Request Inferences from a Deployed Service (Boto3) - Amazon SageMaker

Request Inferences from a Deployed Service (Boto3)

You can submit inference requests using SageMaker SDK for Python (Boto3) client and invoke_endpoint() API once you have an SageMaker endpoint InService. The following code example shows how to send an image for inference:

PyTorch and MXNet
import boto3 import json endpoint = 'insert name of your endpoint here' runtime = boto3.Session().client('sagemaker-runtime') # Read image into memory with open(image, 'rb') as f: payload = f.read() # Send image via InvokeEndpoint API response = runtime.invoke_endpoint(EndpointName=endpoint, ContentType='application/x-image', Body=payload) # Unpack response result = json.loads(response['Body'].read().decode())
TensorFlow

For TensorFlow submit an input with application/json for the content type.

from PIL import Image import numpy as np import json import boto3 client = boto3.client('sagemaker-runtime') input_file = 'path/to/image' image = Image.open(input_file) batch_size = 1 image = np.asarray(image.resize((224, 224))) image = image / 128 - 1 image = np.concatenate([image[np.newaxis, :, :]] * batch_size) body = json.dumps({"instances": image.tolist()}) ioc_predictor_endpoint_name = 'insert name of your endpoint here' content_type = 'application/json' ioc_response = client.invoke_endpoint( EndpointName=ioc_predictor_endpoint_name, Body=body, ContentType=content_type )
XGBoost

For an XGBoost application, you should submit a CSV text instead:

import boto3 import json endpoint = 'insert your endpoint name here' runtime = boto3.Session().client('sagemaker-runtime') csv_text = '1,-1.0,1.0,1.5,2.6' # Send CSV text via InvokeEndpoint API response = runtime.invoke_endpoint(EndpointName=endpoint, ContentType='text/csv', Body=csv_text) # Unpack response result = json.loads(response['Body'].read().decode())

Note that BYOM allows for a custom content type. For more information, see runtime_InvokeEndpoint.