如何使用Lambda函数将numpy数组发送到sagemaker端点 [英] How to send numpy array to sagemaker endpoint using lambda function

问题描述

如何使用输入数据类型为 numpy.ndarray 的sagemaker端点进行调用.我已经部署了一个Sagemaker模型，并尝试使用lambda函数来实现它.但是我不知道该怎么做.我收到服务器错误消息.

How to invoke sagemaker endpoint with input data type numpy.ndarray. I have deployed a sagemaker model and trying to hit it using lambda function. But I am unable to figure out how to do it. I am getting server error.

一行输入数据.总数据集具有 shape =(91,5,12).以下只是输入数据的一行.

One row of the Input data. The total data set has shape=(91,5,12). The below is only one row of Input data.

array([[[0.30440741, 0.30209799, 0.33520652, 0.41558442, 0.69096432,
         0.69611016, 0.25153326, 0.98333333, 0.82352941, 0.77187154,
         0.7664042 , 0.74468085],
        [0.30894981, 0.33151662, 0.22907725, 0.46753247, 0.69437367,
         0.70410559, 0.29259044, 0.9       , 0.80882353, 0.79401993,
         0.89501312, 0.86997636],
        [0.33511896, 0.34338939, 0.24065546, 0.48051948, 0.70384005,
         0.71058715, 0.31031288, 0.86666667, 0.89705882, 0.82724252,
         0.92650919, 0.89125296],
        [0.34617355, 0.36150251, 0.23726854, 0.54545455, 0.71368726,
         0.71703244, 0.30228356, 0.85      , 0.86764706, 0.86157254,
         0.97112861, 0.94089835],
        [0.36269508, 0.35923332, 0.40285461, 0.62337662, 0.73325475,
         0.7274392 , 0.26241391, 0.85      , 0.82352941, 0.89922481,
         0.9343832 , 0.90780142]]])

我正在使用以下代码，但无法调用端点

I am using the following code but unable to invoke the endpoint

import boto3
def lambda_handler(event, context):
    # The SageMaker runtime is what allows us to invoke the endpoint that we've created.
    runtime = boto3.Session().client('sagemaker-runtime')

    endpoint = 'sagemaker-tensorflow-2019-04-22-07-16-51-717'

    print('givendata ', event['body'])
    # data = numpy.array([numpy.array(xi) for xi in event['body']])
    data = event['body']
    print('numpy array ', data)

    # Now we use the SageMaker runtime to invoke our endpoint, sending the review we were given
    response = runtime.invoke_endpoint(EndpointName = endpoint,# The name of the endpoint we created
                                       ContentType = 'application/json',                 # The data format that is expected
                                       Body = data) # The actual review

    # The response is an HTTP response whose body contains the result of our inference
    result = response['Body'].read().decode('utf-8')

    print('response', result)

    # Round the result so that our web app only gets '1' or '0' as a response.
    result = round(float(result))

    return {
        'statusCode' : 200,
        'headers' : { 'Content-Type' : 'text/plain', 'Access-Control-Allow-Origin' : '*' },
        'body' : str(result)
    }

我无法弄清楚应该用什么代替ContentType.因为在 numpy.ndarray 的情况下，我不知道MIME类型.

I am unable to figure out what should be written in place of ContentType. Because I am not aware of MIME type in case of numpy.ndarray.

推荐答案

我所拥有的食物以及如何解决的例证

从sagemaker.tensorflow导入

Illustration of what I had and how I solved

from sagemaker.tensorflow import TensorFlowPredictor

predictor = TensorFlowPredictor('sagemaker-tensorflow-serving-date')
data = np.array(raw_data)
response = predictor.predict(data=data)
predictions = response['predictions']
print(predictions)

我为找到答案所做的事情:

What I did to find the answer:

• 在sagemaker python库中查找了projections.py和content_types.py实现，以查看其使用的内容类型和参数.
• 首先，我认为使用了 application/x-npy content_type，因此尝试使用来自预报器.py的序列化代码并将 application/x-npy 作为content_type传递给invoke_endpoint.
• 在收到415(不受支持的媒体类型)之后，问题仍然是content_type.以下打印语句帮助我揭示了预报器实际使用的content_type( application/json )，因此我从预报器中获取了适当的序列化代码.

• Looked up predictions.py and content_types.py implementation in sagemaker python library to see what content types it used and what arguments it had.
• First I thought that application/x-npy content_type was used and thus tried using serialisation code from predictor.py and passing the application/x-npy as content_type to invoke_endpoint.
• After receiving 415 (unsupported media type), the issue was still the content_type. The following print statements helped me to reveal what content_type predictor actually uses (application/json) and thus I took the appropriate serialisation code from predictor.py

from sagemaker.tensorflow import TensorFlowPredictor

predictor = TensorFlowPredictor('sagemaker-tensorflow-serving-date')
data = np.array(raw_data)
response = predictor.predict(data=data)
print(predictor.content_type)
print(predictor.accept)
predictions = response['predictions']
print(predictions)

TL; DR

lambda解决方案:

TL;DR

Solution for lambda:

import json
import boto3

ENDPOINT_NAME = 'sagemaker-tensorflow-serving-date'
config = botocore.config.Config(read_timeout=80)
runtime= boto3.client('runtime.sagemaker', config=config)
data = np.array(raw_data)
payload = json.dumps(data.tolist())
response = runtime.invoke_endpoint(EndpointName=ENDPOINT_NAME,
                                   ContentType='application/json',
                                   Body=payload)
result = json.loads(response['Body'].read().decode())
res = result['predictions']

注意:Lambda中不包含numpy，因此您可以自己包含numpy，也可以不包含numpy.data.tolist()与python list和json.dump一起使用该列表(包含列表).从您的代码看来，您似乎拥有python列表而不是numpy数组，因此简单的json转储应该可以工作.

Note: numpy is not included in lambda thus you would either include the numpy yourself or instead of data.tolist() operate with python list and json.dump that list (of lists). From your code it seems to me you have python list instead of numpy array, so simple json dump should work.

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