How do you load an existing S3 bucket in Sagemaker using R Programming?

Question

I know how to use python to load an existing s3 bucket in sage maker using R. Something like this:

role = get_execution_role() region = boto3.Session().region_name bucket='existing S3 Bucket' data_key = 'Data file in the existing s3 bucket' data_location = 's3://{}/{}'.format(bucket, data_key)

How can one recreate this using R in Sage maker? All i see in the available documentation is how to create a new bucket but none of it mentions how to use an existing S3 bucket. Help would be appreciated.

link to documentation for R in sage maker: https://aws.amazon.com/blogs/machine-learning/using-r-with-amazon-sage maker/

you are not typing the link correctly: aws.amazon.com/blogs/machine-learning/… — Vitali Avagyan
– Vitali Avagyan, Commented Nov 29, 2019 at 23:30
I do not know the details but you can conceptually load reticulate library and hopefully execute the same Python code in R. — Vitali Avagyan
– Vitali Avagyan, Commented Nov 29, 2019 at 23:32

Neelam Gehlot · Accepted Answer · 2019-12-13 23:29:08Z

Thanks for using Amazon SageMaker!

You can use SageMaker Session helper methods for listing and reading files from S3. Please checkout this sample notebook if you need examples for using SageMaker Session using_r_with_amazon_sagemaker.ipynb .

Thanks,

Neelam

RobinGower · Accepted Answer · 2022-11-17 10:40:56Z

You can use the Sagemaker Python SDK via reticulate, for example:

library(reticulate) sagemaker <- import("sagemaker") uri <- "s3://my-bucket/my-prefix" files <- sagemaker$s3$S3Downloader$list(uri) csv <- sagemaker$s3$S3Downloader$read_file(files[1]) df <- read.csv(text=csv)

RobinGower · Accepted Answer · 2022-11-17 12:20:55Z

You can use packages from the cloudyr project:

library(aws.s3) library(aws.ec2metadata) # will mutate environment to add credentials library(readr) df <- s3read_using(FUN = read_csv, bucket = "my-bucket", object = "my-key.csv")

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