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Chapter 2. Creating an Amazon S3 client using notebook cells

To interact with data in Amazon S3 buckets, you must create a local client to handle requests to that service.

Prerequisites

  • Access to a Jupyter notebook server running on Red Hat OpenShift AI.
  • Define values for the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables when you start your notebook server, using the values from your Amazon Web Services account under My Security Credentials.

Procedure

  1. In a new notebook cell, import the required libraries by adding the following: 

    import os
import boto3
from boto3 import session
    Copy to Clipboard Toggle word wrap

  2. In another new notebook cell, define the following to create your session and client.

    1. Define your credentials. 

      key_id = os.environ.get('AWS_ACCESS_KEY_ID')
secret_key = os.environ.get('AWS_SECRET_ACCESS_KEY')
      Copy to Clipboard Toggle word wrap

    2. Define the client session. 

      session = boto3.session.Session(aws_access_key_id=key_id, aws_secret_access_key=secret_key)
      Copy to Clipboard Toggle word wrap

    3. Define the client connection. 

      s3_client = boto3.client('s3', aws_access_key_id=key_id, aws_secret_access_key=secret_key)
      Copy to Clipboard Toggle word wrap

Verification

  • Create a new cell and run an Amazon S3 command such as the following: 

    s3_client.list_buckets()
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    A successful response includes a HTTPStatusCode of 200 and a list of Buckets similar to the following: 

     'Buckets': [{'Name': 'my-app-asdf3-image-registry-us-east-1-wbmlcvbasdfasdgvtsmkpt',
   'CreationDate': datetime.datetime(2021, 4, 21, 6, 8, 52, tzinfo=tzlocal())},
  {'Name': 'cf-templates-18rxasdfggawsvb-us-east-1',
   'CreationDate': datetime.datetime(2021, 2, 15, 18, 35, 34, tzinfo=tzlocal())}
    Copy to Clipboard Toggle word wrap
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