AWS Sagemaker, InvokeEndpoint operation, Model error: "setting an array element with a sequence."

AWS Sagemaker, InvokeEndpoint operation, Model error: "setting an array element with a sequence."

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AWS Sagemaker, InvokeEndpoint operation, Model error: "setting an array element with a sequence."
Tag : python , By : esimran
Date : November 29 2020, 04:01 AM

I wish this help you I am trying to Invoke Endpoint, previously deployed on Amazon SageMaker. Here is my code: , This works:
import numpy as np
import boto3

client = boto3.client('sagemaker-runtime')
endpoint_name = 'DEMO-XGBoostEndpoint-2018-12-12-22-07-28'
test_vector = [3.60606061e+00, 

body = ',',join([str(item) for item in test_vector])
response = client.invoke_endpoint(EndpointName=endpoint_name,

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"Error: setting an array element with a sequence"

Tag : python , By : SilverRuby
Date : March 29 2020, 07:55 AM
will help you I am trying to convert Matlab code into Python, but I'm receiving an error when I append zeros in my array. , You want to join the list with the array, so try
bits=concatenate(([0,0,0,0,0,0,0,0], bits))
>> x = [1,2,3]

x =

     1     2     3

>> [0,0,x]

ans =

     0     0     1     2     3
>>> x = [1,2,3]
>>> [0,0,x]
[0, 0, [1, 2, 3]]
>>> [0,0] + x
[0, 0, 1, 2, 3]

"Setting an array element with a sequence" error when using fsolve for a system of nonlinear equations

Tag : python , By : kokok13
Date : March 29 2020, 07:55 AM
I wish this helpful for you The one thing I can spot is that the y[5] here looks like a typo:

"Setting an array element with a sequence" numpy error

Tag : python , By : user109285
Date : March 29 2020, 07:55 AM
Any of those help The spokenMean type is object, i.e. it's a 1D array which holds smaller 1D arrays. The first thing you need to do is to convert it to a 2D float array. This worked for me:
spokenMean = features(spoken, np.mean)
spokenMean = np.vstack(spokenMean[:]).astype(np.float32)
result = np.concatenate([spokenMean, written], axis=1)

AWS sagemaker invokeEndpoint model internal error

Tag : chash , By : Bo.
Date : March 29 2020, 07:55 AM
wish helps you The problem relied on the data format as suspected. In my case all I had to do is send the data as a json serialized string array and use ContentType = application/json because the python function running on the endpoint which is responsible for sending the data to the predictor was only accepting json strings.
Another way to solve this issues is to modify the python function which is responsible for the input handling to accept all content types and modify the data in a way that the predictor will understand.
        var data = new string[] { "this movie was extremely good .", "the plot was very boring ." };
        var serializedData = JsonConvert.SerializeObject(data);

        var credentials = new Amazon.Runtime.BasicAWSCredentials("","");
        var awsClient = new AmazonSageMakerRuntimeClient(credentials, RegionEndpoint.EUCentral1);
        var request = new Amazon.SageMakerRuntime.Model.InvokeEndpointRequest
            EndpointName = "endpoint",
            ContentType = "application/json",
            Body = new MemoryStream(Encoding.ASCII.GetBytes(serializedData)),

        var response = awsClient.InvokeEndpoint(request);
        var predictions = Encoding.UTF8.GetString(response.Body.ToArray());

Amazon Sagemaker. AccessDeniedException when calling the InvokeEndpoint operation

Tag : amazon-web-services , By : alchemist
Date : March 29 2020, 07:55 AM
it should still fix some issue The problem was with the MFA autharization. When I invoked the model from inside the model, the MFA was passed. But when I tried to invoke the model from my machine, the MFA was not passed, so the access was denied.
I created special user without MFA to debug the model, and that solved my problem.
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