1. On-Premises Access to Cloud-Based AI Training Data via AWS Storage Gateway


    The AWS Storage Gateway service offers a hybrid storage service that enables your on-premises applications to seamlessly use AWS cloud storage. You can use it for various scenarios such as backup and restore, disaster recovery, and data processing. Specifically for your use case of providing on-premises access to AI training data, the Storage Gateway can be particularly useful by presenting cloud storage to your on-premises machines as if it were local storage.

    To facilitate on-premises access to cloud-based AI training data, you would typically set up a file gateway. The file gateway configuration provides a file-based interface to Amazon S3 buckets, allowing you to store and retrieve files as you would with a local file system. Here, we’ll use the Pulumi AWS package to set up a file gateway and an S3 bucket where your AI training data will reside.

    The following Pulumi program does the following:

    1. Setup an Amazon S3 bucket to hold your AI training data.
    2. Setup an AWS Storage Gateway (as a file gateway) that connects to the S3 bucket.
    3. Create an SMB file share on the AWS Storage Gateway. SMB (Server Message Block) is a network protocol that enables shared access to files, printers, serial ports, and other resources.

    Let's walk through the setup.

    import pulumi import pulumi_aws as aws # Create an Amazon S3 bucket to store your AI training data. ai_data_bucket = aws.s3.Bucket("aiDataBucket", acl="private", tags={ "Name": "ai-training-data-bucket", }) # Set up an AWS Storage Gateway for file sharing (File Gateway). # The Gateway requires a physical piece of hardware or software appliance to be set up on-premises to connect to AWS S3. # The activation process integrates this appliance with AWS. Below is a placeholder ARN for the gateway. # In practice, you will need to replace `gateway_arn` with the ARN from your activated gateway. gateway = aws.storagegateway.Gateway("aiDataGateway", gateway_name="aiData", gateway_timezone="GMT", gateway_type="FILE_S3", activation_key="YOUR_GATEWAY_ACTIVATION_KEY", # You receive this key when you activate the gateway. gateway_ip_address="YOUR_ON_PREMISES_IP_ADDRESS", # The IP address of the device that's running your Storage Gateway. smb_active_directory_settings={ "domainName": "YOUR_AD_DOMAIN", # Your Active Directory domain name, if using AD for user authentication. "username": "YOUR_AD_USER", "password": "YOUR_AD_PASSWORD", }) # Create an SMB File Share on the Storage Gateway. # This allows on-premises devices to connect via the SMB protocol to the S3 bucket through the Gateway. smb_file_share = aws.storagegateway.SmbFileShare("aiDataSmbFileShare", role_arn="arn:aws:iam::123456789012:role/StorageGatewayAccess", # Replace with an appropriate IAM Role ARN with permissions to access S3. gateway_arn=gateway.arn, location_arn=ai_data_bucket.arn, # Link the S3 bucket to the file share. audit_destination_arn="arn:aws:logs:us-west-2:123456789012:log-group:/aws/storagegateway/aiDataGateway", # Optional: For audit logging purposes. kms_encrypted=False, # Decide if you want to use KMS for encryption. Set to 'True' if KMS keys are used. bucket_region="us-west-2", # The S3 bucket's region. tags={ "Name": "ai-training-data-smb-file-share", }) # Export the S3 bucket name and the SMB file share ID. pulumi.export('s3_bucket_name', ai_data_bucket.id) pulumi.export('smb_file_share_id', smb_file_share.id)

    This code sets up the basic infrastructure needed on the AWS side for your scenario. The actual connection and interaction with the on-premises environment will depend on your specific network configuration and processes.

    The YOUR_GATEWAY_ACTIVATION_KEY, YOUR_ON_PREMISES_IP_ADDRESS, YOUR_AD_DOMAIN, YOUR_AD_USER, and YOUR_AD_PASSWORD placeholders must be replaced with actual values from your environment. Additionally, make sure the IAM Role with arn:aws:iam::123456789012:role/StorageGatewayAccess has the necessary permissions to interact with the S3 bucket and the AWS Storage Gateway.

    The pulumi.export statements at the end of the program will output the names of the created resources, which can be useful for integration with other systems or for checking the deployment status.

    Please note that for setting up the physical or virtual appliance that runs the Storage Gateway software on-premises, you would need to follow the AWS documentation or refer to your network administrator. This on-premises setup is outside the scope of a Pulumi program as it involves tasks that are performed outside of AWS itself.