1回答
- 新しい順
- 投票が多い順
- コメントが多い順
0
One way to solve this would be to save the CSV to the local storage on the SageMaker notebook instance, and then use the S3 API's via boto3 to upload the file as an s3 object. S3 docs for upload_file() available here.
Note, you'll need to ensure that your SageMaker hosted notebook instance has proper ReadWrite permissions in its IAM role, otherwise you'll receive a permissions error.
code you already have, saving the file locally to whatever directory you wish
file_name = "mydata.csv"
df.to_csv(file_name)
instantiate S3 client and upload to s3
import boto3
s3 = boto3.resource('s3')
s3.meta.client.upload_file(file_name, 'YOUR_S3_BUCKET_NAME', 'DESIRED_S3_OBJECT_NAME')
Alternatively, upload_fileobj() may help for parallelizing as a multi-part upload.
回答済み 5年前
関連するコンテンツ
- AWS公式更新しました 1年前