1. Scalable ML Model File System with AWS EFS


    To set up a scalable file system for a Machine Learning (ML) model using AWS Elastic File System (EFS), you would typically want to create an EFS file system and configure it for high performance and scalability. In the context of ML workloads, where the model might need to read and write large datasets, it's important that the file system can scale and provide the necessary throughput.

    AWS EFS is a scalable file storage service for use with AWS Cloud services and on-premises resources. It grows and shrinks automatically as you add and remove files, so you only pay for what you use, and it is designed to offer high performance for a range of workloads, including those that are I/O intensive, like ML model training.

    In the Pulumi program below, we will create an AWS EFS file system, enable encryption at rest, and set the performance mode to MaxIO which is optimized for high concurrency and throughput, making it suitable for ML workloads that require high levels of I/O operations. We shall also create a backup policy to ensure our data is backed up automatically, adding a layer of data protection.

    Here's a Pulumi Python program that achieves this:

    import pulumi import pulumi_aws as aws # Creates a new EFS file system configured for high performance (MaxIO performance mode). efs_file_system = aws.efs.FileSystem("mlModelEfs", encrypted=True, # Encrypt the file system for security. kms_key_id="your-kms-key-id", # Replace with your KMS Key ID for encryption. performance_mode="maxIO", # Performance mode suitable for ML workloads. throughput_mode="provisioned", # Provisioned throughput mode. provisioned_throughput_in_mibps=100, # Provisioned throughput in MiB/s. tags = { "Name": "ml-model-efs", # Name tag for easier identification. "Project": "ML_Model_Storage" } ) # Creates a backup policy for the EFS file system. efs_backup_policy = aws.efs.BackupPolicy("mlModelEfsBackupPolicy", file_system_id=efs_file_system.id, backup_policy={ "status": "ENABLED" }, ) # Exports the EFS file system ID and ARN, which can be used to mount the file system # or used by other AWS resources or services. pulumi.export("efs_file_system_id", efs_file_system.id) pulumi.export("efs_arn", efs_file_system.arn)

    Before using this Pulumi program, ensure that you have set up your AWS provider credentials correctly for Pulumi to interact with your AWS account. Also, replace "your-kms-key-id" with the Key ID of an AWS KMS key that you have created to manage encryption keys. This Key ID is used to encrypt the EFS file system at rest.

    To apply this program and create the resources in your AWS account, you would run pulumi up in your terminal. This command would prompt you to confirm the creation of the specified resources. After confirmation, Pulumi will provision the EFS file system and apply the backup policy automatically, giving you a scalable and secure file system for your ML workloads.

    Keep in mind that you should configure your Pulumi stack to use an AWS region that supports EFS. Additionally, consider setting up EFS mount targets and access points depending on how you plan to use the file system with your ML models. If you need guidance on those additional configurations, please feel free to ask, and we can expand on the program as needed.