部署在AWS上Sagemaker一个scikit模型后，我调用下面用我的模型：
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)
上面的代码完美地工作，但是当我尝试：
payload = np.array([[100,5,1,2,3,4]])
我得到的错误：
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>
它在Scikit-learn SageMaker Estimators and Models提及的是，
SageMaker Scikit学习模型服务器提供input_fn的默认实现。此功能反序列化JSON，CSV或NPY编码数据分成NumPy的阵列。
我想知道我怎么可以修改默认接受2D numpy的阵列，因此它可以被用于实时预测。
任何建议？我一直在使用Inference Pipeline with Scikit-learn and Linear Learner作为参考尝试，但不能用Scikit模型代替线性学习者。我收到了同样的错误。
如果有人找到了一种方法来改变默认input_fn，predict_fn和output_fn接受numpy的数组或字符串，那么请做份额。
但我没有找到与默认这样做的方式。
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)
但请，如果有一个更好的解决方案那么这将是真正的帮助。
您应该能够设置（）你model.deploy返回的预测串行器/解串器。有一个在FM例如笔记本电脑在这里做这样的一个例子：
请试试这个，让我知道，如果你是这样的！