        
# Delete endpoint configuration
sagemaker_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)

For more information about other response elements returned by DescribeEndpointConfig, see DescribeEndpointConfig in the SageMaker API Reference guide.

AWS CLI

Use the delete-endpoint-config command to delete your endpoint configuration. Specify the name of your endpoint configuration for the endpoint-config-name flag.

aws sagemaker delete-endpoint-config \
                        --endpoint-config-name <endpoint-config-name>

You can optionally use the describe-endpoint-config command to return information about the name of the your deployed models (production variants) such as the name of your model and the name of the endpoint configuration associated with that deployed model. Provide the name of your endpoint for the endpoint-config-name flag.

aws sagemaker describe-endpoint-config --endpoint-config-name <endpoint-config-name>

This will return a JSON response. You can copy and paste, use a JSON parser, or use a tool built for JSON parsing to obtain the endpoint configuration name associated with that endpoint.

SageMaker AI Console

Delete your endpoint configuration interactively with the SageMaker AI console.

  1. In the SageMaker AI console at https://console.aws.amazon.com/sagemaker/ navigation menu, choose Inference.

  2. Choose Endpoint configurations from the dropdown menu. A list of endpoint configurations created in you AWS account will appear by name, Amazon Resource Name (ARN), and creation time.

  3. Select the endpoint configuration you want to delete.

  4. Select the Actions dropdown button in the top right corner.

  5. Choose Delete.

Delete Model

Delete your SageMaker AI model programmaticially using AWS SDK for Python (Boto3), with the AWS CLI, or interactively using the SageMaker AI console. Deleting a SageMaker AI model only deletes the model entry that was created in SageMaker AI. Deleting a model does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.

AWS SDK for Python (Boto3)

Use the DeleteModel API to delete your SageMaker AI model. Specify the name of your model for the ModelName field.

import boto3

# Specify your AWS Region
aws_region='<aws_region>'

# Specify the name of your endpoint configuration
model_name='<model_name>'

# Create a low-level SageMaker service client.
sagemaker_client = boto3.client('sagemaker', region_name=aws_region)

# Delete model
sagemaker_client.delete_model(ModelName=model_name)

You can optionally use the DescribeEndpointConfig API to return information about the name of the your deployed models (production variants) such as the name of your model and the name of the endpoint configuration associated with that deployed model. Provide the name of your endpoint for the EndpointConfigName field. 

# Specify the name of your endpoint
endpoint_name='<endpoint_name>'

# Create a low-level SageMaker service client.
sagemaker_client = boto3.client('sagemaker', region_name=aws_region)

# Store DescribeEndpointConfig response into a variable that we can index in the next step.
response = sagemaker_client.describe_endpoint_config(EndpointConfigName=endpoint_name)

# Delete endpoint
model_name = response['ProductionVariants'][0]['ModelName']
sagemaker_client.delete_model(ModelName=model_name)

For more information about other response elements returned by DescribeEndpointConfig, see DescribeEndpointConfig in the SageMaker API Reference guide.

AWS CLI

Use the delete-model command to delete your SageMaker AI model. Specify the name of your model for the model-name flag.

aws sagemaker delete-model \
                        --model-name <model-name>

You can optionally use the describe-endpoint-config command to return information about the name of the your deployed models (production variants) such as the name of your model and the name of the endpoint configuration associated with that deployed model. Provide the name of your endpoint for the endpoint-config-name flag.

aws sagemaker describe-endpoint-config --endpoint-config-name <endpoint-config-name>

This will return a JSON response. You can copy and paste, use a JSON parser, or use a tool built for JSON parsing to obtain the name of the model associated with that endpoint.

SageMaker AI Console

Delete your SageMaker AI model interactively with the SageMaker AI console.

  1. In the SageMaker AI console at https://console.aws.amazon.com/sagemaker/ navigation menu, choose Inference.

  2. Choose Models from the dropdown menu. A list of models created in you AWS account will appear by name, Amazon Resource Name (ARN), and creation time.

  3. Select the model you want to delete.

  4. Select the Actions dropdown button in the top right corner.

  5. Choose Delete.