1. AI Systems Configuration Management and History Recording.

    Python

    Managing configurations and recording history for AI systems can be a complex task, involving maintaining the state of infrastructure and keeping track of changes over time for auditing or rollback purposes. One approach to address this in the cloud is to use infrastructure as code (IaC) tools like Pulumi, which allow you to define infrastructure using programming languages. Pulumi then takes care of provisioning and managing the infrastructure as it changes over time.

    The following Pulumi program will demonstrate how to set up configuration management and history recording for an AI system using AWS as the cloud provider:

    1. We'll use AWS Config to record configuration changes to AWS resources. This service provides detailed records of the configurations of your AWS resources and allows you to automate the evaluation of recorded configurations against desired configurations.

    2. The aws.cfg.Recorder resource sets up the recording of selected resource types and captures all the changes.

    3. The aws.cfg.DeliveryChannel resource manages the delivery of recorded configurations to an S3 bucket where they will be stored.

    4. For the sake of simplicity, we will not set up any rules or multi-account setups.

    Now, let’s take a look at the Python program:

    import pulumi import pulumi_aws as aws # Create an S3 bucket to store configuration snapshots and history config_bucket = aws.s3.Bucket("config-bucket") # Set up the AWS Config service to record all changes to all supported types. config_recorder = aws.cfg.Recorder("config-recorder", role_arn=pulumi.Output.secret("<ARN_OF_AWS_CONFIG_SERVICE_ROLE>"), # Replace with the ARN of a role that AWS Config can assume recording_group=aws.cfg.RecorderRecordingGroupArgs( all_supported=True, include_global_resource_types=True, # Set to `True` if you want to record all resources that are global (IAM, Route53, etc.) )) # Establish a delivery channel for AWS Config information to be stored in the S3 bucket delivery_channel = aws.cfg.DeliveryChannel("delivery-channel", s3_bucket_name=config_bucket.id, snapshot_delivery_properties=aws.cfg.DeliveryChannelSnapshotDeliveryPropertiesArgs( delivery_frequency="Six_Hours", )) # To output the S3 bucket URL where the configurations are stored pulumi.export("config_bucket_url", config_bucket.website_endpoint)

    Breaking down each part of the program:

    • A new S3 bucket is created, which will serve as the storage for all our configuration history.
    • AWS Config is set up with a Recorder that specifies we want to track all supported AWS resources. You will have to create an IAM role with the necessary permissions for AWS Config to assume and replace <ARN_OF_AWS_CONFIG_SERVICE_ROLE> with its ARN.
    • A DeliveryChannel for AWS Config is set up to point at the S3 bucket. The delivery frequency is set to six hours, meaning AWS Config will deliver configuration snapshots to the S3 bucket every six hours.

    Please note that this program does not provision the required IAM role for AWS Config. You would have to create it manually or add it to your Pulumi program using the respective aws.iam resources, ensuring that the role has the correct permissions policy attached.

    By running this program with Pulumi, you instruct the Pulumi engine to create these resources in the order they are needed and to manage the state of your infrastructure. The resulting system records configurations and changes over time, which can then be audited and used for compliance purposes.


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