Create a Model

The following example shows how to create a model using the AWS SDK for Python (Boto3). The first few lines define:

import boto3

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

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

# Role to give SageMaker permission to access AWS services.
sagemaker_role= "arn:aws:iam::<account>:role/*"

Next, specify the location of the pre-trained model stored in Amazon S3. In this example, we use a pre-trained XGBoost model named demo-xgboost-model.tar.gz. The full Amazon S3 URI is stored in a string variable model_url:

#Create a variable w/ the model S3 URI
s3_bucket = '<your-bucket-name>' # Provide the name of your S3 bucket
bucket_prefix='saved_models'
model_s3_key = f"{bucket_prefix}/demo-xgboost-model.tar.gz"

#Specify S3 bucket w/ model
model_url = f"s3://{s3_bucket}/{model_s3_key}"

Specify 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.

In this example, we specify an XGBoost built-in algorithm container image:

from sagemaker import image_uris

# Specify an AWS container image. 
container = image_uris.retrieve(region=aws_region, framework='xgboost', version='0.90-1')

Create a model in Amazon SageMaker with CreateModel. Specify the following:

model_name = '<The_name_of_the_model>'

#Create model
create_model_response = sagemaker_client.create_model(
    ModelName = model_name,
    ExecutionRoleArn = sagemaker_role,
    PrimaryContainer = {
        'Image': container,
        'ModelDataUrl': model_url,
    })

See CreateModel description in the SageMaker API Reference Guide for a full list of API parameters.

If you're using a SageMaker provided container, you can increase the model server timeout and payload sizes from the default values to the framework‐supported maximums by setting environment variables in this step. You might not be able to leverage the maximum timeout and payload sizes that Asynchronous Inference supports if you don't explicitly set these variables. The following example shows how you can set the environment variables for a PyTorch Inference container based on TorchServe.

model_name = '<The_name_of_the_model>'

#Create model
create_model_response = sagemaker_client.create_model(
    ModelName = model_name,
    ExecutionRoleArn = sagemaker_role,
    PrimaryContainer = {
        'Image': container,
        'ModelDataUrl': model_url,
        'Environment': {
            'TS_MAX_REQUEST_SIZE': '100000000',
            'TS_MAX_RESPONSE_SIZE': '100000000',
            'TS_DEFAULT_RESPONSE_TIMEOUT': '1000'
        },
    })

After you finish creating your endpoint, you should test that you've set the environment variables correctly by printing them out from your inference.py script. The following table lists the environment variables for several frameworks that you can set to change the default values.