Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models
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 create_model API,
to deploy and the resources that you want Amazon SageMaker to provision.
Then you call the create_endpoint 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 create_endpoint, 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
describe_endpoint_config before
calling create_endpoint to minimize the
potential impact of a DynamoDB eventually consistent read.
sagemaker_create_endpoint_config(EndpointConfigName, ProductionVariants, DataCaptureConfig, Tags, KmsKeyId)
EndpointConfigName |
[required] The name of the endpoint configuration. You specify this name in a
|
ProductionVariants |
[required] An list of |
DataCaptureConfig |
|
Tags |
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. |
KmsKeyId |
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:
The KMS key policy must grant permission to the IAM role that you
specify in your 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 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. |
A list with the following syntax:
list( EndpointConfigArn = "string" )
svc$create_endpoint_config(
EndpointConfigName = "string",
ProductionVariants = list(
list(
VariantName = "string",
ModelName = "string",
InitialInstanceCount = 123,
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 = 123.0,
AcceleratorType = "ml.eia1.medium"|"ml.eia1.large"|"ml.eia1.xlarge"|"ml.eia2.medium"|"ml.eia2.large"|"ml.eia2.xlarge"
)
),
DataCaptureConfig = list(
EnableCapture = TRUE|FALSE,
InitialSamplingPercentage = 123,
DestinationS3Uri = "string",
KmsKeyId = "string",
CaptureOptions = list(
list(
CaptureMode = "Input"|"Output"
)
),
CaptureContentTypeHeader = list(
CsvContentTypes = list(
"string"
),
JsonContentTypes = list(
"string"
)
)
),
Tags = list(
list(
Key = "string",
Value = "string"
)
),
KmsKeyId = "string"
)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.