Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
|
import io
|
||
|
|
import json
|
||
|
|
import numpy as np
|
||
|
|
from PIL import Image
|
||
|
|
|
||
|
|
|
||
|
|
def input_handler(data, context):
|
||
|
|
""" Pre-process request input before it is sent to TensorFlow Serving REST API
|
||
|
|
https://github.com/aws/amazon-sagemaker-examples/blob/0e57a288f54910a50dcbe3dfe2acb8d62e3b3409/sagemaker-python-sdk/tensorflow_serving_container/sample_utils.py#L61
|
||
|
|
|
||
|
|
Args:
|
||
|
|
data (obj): the request data stream
|
||
|
|
context (Context): an object containing request and configuration details
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
(dict): a JSON-serializable dict that contains request body and headers
|
||
|
|
"""
|
||
|
|
|
||
|
|
if context.request_content_type == 'application/x-image':
|
||
|
|
buf = np.fromstring(data.read(), np.uint8)
|
||
|
|
image = Image.open(io.BytesIO(buf)).resize((224, 224))
|
||
|
|
image = np.array(image)
|
||
|
|
image = np.expand_dims(image, axis=0)
|
||
|
|
return json.dumps({"instances": image.tolist()})
|
||
|
|
else:
|
||
|
|
_return_error(415, 'Unsupported content type "{}"'.format(
|
||
|
|
context.request_content_type or 'Unknown'))
|
||
|
|
|
||
|
|
|
||
|
|
def output_handler(response, context):
|
||
|
|
"""Post-process TensorFlow Serving output before it is returned to the client.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
response (obj): the TensorFlow serving response
|
||
|
|
context (Context): an object containing request and configuration details
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
(bytes, string): data to return to client, response content type
|
||
|
|
"""
|
||
|
|
if response.status_code != 200:
|
||
|
|
_return_error(response.status_code, response.content.decode('utf-8'))
|
||
|
|
response_content_type = context.accept_header
|
||
|
|
prediction = response.content
|
||
|
|
return prediction, response_content_type
|
||
|
|
|
||
|
|
|
||
|
|
def _return_error(code, message):
|
||
|
|
raise ValueError('Error: {}, {}'.format(str(code), message))
|