python - 如何在 Python 中 Sagemaker 的 XGBoost 训练作业中指定 content_type?
问题描述
我正在尝试使用 sagemaker 库训练模型。到目前为止,我的代码如下:
container = get_image_uri(boto3.Session().region_name,
'xgboost',
repo_version='0.90-1')
estimator = sagemaker.estimator.Estimator(container,
role = 'AmazonSageMaker-ExecutionRole-20190305TXXX',
train_instance_count = 1,
train_instance_type = 'ml.m4.2xlarge',
output_path = 's3://antifraud/production/',
hyperparameters = {'num_rounds':'400',
'objective':'binary:logistic',
'eval_metric':'error@0.1'})
train_config = training_config(estimator=estimator,
inputs = {'train':'s3://antifraud/production/train',
'validation':'s3://-antifraud/production/validation'})
我在解析超参数时遇到错误。此命令在控制台中为我提供了配置 JSON 输出。我已经能够使用配置为 Json 的 boto3 运行训练作业,所以我发现我的代码生成的 json 配置中缺少的是 content_type 参数,它应该如下所示:
"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 中编码 content_type = 'text/csv' 作为参数,并在输入中作为字典的另一个键,但没有成功。我怎样才能使这项工作?
解决方案
我已经使用 s3_input 对象解决了它:
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})