Create a Multi-Model Endpoint - Amazon SageMaker AI (original) (raw)

You can use the SageMaker AI console or the AWS SDK for Python (Boto) to create a multi-model endpoint. To create either a CPU or GPU backed endpoint through the console, see the console procedure in the following sections. If you want to create a multi-model endpoint with the AWS SDK for Python (Boto), use either the CPU or GPU procedure in the following sections. The CPU and GPU workflows are similar but have several differences, such as the container requirements.

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Create a multi-model endpoint (console)

You can create both CPU and GPU backed multi-model endpoints through the console. Use the following procedure to create a multi-model endpoint through the SageMaker AI console.

To create a multi-model endpoint (console)
  1. Open the Amazon SageMaker AI console at https://console.aws.amazon.com/sagemaker/.
  2. Choose Model, and then from the Inference group, choose Create model.
  3. For Model name, enter a name.
  4. For IAM role, choose or create an IAM role that has theAmazonSageMakerFullAccess IAM policy attached.
  5. In the Container definition section, for Provide model artifacts and inference image options, choose Use multiple models.
    The section of the Create model page where you can choose Use multiple models.
  6. For the Inference container image, enter the Amazon ECR path for your desired container image.
    For GPU models, you must use a container backed by the NVIDIA Triton Inference Server. For a list of container images that work with GPU backed endpoints, see theNVIDIA Triton Inference Containers (SM support only). For more information about the NVIDIA Triton Inference Server, see Use Triton Inference Server with SageMaker AI.
  7. Choose Create model.
  8. Deploy your multi-model endpoint as you would a single model endpoint. For instructions, see Deploy the Model to SageMaker AI Hosting Services.

Create a multi-model endpoint using CPUs with the AWS SDK for Python (Boto3)

Use the following section to create a multi-model endpoint backed by CPU instances. You create a multi-model endpoint using the Amazon SageMaker AI create_model, create_endpoint_config, and create_endpoint APIs just as you would create a single model endpoint, but with two changes. When defining the model container, you need to pass a newMode parameter value, MultiModel. You also need to pass theModelDataUrl field that specifies the prefix in Amazon S3 where the model artifacts are located, instead of the path to a single model artifact, as you would when deploying a single model.

For a sample notebook that uses SageMaker AI to deploy multiple XGBoost models to an endpoint, see Multi-Model Endpoint XGBoost Sample Notebook.

The following procedure outlines the key steps used in that sample to create a CPU backed multi-model endpoint.

To deploy the model (AWS SDK for Python (Boto 3))
  1. Get a container with an image that supports deploying multi-model endpoints. For a list of built-in algorithms and framework containers that support multi-model endpoints, see Supported algorithms, frameworks, and instances for multi-model endpoints. For this example, we use the K-Nearest Neighbors (k-NN) Algorithm built-in algorithm. We call the SageMaker Python SDK utility function image_uris.retrieve() to get the address for the K-Nearest Neighbors built-in algorithm image.
import sagemaker  
region = sagemaker_session.boto_region_name  
image = sagemaker.image_uris.retrieve("knn",region=region)  
container = {  
              'Image':        image,  
              'ModelDataUrl': 's3://<BUCKET_NAME>/<PATH_TO_ARTIFACTS>',  
              'Mode':         'MultiModel'  
            }
  1. Get an AWS SDK for Python (Boto3) SageMaker AI client and create the model that uses this container.
import boto3  
sagemaker_client = boto3.client('sagemaker')  
response = sagemaker_client.create_model(  
              ModelName        = '<MODEL_NAME>',  
              ExecutionRoleArn = role,  
              Containers       = [container])
  1. (Optional) If you are using a serial inference pipeline, get the additional container(s) to include in the pipeline, and include it in the Containers argument of CreateModel:
preprocessor_container = {  
               'Image': '<ACCOUNT_ID>.dkr.ecr.<REGION_NAME>.amazonaws.com/<PREPROCESSOR_IMAGE>:<TAG>'  
            }  
multi_model_container = {  
              'Image': '<ACCOUNT_ID>.dkr.ecr.<REGION_NAME>.amazonaws.com/<IMAGE>:<TAG>',  
              'ModelDataUrl': 's3://<BUCKET_NAME>/<PATH_TO_ARTIFACTS>',  
              'Mode':         'MultiModel'  
            }  
response = sagemaker_client.create_model(  
              ModelName        = '<MODEL_NAME>',  
              ExecutionRoleArn = role,  
              Containers       = [preprocessor_container, multi_model_container]  
            )
Note

