Deploy a Compiled Model Using SageMaker SDK

You must satisfy the prerequisites section if the model was compiled using AWS SDK for Python (Boto3), AWS CLI, or the Amazon SageMaker AI console. Follow one of the following use cases to deploy a model compiled with SageMaker Neo based on how you compiled your model.

If you compiled your model using the SageMaker SDK

The sagemaker.Model object handle for the compiled model supplies the deploy() function, which enables you to create an endpoint to serve inference requests. The function lets you set the number and type of instances that are used for the endpoint. You must choose an instance for which you have compiled your model. For example, in the job compiled in Compile a Model (Amazon SageMaker SDK) section, this is ml_c5.

from sagemaker.serve import ModelBuilder

model_builder = ModelBuilder(
    model=compiled_model,
    role_arn=role,
    instance_type='ml.c5.4xlarge',
)
endpoint = model_builder.build().deploy(
    initial_instance_count=1,
    instance_type='ml.c5.4xlarge'
)

# Print the name of newly created endpoint
print(endpoint.endpoint_name)

If you compiled your model using MXNet or PyTorch

Create and deploy the SageMaker AI model using ModelBuilder. Specify the s3_model_data_url parameter as the S3 path to your compiled model archive, the role_arn parameter as your execution role, and the instance_type parameter for your target instance. You must also set the MMS_DEFAULT_RESPONSE_TIMEOUT environment variable to 500.

The following example shows how to use these functions to deploy a compiled model using the SageMaker AI SDK for Python:

from sagemaker.serve import ModelBuilder

# V3 uses a unified ModelBuilder approach for all frameworks
model_builder = ModelBuilder(
    s3_model_data_url='insert S3 path of compiled model archive',
    role_arn='AmazonSageMaker-ExecutionRole',
    instance_type='ml.p3.2xlarge',
    env={'MMS_DEFAULT_RESPONSE_TIMEOUT': '500'},
)
endpoint = model_builder.build().deploy(
    initial_instance_count=1,
    instance_type='ml.p3.2xlarge'
)

# Print the name of newly created endpoint
print(endpoint.endpoint_name)

If you compiled your model using Boto3, SageMaker console, or the CLI for TensorFlow

Construct a model object, then call deploy:

from sagemaker.serve import ModelBuilder

model_builder = ModelBuilder(
    s3_model_data_url='S3 path for model file',
    role_arn=role,
    instance_type='ml.c5.xlarge',
)
endpoint = model_builder.build().deploy(
    initial_instance_count=1,
    instance_type='ml.c5.xlarge'
)

See Deploying directly from model artifacts for more information.

You can select a Docker image Amazon ECR URI that meets your needs from this list.

For more information on how to construct a TensorFlowModel object, see the SageMaker SDK.

The following code snippet demonstrates how to produce the warm-up file for image classification example in the prerequisites section:

import tensorflow as tf
from tensorflow_serving.apis import classification_pb2
from tensorflow_serving.apis import inference_pb2
from tensorflow_serving.apis import model_pb2
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_log_pb2
from tensorflow_serving.apis import regression_pb2
import numpy as np

with tf.python_io.TFRecordWriter("tf_serving_warmup_requests") as writer:       
    img = np.random.uniform(0, 1, size=[224, 224, 3]).astype(np.float32)
    img = np.expand_dims(img, axis=0)
    test_data = np.repeat(img, 1, axis=0)
    request = predict_pb2.PredictRequest()
    request.model_spec.name = 'compiled_models'
    request.model_spec.signature_name = 'serving_default'
    request.inputs['Placeholder:0'].CopyFrom(tf.compat.v1.make_tensor_proto(test_data, shape=test_data.shape, dtype=tf.float32))
    log = prediction_log_pb2.PredictionLog(
    predict_log=prediction_log_pb2.PredictLog(request=request))
    writer.write(log.SerializeToString())

For more information on how to “warm up” your model, see the TensorFlow TFX page.