# Compile a Model (Amazon SageMaker AI SDK)
<a name="neo-job-compilation-sagemaker-sdk"></a>

 You can use the [https://sagemaker.readthedocs.io/en/stable/api/sagemaker_train.html](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_train.html) API in the [Amazon SageMaker AI SDK for Python](https://sagemaker.readthedocs.io/en/stable/) to compile a trained model and optimize it for specific target hardware. The API should be invoked on the estimator object used during model training. 

**Note**  
You must set `MMS_DEFAULT_RESPONSE_TIMEOUT` environment variable to `500` when compiling the model with MXNet or PyTorch. The environment variable is not needed for TensorFlow. 

 The following is an example of how you can compile a model using the `trained_model_estimator` object: 

------
#### [ SageMaker Python SDK v3 ]

```
# Neo compilation in V3 uses ModelBuilder.optimize() with compilation_config
from sagemaker.serve import ModelBuilder

# Replace the value of expected_trained_model_input below and
# specify the name & shape of the expected inputs for your trained model
# in json dictionary form
expected_trained_model_input = {'data':[1, 784]}

# Replace the example target_instance_family below to your preferred target_instance_family
model_builder = ModelBuilder(
    model=trained_model,
    role_arn=role,
    instance_type='ml.c5.xlarge',
)
optimized_model = model_builder.optimize(
    instance_type='ml.c5.xlarge',
    output_path='insert s3 output path',
    compilation_config={
        'OverrideEnvironment': {
            'OPTION_TARGET_INSTANCE_FAMILY': 'ml_c5',
            'MMS_DEFAULT_RESPONSE_TIMEOUT': '500',
        },
    },
)
```

------
#### [ SageMaker Python SDK v2 (Legacy) ]

```
# Replace the value of expected_trained_model_input below and
# specify the name & shape of the expected inputs for your trained model
# in json dictionary form
expected_trained_model_input = {'data':[1, 784]}

# Replace the example target_instance_family below to your preferred target_instance_family
compiled_model = trained_model_estimator.compile_model(target_instance_family='ml_c5',
        input_shape=expected_trained_model_input,
        output_path='insert s3 output path',
        env={'MMS_DEFAULT_RESPONSE_TIMEOUT': '500'})
```

------

The code compiles the model, saves the optimized model at `output_path`, and creates a SageMaker AI model that can be deployed to an endpoint.