You can use only one multi-model-enabled endpoint in a serial inference pipeline. 4. (Optional) If your use case does not benefit from model caching, set the value of the ModelCacheSetting field of the MultiModelConfig parameter to Disabled, and include it in the Container argument of the call to create_model. The value of the ModelCacheSetting field is Enabled by default. 

container = {  
                'Image': image,  
                'ModelDataUrl': 's3://<BUCKET_NAME>/<PATH_TO_ARTIFACTS>',  
                'Mode': 'MultiModel'  
                'MultiModelConfig': {  
                        // Default value is 'Enabled'  
                        'ModelCacheSetting': 'Disabled'  
                }  
           }  
response = sagemaker_client.create_model(  
              ModelName        = '<MODEL_NAME>',  
              ExecutionRoleArn = role,  
              Containers       = [container]  
            )
  1. Configure the multi-model endpoint for the model. We recommend configuring your endpoints with at least two instances. This allows SageMaker AI to provide a highly available set of predictions across multiple Availability Zones for the models.
response = sagemaker_client.create_endpoint_config(  
                EndpointConfigName = '<ENDPOINT_CONFIG_NAME>',  
                ProductionVariants=[  
                     {  
                        'InstanceType':        'ml.m4.xlarge',  
                        'InitialInstanceCount': 2,  
                        'InitialVariantWeight': 1,  
                        'ModelName':            '<MODEL_NAME>',  
                        'VariantName':          'AllTraffic'  
                      }  
                ]  
           )
Note

You can use only one multi-model-enabled endpoint in a serial inference pipeline. 6. Create the multi-model endpoint using the EndpointName andEndpointConfigName parameters. 

response = sagemaker_client.create_endpoint(  
              EndpointName       = '<ENDPOINT_NAME>',  
              EndpointConfigName = '<ENDPOINT_CONFIG_NAME>')

Create a multi-model endpoint using GPUs with the AWS SDK for Python (Boto3)

Use the following section to create a GPU backed multi-model endpoint. You create a multi-model endpoint using the Amazon SageMaker AI create_model, create_endpoint_config, and create_endpoint APIs similarly to creating single model endpoints, but there are several changes. When defining the model container, you need to pass a newMode parameter value, MultiModel. You also need to pass theModelDataUrl field that specifies the prefix in Amazon S3 where the model artifacts are located, instead of the path to a single model artifact, as you would when deploying a single model. For GPU backed multi-model endpoints, you also must use a container with the NVIDIA Triton Inference Server that is optimized for running on GPU instances. For a list of container images that work with GPU backed endpoints, see theNVIDIA Triton Inference Containers (SM support only).

For an example notebook that demonstrates how to create a multi-model endpoint backed by GPUs, see Run mulitple deep learning models on GPUs with Amazon SageMaker AI Multi-model endpoints (MME).

The following procedure outlines the key steps to create a GPU backed multi-model endpoint.

