在 AWS SageMaker for Scikit Learn 模型中调用终端节点

在 AWS Sagemaker 上部署 scikit 模型后，我使用以下方法调用我的模型：

import pandas as pd
payload = pd.read_csv('test3.csv')
payload_file = io.StringIO()
payload.to_csv(payload_file, header = None, index = None)
import boto3
client = boto3.client('sagemaker-runtime')
response = client.invoke_endpoint(
    EndpointName= endpoint_name,
    Body= payload_file.getvalue(),
    ContentType = 'text/csv')
import json
result = json.loads(response['Body'].read().decode())
print(result)

上面的代码运行良好，但是当我尝试时：

payload = np.array([[100,5,1,2,3,4]])

我收到错误：

ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received server error (500) from container-1 with message 
"<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2 Final//EN"> <title>500 Internal Server Error</title> <h1>
Internal Server Error</h1> <p>The server encountered an internal error and was unable to complete your request.  
Either the server is overloaded or there is an error in the application.</p>

在Scikit-learn SageMaker Estimators and Models中提到，

SageMaker Scikit-learn模型服务器提供默认实现的input_fn。此函数反序列化 JSON、CSV 或 NPY 编码的数据到一个 NumPy 数组中。

我想知道如何修改默认值以接受 2D numpy 数组，以便将其用于实时预测。

有什么建议吗？我尝试使用带有Scikit-learn和Linear Learner的推理管道作为参考，但无法用Scikit模型替换线性学习器。我收到了同样的错误。

如果有人找到了一种方法来更改默认input_fn、predict_fn和output_fn以接受 numpy 数组或字符串，那么请分享。

但我确实找到了一种默认的方法。

import numpy as np
import pandas as pd
df = pd.DataFrame(np.array([[100.0,0.08276299999999992,77.24,0.0008276299999999992,43.56,
                             6.6000000000000005,69.60699488825647,66.0,583.0,66.0,6.503081996847735,44.765133295284,
                             0.4844340723821271,21.35599999999999],
                            [100.0,0.02812099999999873,66.24,0.0002855600000003733,43.56,6.6000000000000005,
                             1.6884635296354735,66.0,78.0,66.0,6.754543287329573,47.06480204081666,
                             0.42642318733140017,0.4703999999999951],
                            [100.0,4.374382,961.36,0.043743819999999996,25153.96,158.6,649.8146514292529,120.0,1586.0
                             ,1512.0,-0.25255116297020636,1.2255274408634853,-2.5421402801039323,614.5056]]),
                  columns=['a', 'b', 'c','d','e','f','g','h','i','j','k','l','m','n'])
import io
from io import StringIO
test_file = io.StringIO()
df.to_csv(test_file,header = None, index = None)

然后：

import boto3
client = boto3.client('sagemaker-runtime')
response = client.invoke_endpoint(
    EndpointName= endpoint_name,
    Body= test_file.getvalue(),
    ContentType = 'text/csv')
import json
result = json.loads(response['Body'].read().decode())
print(result)

但是，如果有更好的解决方案，那么这将非常有帮助。

您应该能够为 model.deploy（）返回的预测器设置序列化程序/反序列化程序。此处的 FM 示例笔记本中有一个执行此操作的示例：

https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/factorization_machines_mnist/factorization_machines_mnist.ipynb

请尝试此操作，并让我知道这是否适合您！

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