# Create a multi-container endpoint (Boto 3)
<a name="multi-container-create"></a>

Create a Multi-container endpoint by calling [CreateModel](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html), [CreateEndpointConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html), and [CreateEndpoint](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html) APIs as you would to create any other endpoints. You can run these containers sequentially as an inference pipeline, or run each individual container by using direct invocation. Multi-container endpoints have the following requirements when you call `create_model`:
+ Use the `Containers` parameter instead of `PrimaryContainer`, and include more than one container in the `Containers` parameter.
+ The `ContainerHostname` parameter is required for each container in a multi-container endpoint with direct invocation.
+ Set the `Mode` parameter of the `InferenceExecutionConfig` field to `Direct` for direct invocation of each container, or `Serial` to use containers as an inference pipeline. The default mode is `Serial`. 

**Note**  
Currently there is a limit of up to 15 containers supported on a multi-container endpoint.

The following example creates a multi-container model for direct invocation.

1. Create container elements and `InferenceExecutionConfig` with direct invocation.

   ```
   container1 = {
                    'Image': '123456789012.dkr.ecr.us-east-1.amazonaws.com/myimage1:mytag',
                    'ContainerHostname': 'firstContainer'
                }
   
   container2 = {
                    'Image': '123456789012.dkr.ecr.us-east-1.amazonaws.com/myimage2:mytag',
                    'ContainerHostname': 'secondContainer'
                }
   inferenceExecutionConfig = {'Mode': 'Direct'}
   ```

1. Create the model with the container elements and set the `InferenceExecutionConfig` field.

   ```
   import boto3
   sm_client = boto3.Session().client('sagemaker')
   
   response = sm_client.create_model(
                  ModelName = 'my-direct-mode-model-name',
                  InferenceExecutionConfig = inferenceExecutionConfig,
                  ExecutionRoleArn = role,
                  Containers = [container1, container2]
              )
   ```

To create an endoint, you would then call [create\$1endpoint\$1config](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_endpoint_config) and [create\$1endpoint](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_endpoint) as you would to create any other endpoint.