Create a model - Amazon SageMaker
To create a model (using Model Registry)To create a model (using API)To create a model (using the console)

Create a model

To create your model, you must provide the location of your model artifacts and container image. You can also use a model version from SageMaker Model Registry. The examples in the following sections show you how to create a model using the CreateModel API, Model Registry, and the Amazon SageMaker console.

To create a model (using Model Registry)

Model Registry is a feature of SageMaker that helps you catalog and manage versions of your model for use in ML pipelines. To use Model Registry with Serverless Inference, you must first register a model version in a Model Registry model group. To learn how to register a model in Model Registry, follow the procedures in Create a Model Group and Register a Model Version.

The following example requires you to have the ARN of a registered model version and uses the AWS SDK for Python (Boto3) to call the CreateModel API. For Serverless Inference, Model Registry is currently only supported by the AWS SDK for Python (Boto3). For the example, specify the following values:

  • For model_name, enter a name for the model.

  • For sagemaker_role, you can use the default SageMaker-created role or a customized SageMaker IAM role from Step 4 of the Complete the prerequisites section.

  • For ModelPackageName, specify the ARN for your model version, which must be registered to a model group in Model Registry.

#Setup
import boto3
import sagemaker
region = boto3.Session().region_name
client = boto3.client("sagemaker", region_name=region)

#Role to give SageMaker permission to access AWS services.
sagemaker_role = sagemaker.get_execution_role()

#Specify a name for the model
model_name = "<name-for-model>"

#Specify a Model Registry model version
container_list = [
    {
        "ModelPackageName": <model-version-arn>
     }
 ]

#Create the model
response = client.create_model(
    ModelName = model_name,
    ExecutionRoleArn = sagemaker_role,
    container_list
)

To create a model (using API)

The following example uses the AWS SDK for Python (Boto3) to call the CreateModel API. Specify the following values:

  • For sagemaker_role, you can use the default SageMaker-created role or a customized SageMaker IAM role from Step 4 of the Complete the prerequisites section.

  • For model_url, specify the Amazon S3 URI to your model.

  • For container, retrieve the container you want to use by its Amazon ECR path. This example uses a SageMaker-provided XGBoost container. If you have not selected a SageMaker container or brought your own, see Step 6 of the Complete the prerequisites section for more information.

  • For model_name, enter a name for the model.

#Setup
import boto3
import sagemaker
region = boto3.Session().region_name
client = boto3.client("sagemaker", region_name=region)

#Role to give SageMaker permission to access AWS services.
sagemaker_role = sagemaker.get_execution_role()

#Get model from S3
model_url = "s3://amzn-s3-demo-bucket/models/model.tar.gz"

#Get container image (prebuilt example)
from sagemaker import image_uris
container = image_uris.retrieve("xgboost", region, "0.90-1")

#Create model
model_name = "<name-for-model>"

response = client.create_model(
    ModelName = model_name,
    ExecutionRoleArn = sagemaker_role,
    Containers = [{
        "Image": container,
        "Mode": "SingleModel",
        "ModelDataUrl": model_url,
    }]
)

To create a model (using the console)

  1. Sign in to the Amazon SageMaker console.

  2. In the navigation tab, choose Inference.

  3. Next, choose Models.

  4. Choose Create model.

  5. For Model name, enter a name for the model that is unique to your account and AWS Region.

  6. For IAM role, either select an IAM role you have already created (see Complete the prerequisites) or allow SageMaker to create one for you.

  7. In Container definition 1, for Container input options, select Provide model artifacts and input location.

  8. For Provide model artifacts and inference image options, select Use a single model.

  9. For Location of inference code image, enter an Amazon ECR path to a container. The image must either be a SageMaker-provided first party image (e.g. TensorFlow, XGBoost) or an image that resides in an Amazon ECR repository within the same account in which you are creating the endpoint. If you do not have a container, go back to Step 6 of the Complete the prerequisites section for more information.

  10. For Location of model artifacts, enter the Amazon S3 URI to your ML model. For example, s3://amzn-s3-demo-bucket/models/model.tar.gz.

  11. (Optional) For Tags, add key-value pairs to create metadata for your model.

  12. Choose Create model.