# Use a Model Package to Create a Model
<a name="sagemaker-mkt-model-pkg-model"></a>

Use a model package to create a deployable model that you can use to get real-time inferences by creating a hosted endpoint or to run batch transform jobs. You can create a deployable model from a model package by using the Amazon SageMaker AI console, the low-level SageMaker API), or the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable).

**Topics**
+ [Use a Model Package to Create a Model (Console)](#sagemaker-mkt-model-pkg-model-console)
+ [Use a Model Package to Create a Model (API)](#sagemaker-mkt-model-pkg-model-api)
+ [Use a Model Package to Create a Model ([Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable))](#sagemaker-mkt-model-pkg-model-sdk)

## Use a Model Package to Create a Model (Console)
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**To create a deployable model from a model package (console)**

1. Open the SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).

1. Choose **Model packages**.

1. Choose a model package that you created from the list on the **My model packages** tab or choose a model package that you subscribed to on the **AWS Marketplace subscriptions** tab.

1. Choose **Create model**.

1. For **Model name**, type a name for the model.

1. For **IAM role**, choose an IAM role that has the required permissions to call other services on your behalf, or choose **Create a new role** to allow SageMaker AI to create a role that has the `AmazonSageMakerFullAccess` managed policy attached. For information, see [How to use SageMaker AI execution roles](sagemaker-roles.md).

1. For **VPC**, choose a Amazon VPC that you want to allow the model to access. For more information, see [Give SageMaker AI Hosted Endpoints Access to Resources in Your Amazon VPC](host-vpc.md).

1. Leave the default values for **Container input options** and **Choose model package**.

1. For environment variables, provide the names and values of environment variables you want to pass to the model container.

1. For **Tags**, specify one or more tags to manage the model. Each tag consists of a key and an optional value. Tag keys must be unique per resource.

1. Choose **Create model**.

After you create a deployable model, you can use it to set up an endpoint for real-time inference or create a batch transform job to get inferences on entire datasets. For information about hosting endpoints in SageMaker AI, see [Deploy Models for Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-model.html).

## Use a Model Package to Create a Model (API)
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To use a model package to create a deployable model by using the SageMaker API, specify the name or the Amazon Resource Name (ARN) of the model package as the `ModelPackageName` field of the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html) object that you pass to the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html) API.

After you create a deployable model, you can use it to set up an endpoint for real-time inference or create a batch transform job to get inferences on entire datasets. For information about hosted endpoints in SageMaker AI, see [Deploy Models for Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-model.html).

## Use a Model Package to Create a Model ([Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable))
<a name="sagemaker-mkt-model-pkg-model-sdk"></a>

To use a model package to create a deployable model by using the SageMaker AI Python SDK, initialize a `ModelPackage` object, and pass the Amazon Resource Name (ARN) of the model package as the `model_package_arn` argument. For example:

```
from sagemaker import ModelPackage
model = ModelPackage(role='SageMakerRole',
         model_package_arn='training-job-scikit-decision-trees-1542660466-6f92',
         sagemaker_session=sagemaker_session)
```

After you create a deployable model, you can use it to set up an endpoint for real-time inference or create a batch transform job to get inferences on entire datasets. For information about hosting endpoints in SageMaker AI, see [Deploy Models for Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-model.html).