Creates a model in Amazon SageMaker
Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the
create_endpoint_config API, and
then create an endpoint with the
create_endpoint API. Amazon SageMaker
then deploys all of the containers that you defined for the model in the
hosting environment.
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)).
To run a batch transform using your model, you start a job with the
create_transform_job API. Amazon
SageMaker uses your model and your dataset to get inferences which are
then saved to a specified S3 location.
In the create_model request, you must define
a container with the PrimaryContainer parameter.
In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.
sagemaker_create_model(ModelName, PrimaryContainer, Containers, ExecutionRoleArn, Tags, VpcConfig, EnableNetworkIsolation)
ModelName |
[required] The name of the new model. |
PrimaryContainer |
The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions. |
Containers |
Specifies the containers in the inference pipeline. |
ExecutionRoleArn |
[required] The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles. To be able to pass this role to Amazon SageMaker, the caller of this API
must have the |
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. |
VpcConfig |
A VpcConfig object that specifies the VPC that you want your model to
connect to. Control access to and from your model container by
configuring the VPC. |
EnableNetworkIsolation |
Isolates the model container. No inbound or outbound network calls can be made to or from the model container. |
A list with the following syntax:
list( ModelArn = "string" )
svc$create_model(
ModelName = "string",
PrimaryContainer = list(
ContainerHostname = "string",
Image = "string",
ImageConfig = list(
RepositoryAccessMode = "Platform"|"Vpc"
),
Mode = "SingleModel"|"MultiModel",
ModelDataUrl = "string",
Environment = list(
"string"
),
ModelPackageName = "string"
),
Containers = list(
list(
ContainerHostname = "string",
Image = "string",
ImageConfig = list(
RepositoryAccessMode = "Platform"|"Vpc"
),
Mode = "SingleModel"|"MultiModel",
ModelDataUrl = "string",
Environment = list(
"string"
),
ModelPackageName = "string"
)
),
ExecutionRoleArn = "string",
Tags = list(
list(
Key = "string",
Value = "string"
)
),
VpcConfig = list(
SecurityGroupIds = list(
"string"
),
Subnets = list(
"string"
)
),
EnableNetworkIsolation = TRUE|FALSE
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