Load S3 Data into AWS SageMaker Notebook

Question

I've just started to experiment with AWS SageMaker and would like to load data from an S3 bucket into a pandas dataframe in my SageMaker python jupyter notebook for analysis.

I could use boto to grab the data from S3, but I'm wondering whether there is a more elegant method as part of the SageMaker framework to do this in my python code?

Thanks in advance for any advice.

Answer 1

import boto3
import pandas as pd
from sagemaker import get_execution_role

role = get_execution_role()
bucket='my-bucket'
data_key = 'train.csv'
data_location = 's3://{}/{}'.format(bucket, data_key)

pd.read_csv(data_location)

Answer 2

In the simplest case you don't need boto3, because you just read resources.
Then it's even simpler:

import pandas as pd

bucket='my-bucket'
data_key = 'train.csv'
data_location = 's3://{}/{}'.format(bucket, data_key)

pd.read_csv(data_location)

But as Prateek stated make sure to configure your SageMaker notebook instance to have access to s3. This is done at configuration step in Permissions > IAM role

Answer 3

If you have a look here it seems you can specify this in the InputDataConfig. Search for "S3DataSource" (ref) in the document. The first hit is even in Python, on page 25/26.