1. Automated AI Dataset Backup to AWS S3 with AWS Storage Gateway

    Python

    To create an automated backup solution for an AI dataset to AWS S3 using AWS Storage Gateway, we will accomplish the following steps:

    1. Deploy an AWS Storage Gateway to act as an interface between on-premises storage and AWS S3 storage.
    2. Configure a file share on the Storage Gateway that's linked to an S3 bucket.
    3. Set up the file share to automatically sync the dataset to S3, providing an offsite backup.

    The resources we will use are:

    • aws.storagegateway.Gateway: This represents a gateway that links on-premise software appliance with cloud-based storage, providing seamless integration with data security features.
    • aws.s3.Bucket: This resource will create a new Amazon S3 bucket where our datasets will be stored.
    • aws.storagegateway.SmbFileShare: This creates an SMB (Server Message Block) file share on the AWS Storage Gateway. This file share will be backed up by an S3 bucket.
    import pulumi import pulumi_aws as aws # Important: Replace these placeholder values with your actual configuration details. gateway_name = "my-ai-dataset-gateway" gateway_ip_address = "192.168.0.1" # The IP address of your gateway device. s3_bucket_name = "my-ai-dataset-backup" gateway_timezone = "GMT" gateway_region = "us-west-2" # Create a new Amazon S3 bucket for storing the AI dataset. ai_dataset_s3_bucket = aws.s3.Bucket(s3_bucket_name, acl="private", tags={ "Purpose": "AI Data Backup", }) # Deploy a new AWS Storage Gateway to interface with on-premises storage and backup to AWS S3. storage_gateway = aws.storagegateway.Gateway(gateway_name, gateway_ip_address=gateway_ip_address, gateway_timezone=gateway_timezone, gateway_type="FILE_S3", smb_active_directory_settings={ "domainName": "EXAMPLE_DOMAIN", "username": "YOUR_USERNAME", "password": "YOUR_PASSWORD", # Securely handle the password in a production environment. }) # This is a unique string that AWS Storage Gateway uses to activate the gateway activation_key = "ACTIVATION_KEY_HERE" # Set up a Storage Gateway SMB file share backed by the S3 bucket we created earlier. # This can be used to upload the data to S3 through the Storage Gateway. smb_file_share = aws.storagegateway.SmbFileShare("mySmbFileShare", gateway_arn=storage_gateway.arn, location_arn=ai_dataset_s3_bucket.arn, role_arn="arn:aws:iam::123456789012:role/StorageGatewayAccess", # Replace with your actual IAM role ARN. bucket_region=gateway_region, cache_attributes={ "cache_stale_timeout_in_seconds": 600, }, audit_destination_arn=ai_dataset_s3_bucket.arn, case_sensitivity="CaseSensitive") # Exports pulumi.export('s3_bucket_name', ai_dataset_s3_bucket.bucket) # The name of the S3 bucket used for backup. pulumi.export('storage_gateway_arn', storage_gateway.arn) # The ARN of the Storage Gateway. pulumi.export('smb_file_share_arn', smb_file_share.arn) # The ARN of the Storage Gateway SMB file share.

    In this program:

    • We start by importing pulumi and pulumi_aws, which are libraries we need for interacting with AWS resources using Pulumi.
    • A new Amazon S3 bucket is created to be the destination for our dataset backups.
    • We deploy a new AWS Storage Gateway to act as the interface between on-premises storage and our S3 bucket. Note that gateway_type is set to FILE_S3, which allows us to use the gateway for file operations against S3 buckets.
    • The storagegateway.SmbFileShare sets up an SMB file share on our Storage Gateway. This resource backs up the data to the S3 bucket we've set up.
    • The activation_key would be unique to your Gateway setup and needs to be retrieved according to the AWS documentation.
    • IAM role with necessary permissions is mentioned; this needs to be created and configured in AWS IAM.
    • We conclude the script by exporting key attributes such as the S3 bucket name and the ARNs of the Storage Gateway and SMB file share, which can be useful for management and referencing in other Pulumi programs or AWS services.

    Security Note: This program contains sensitive information (like the password). In a production scenario, this should be better managed through secure secret management mechanisms offered by Pulumi or AWS (such as AWS Secrets Manager or Pulumi Config Secrets).

    In a real-world scenario, more details and configurations would be required depending on the actual setup of your on-premises environment, the specifics of your AI dataset, and your network configuration. Additionally, you need to ensure that your on-premises environment can connect to the AWS Storage Gateway for this to work correctly.


GPT4o|Modeexplainbalancedcode