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 amount of dataset. Storing all of them on S3 and then using the same in AWS SageMaker to use for projects.
AWS administrator will create DeepSense AWS IAM user account. AWS will send you an email containing information about your account. and users will have access to the AWS SageMaker Studio specific to thee project in new domain.
Accessing the S3 Bucket on DeepSense AWS
- Once your account is accessible and you are sign on to the AWS access portal.
- Select the account dropdown of DeepSense select the respective bucket access you want to use.
- After selection it will open AWS console.
- Click on the link directly from your Cloud administrator has provided you the link (Bookmark link for easier access)
- S3 bucket should be accessible now on your browser.
Using the S3 Bucket on DeepSense AWS
- Once you are in the S3 bucket you can download, upload and delete files/ directories from the bucket.
- If you need to open the bucket in your terminal use the access key from the access portal.
- For adding your bucket files to AI Development Platform notebook, 1. Easy way - good for small files. 2. For big files.
- 1. Simply download the files from S3 console to your local and then upload it to your JupyterLab directory to use it for project.
- 2. Create your access keys from access portal then open terminal of your notebook instance and follow these steps https://docs.aws.amazon.com/cli/v1/userguide/cli-authentication-user.html#cli-authentication-user-configure.title after that just type in the S3 commands to get or put files in bucket.
