如何在Sagemaker的Python的XGBoost培训工作中指定content_type?

And I get an error parsing the hyperparameters. This commands gives me a configuration JSON output in the console. I have been able to run a training job using boto3 with the configuration as Json, so I have figured out that the thing I am missing in my json configuration generated by my code is the content_type parameter, which should be there as follow:"InputDataConfig": [ { "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://antifraud/production/data/train", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" }, { "ChannelName": "validation", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://antifraud/production/validation", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" }]我尝试在容器，估计器和train_config中将content_type ='text/csv'编码为参数，并在输入内部将其编码为字典的另一个键，但没有成功.我该怎么做?I have tried coding content_type = 'text/csv' in container, estimator and train_config as parameter and also inside inputs as another key of the dictionary, with no success. How could I make this work?推荐答案我已经使用s3_input对象解决了它:I have solved it using s3_input objects:s3_input_train = sagemaker.s3_input(s3_data='s3://antifraud/production/data/{domain}-{product}-{today}/train_data.csv',content_type='text/csv')s3_input_validation = sagemaker.s3_input(s3_data='s3://antifraud/production/data/{domain}-{product}-{today}/validation_data.csv',content_type='text/csv')train_config = training_config(estimator=estimator,inputs = {'train':s3_input_train, 'validation':s3_input_validation}) 这篇关于如何在Sagemaker的Python的XGBoost培训工作中指定content_type?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！