Sagemaker：如何在 Predictor 中设置 content_type（Sagemake > 2.0）？

请求帮助解决以下错误。

调用 InvokeEndpoint 时发生错误 (ModelError) 操作：从模型收到客户端错误 (415) 和消息 “不支持内容类型应用程序/八位字节流。支持 内容类型是文本/csv、文本/libsvm”

这是相关代码 -

from sagemaker import image_uris
from sagemaker.estimator import Estimator

xgboost_hyperparameters = {
        "max_depth":"5",
        "eta":"0.2",
        "gamma":"4",
        "min_child_weight":"6",
        "subsample":"0.7",
        "num_round":"50"
}

xgboost_image = image_uris.retrieve("xgboost", boto3.Session().region_name, version="1")



estimator = Estimator(image_uri = xgboost_image,
                     hyperparameters = xgboost_hyperparameters,
                     role = role,
                     instance_count=1, 
                     instance_type='ml.m5.2xlarge', 
                      output_path= output_loc,
                     volume_size=5 )

from sagemaker.serializers import CSVSerializer
from sagemaker.deserializers import CSVDeserializer

train_input = sagemaker.inputs.TrainingInput(s3_data = train_loc, content_type='text/csv',s3_data_type = 'S3Prefix')
valid_input = sagemaker.inputs.TrainingInput(s3_data = validation_loc, content_type='text/csv',s3_data_type = 'S3Prefix')

estimator.CONTENT_TYPE = 'text/csv'
estimator.serializer = CSVSerializer()
estimator.deserializer = None

estimator.fit({'train':train_input, 'validation': valid_input})

# deploy model with data config
from sagemaker.model_monitor import DataCaptureConfig
from time import gmtime, strftime
s3_capture_upload_path = 's3://{}/{}/monitoring/datacapture'.format(bucket, prefix)
model_name = 'project3--model-' + strftime("%Y-%m-%d-%H-%M-%S", gmtime())
endpoint_name = 'project3-endpoint'
data_capture_configuration = DataCaptureConfig(
                        enable_capture = True,
                        sampling_percentage=100,
                        destination_s3_uri=s3_capture_upload_path  )

deploy = estimator.deploy(initial_instance_count = 1,
                          instance_type = 'ml.m4.xlarge'    ,
                          data_capture_config=data_capture_configuration,
                          model_name=model_name,
                          endpoint_name = endpoint_name
                         )

然后我面临以下错误预测器

from sagemaker.predictor import Predictor

predictor = Predictor(endpoint_name=endpoint_name)
with open('test.csv', 'r') as f:
    for row in f:
        print(row)
        payload = row.rstrip('\n')
        response = predictor.predict(data=payload[2:])
        sleep(0.5)
print('done!')
 

我查看了这些链接但没有找到答案

1. https://github.com/aws-samples/reinvent2019-aim362-sagemaker-debugger-model-monitor/blob/master/02_deploy_and_monitor/deploy_and_monitor.ipynb https://github.com/aws-samples/reinvent2019-aim362-sagemaker-debugger-model-monitor/blob/master/02_deploy_and_monitor/deploy_and_monitor.ipynb
2. 如何在 Python 中的 Sagemaker 的 XGBoost 训练作业中指定 content_type？ https://stackoverflow.com/questions/57908395/how-can-i-specify-content-type-in-a-training-job-of-xgboost-from-sagemaker-in-py
3. https://github.com/aws/amazon-sagemaker-examples/issues/729 https://github.com/aws/amazon-sagemaker-examples/issues/729

As seen here https://sagemaker.readthedocs.io/en/v2.15.2/api/inference/predictors.html#sagemaker.predictor.Predictor.predict，predict()的initial_args是“boto3的默认参数调用端点 https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker-runtime.html#SageMakerRuntime.Client.invoke_endpoint call.

对我来说，这有效：

predictor.predict(review_input, initial_args={'ContentType': 'text/plain'})