To deploy the model (AWS SDK for Python (Boto 3))
  1. Define the container image. To create a multi-model endpoint with GPU support for ResNet models, define the container to use the NVIDIA Triton Server image. This container supports multi-model endpoints and is optimized for running on GPU instances. We call the SageMaker AI Python SDK utility function image_uris.retrieve() to get the address for the image. For example:
import sagemaker  
region = sagemaker_session.boto_region_name  
// Find the sagemaker-tritonserver image at  
// https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-triton/resnet50/triton_resnet50.ipynb  
// Find available tags at https://github.com/aws/deep-learning-containers/blob/master/available_images.md#nvidia-triton-inference-containers-sm-support-only  
image = "<ACCOUNT_ID>.dkr.ecr.<REGION_NAME>.amazonaws.com/sagemaker-tritonserver:<TAG>".format(  
    account_id=account_id_map[region], region=region  
)  
container = {  
              'Image':        image,  
              'ModelDataUrl': 's3://<BUCKET_NAME>/<PATH_TO_ARTIFACTS>',  
              'Mode':         'MultiModel',  
              "Environment": {"SAGEMAKER_TRITON_DEFAULT_MODEL_NAME": "resnet"},  
            }
  1. Get an AWS SDK for Python (Boto3) SageMaker AI client and create the model that uses this container.
import boto3  
sagemaker_client = boto3.client('sagemaker')  
response = sagemaker_client.create_model(  
              ModelName        = '<MODEL_NAME>',  
              ExecutionRoleArn = role,  
              Containers       = [container])
  1. (Optional) If you are using a serial inference pipeline, get the additional container(s) to include in the pipeline, and include it in the Containers argument of CreateModel:
preprocessor_container = {  
               'Image': '<ACCOUNT_ID>.dkr.ecr.<REGION_NAME>.amazonaws.com/<PREPROCESSOR_IMAGE>:<TAG>'  
            }  
multi_model_container = {  
              'Image': '<ACCOUNT_ID>.dkr.ecr.<REGION_NAME>.amazonaws.com/<IMAGE>:<TAG>',  
              'ModelDataUrl': 's3://<BUCKET_NAME>/<PATH_TO_ARTIFACTS>',  
              'Mode':         'MultiModel'  
            }  
response = sagemaker_client.create_model(  
              ModelName        = '<MODEL_NAME>',  
              ExecutionRoleArn = role,  
              Containers       = [preprocessor_container, multi_model_container]  
            )
Note

You can use only one multi-model-enabled endpoint in a serial inference pipeline. 4. (Optional) If your use case does not benefit from model caching, set the value of the ModelCacheSetting field of the MultiModelConfig parameter to Disabled, and include it in the Container argument of the call to create_model. The value of the ModelCacheSetting field is Enabled by default. 

container = {  
                'Image': image,  
                'ModelDataUrl': 's3://<BUCKET_NAME>/<PATH_TO_ARTIFACTS>',  
                'Mode': 'MultiModel'  
                'MultiModelConfig': {  
                        // Default value is 'Enabled'  
                        'ModelCacheSetting': 'Disabled'  
                }  
           }  
response = sagemaker_client.create_model(  
              ModelName        = '<MODEL_NAME>',  
              ExecutionRoleArn = role,  
              Containers       = [container]  
            )
  1. Configure the multi-model endpoint with GPU backed instances for the model. We recommend configuring your endpoints with more than one instance to allow for high availability and higher cache hits.
response = sagemaker_client.create_endpoint_config(  
                EndpointConfigName = '<ENDPOINT_CONFIG_NAME>',  
                ProductionVariants=[  
                     {  
                        'InstanceType':        'ml.g4dn.4xlarge',  
                        'InitialInstanceCount': 2,  
                        'InitialVariantWeight': 1,  
                        'ModelName':            '<MODEL_NAME>',  
                        'VariantName':          'AllTraffic'  
                      }  
                ]  
           )
  1. Create the multi-model endpoint using the EndpointName andEndpointConfigName parameters.
response = sagemaker_client.create_endpoint(  
              EndpointName       = '<ENDPOINT_NAME>',  
              EndpointConfigName = '<ENDPOINT_CONFIG_NAME>')