# Deploy a Compiled Model Using Boto3
<a name="neo-deployment-hosting-services-boto3"></a>

You must satisfy the [ prerequisites](https://docs.aws.amazon.com//sagemaker/latest/dg/neo-deployment-hosting-services-prerequisites) section if the model was compiled using AWS SDK for Python (Boto3), AWS CLI, or the Amazon SageMaker AI console. Follow the steps below to create and deploy a SageMaker Neo-compiled model using [Amazon Web Services SDK for Python (Boto3)](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html). 

**Topics**
+ [Deploy the Model](#neo-deployment-hosting-services-boto3-steps)

## Deploy the Model
<a name="neo-deployment-hosting-services-boto3-steps"></a>

After you have satisfied the [ prerequisites](https://docs.aws.amazon.com//sagemaker/latest/dg/neo-deployment-hosting-services-prerequisites), use the `create_model`, `create_enpoint_config`, and `create_endpoint` APIs. 

The following example shows how to use these APIs to deploy a model compiled with Neo: 

```
import boto3
client = boto3.client('sagemaker')

# create sagemaker model
create_model_api_response = client.create_model(
                                    ModelName='my-sagemaker-model',
                                    PrimaryContainer={
                                        'Image': <insert the ECR Image URI>,
                                        'ModelDataUrl': 's3://path/to/model/artifact/model.tar.gz',
                                        'Environment': {}
                                    },
                                    ExecutionRoleArn='ARN for AmazonSageMaker-ExecutionRole'
                            )

print ("create_model API response", create_model_api_response)

# create sagemaker endpoint config
create_endpoint_config_api_response = client.create_endpoint_config(
                                            EndpointConfigName='sagemaker-neomxnet-endpoint-configuration',
                                            ProductionVariants=[
                                                {
                                                    'VariantName': <provide your variant name>,
                                                    'ModelName': 'my-sagemaker-model',
                                                    'InitialInstanceCount': 1,
                                                    'InstanceType': <provide your instance type here>
                                                },
                                            ]
                                       )

print ("create_endpoint_config API response", create_endpoint_config_api_response)

# create sagemaker endpoint
create_endpoint_api_response = client.create_endpoint(
                                    EndpointName='provide your endpoint name',
                                    EndpointConfigName=<insert your endpoint config name>,
                                )

print ("create_endpoint API response", create_endpoint_api_response)
```

**Note**  
The `AmazonSageMakerFullAccess` and `AmazonS3ReadOnlyAccess` policies must be attached to the `AmazonSageMaker-ExecutionRole` IAM role. 

For full syntax of `create_model`, `create_endpoint_config`, and `create_endpoint` APIs, see [https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_model](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_model), [https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_endpoint_config](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_endpoint_config), and [https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_endpoint](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_endpoint), respectively. 

If you did not train your model using SageMaker AI, specify the following environment variables: 

------
#### [ MXNet and PyTorch ]

```
"Environment": {
    "SAGEMAKER_PROGRAM": "inference.py",
    "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code",
    "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
    "SAGEMAKER_REGION": "insert your region",
    "MMS_DEFAULT_RESPONSE_TIMEOUT": "500"
}
```

------
#### [ TensorFlow ]

```
"Environment": {
    "SAGEMAKER_PROGRAM": "inference.py",
    "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code",
    "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
    "SAGEMAKER_REGION": "insert your region"
}
```

------

 If you trained your model using SageMaker AI, specify the environment variable `SAGEMAKER_SUBMIT_DIRECTORY` as the full Amazon S3 bucket URI that contains the training script.