Using S3 Bucket

Amazon S3, or Amazon Simple Storage Service, is an object storage service offered by Amazon Web Services (AWS). It provides a highly scalable, durable, and available storage solution for any type of data, accessible from anywhere on the web.

DeepSense AWS S3

At DeepSense, you will be using S3 buckets to store large datasets. These datasets can then be accessed in AWS SageMaker for use in projects.

The AWS administrator will create your DeepSense AWS IAM user account. AWS will send you an email containing your account details. Users will then have access to AWS.

Accessing the S3 Bucket on DeepSense AWS

1. Sign in to the AWS access portal once your account is active.
2. From the account dropdown, select DeepSense and choose the respective bucket you want to access.
3. The AWS console will open after selection.
4. Use the direct link provided by your Cloud Administrator (tip: bookmark the link for easier access).
5. The S3 bucket should now be accessible in your browser.

Using the S3 Bucket on DeepSense AWS

• Once inside the S3 bucket, you can:
  • Download files
  • Upload files
  • Delete files or directories

• To access the bucket in your terminal, use the Access Key from the AWS access portal.

• To add bucket files to your AI Development Platform notebook, you have two approaches:
  • Option 1 – Easy method (for small files):

Download the files from the S3 console to your local machine, then upload them to your JupyterLab directory for use in your project.

• • Option 2 – Recommended for large files:

1. Create your access keys from the AWS access portal.

2. Open the terminal in your notebook instance.

3. Follow the steps in the [AWS CLI Authentication Guide](https://docs.aws.amazon.com/cli/v1/userguide/cli-authentication-user.html#cli-authentication-user-configure.title).

4. Once configured, run S3 CLI commands (e.g., aws s3 cp, aws s3 ls) to upload or download files directly between your bucket and notebook.