SageMaker

SageMaker

LocalStack Pro provides a local version of the SageMaker API, which allows running jobs to create machine learning models (e.g., using PyTorch) and to deploy them.

Model Training

A basic training example using the sagemaker.tensorflow.TensorFlow class is provided in this Github repository. Essentially, the code boils down to these core lines:

inputs = ...  # load training data files
mnist_estimator = TensorFlow(entry_point='mnist.py', role='arn:aws:...',
    framework_version='1.12.0', sagemaker_session=sagemaker_session,
    train_instance_count=1, training_steps=10, evaluation_steps=10)
mnist_estimator.fit(inputs, logs=False)

The code snippet above uploads the model training code to local S3, submits a new training job to the local SageMaker API, and finally puts the trained model back to an output S3 bucket. Please refer to the sample repo for more details.

Model Deployment and Inference

SageMaker supports the deployment and real-time inference of singular local ML models. An example for that is provided in our PRO samples repository. As explained in the ReadMe of the sample, you will need to retrieve the image with your AWS account by connecting with the provided ECR repository:

aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.us-east-1.amazonaws.com

The example also shows the two currently supported options of inference - on the container itself or through the sagemaker-runtime invocation endpoint:

def inference_model_container(run_id: str = "0"):
    ep = sagemaker.describe_endpoint(EndpointName=f"{ENDPOINT_NAME}-{run_id}")
    arn = ep["EndpointArn"]
    tag_list = sagemaker.list_tags(ResourceArn=arn)
    port = "4510"
    for tag in tag_list["Tags"]:
        if tag["Key"] == "_LS_ENDPOINT_PORT_":
            port = tag["Value"]
    inputs = _get_input_dict()
    response = httpx.post(f"http://localhost.localstack.cloud:{port}/invocations", json=inputs,
                          headers={"Content-Type": "application/json", "Accept": "application/json"})
    _show_predictions(json.loads(response.text))


def inference_model_boto3(run_id: str = "0"):
    inputs = _get_input_dict()
    response = sagemaker_runtime.invoke_endpoint(EndpointName=f"{ENDPOINT_NAME}-{run_id}", Body=json.dumps(inputs),
                                                 Accept="application/json",
                                                 ContentType="application/json")
    _show_predictions(json.loads(response["Body"].read()))
Last modified October 7, 2022: add SageMaker inference docs (#289) (a6d40971)