Hybrid Cloud Storage for Machine Learning Data Sets with AWS Storage Gateway

Question

Pulumi · Accepted Answer

To set up a hybrid cloud storage system for machine learning datasets with AWS Storage Gateway, you can leverage several AWS Storage Gateway resources. The AWS Storage Gateway service enables on-premises applications to use AWS cloud storage seamlessly. It connects your on-site environment to cloud storage through various types of gateways, such as file gateways, volume gateways, and tape gateways. Depending on your scenario, you may choose one of these gateway types.

Considering machine learning datasets, a file gateway is often suitable because it stores files as objects in Amazon S3, which provides cost-efficient storage and works seamlessly with machine learning and data processing services in AWS.

Below is a Pulumi program in Python that creates an NFS file gateway with associated storage. The gateway is configured to store files on S3 and make them available as an NFS mount, which is often used in machine learning workflows.

The program includes:
1. AWS Storage Gateway Gateway: To create the storage gateway.
2. AWS Storage Gateway NFS File Share: To set up the NFS file share, connecting the gateway to an S3 bucket.
3. S3 Bucket: To store the ML datasets.

Remember to have AWS CLI configured with the credentials and preferred region where you want to deploy the resources.

```python
import pulumi
import pulumi_aws as aws

# Create an AWS S3 bucket to store machine learning datasets
ml_data_bucket = aws.s3.Bucket("mlDataBucket",
    lifecycle_rules=[aws.s3.BucketLifecycleRuleArgs(
        id="auto-delete-after-90-days",
        enabled=True,
        expiration=aws.s3.BucketLifecycleRuleExpirationArgs(
            days=90,
        ),
    )],
)

# Create an AWS Storage Gateway to connect on-premises applications with cloud storage
storage_gateway = aws.storagegateway.Gateway("mlDataGateway",
    gateway_type="FILE_S3",
    gateway_name="ml-data-gateway",
    gateway_timezone="GMT",
    gateway_ip_address="172.20.30.40",  # Replace with your network's IP address
)

# Create an NFS file share to expose S3 bucket to the on-premises applications
nfs_file_share = aws.storagegateway.NfsFileShare("mlDataNfsFileShare",
    role_arn="arn:aws:iam::123456789012:role/StorageGatewayAccess",  # Replace with the IAM role ARN
    client_lists=["172.20.30.0/24"],  # Replace with your on-premises network range
    gateway_arn=storage_gateway.arn,
    location_arn=ml_data_bucket.arn,
    kms_encrypted=False,
    file_share_name="mlDataFileShare",
)

# Export the NFS file share ARN and the S3 bucket name
pulumi.export("nfs_file_share_arn", nfs_file_share.arn)
pulumi.export("ml_data_bucket_name", ml_data_bucket.bucket)
```

In this program, we first created an S3 bucket to store the machine learning datasets with a lifecycle rule that automatically deletes objects after 90 days. Then, we configured a storage gateway of type "FILE_S3", specifying properties such as the gateway's timezone and IP address within the on-premise network.

After that, we created an NFS file share associated with the storage gateway and linked to the S3 bucket. We used an IAM role ARN that grants the necessary permissions for the file share to interact with S3.

Lastly, we exported the ARN (Amazon Resource Name) of the NFS file share and the S3 bucket name for easy reference.

Adjust the IP addresses, IAM roles, and any other configurations to match your environment and security requirements. Deploying this program with Pulumi will set up the infrastructure for a hybrid cloud storage solution suitable for machine learning datasets.