1. Periodic Snapshots for AI Training Data Versioning


    Creating periodic snapshots for AI training data versioning is an essential practice in machine learning operations (MLOps). It allows you to maintain a history of your AI training data, which is crucial for reproducibility of model training, compliance, and troubleshooting. In a cloud environment, snapshots are point-in-time copies of your data stored in block storage, like disks.

    To implement periodic snapshots using Pulumi, we can use the cloud provider's snapshot resource. For example, if you're using AWS, you'd take advantage of the EBS (Elastic Block Store) snapshot capabilities. If you're on Azure, you'd use disk snapshots, and for Google Cloud, persistent disk snapshots.

    This program demonstrates how to set up periodic snapshots for AI training data using AWS EBS as an example. We'll define an EBS volume to represent our AI training data storage and set up a Pulumi Cron resource to periodically create snapshots of that volume.

    Here's how you can set up such a program in Python using Pulumi:

    import pulumi import pulumi_aws as aws # Create an EBS Volume that represents the AI training data storage training_data_volume = aws.ebs.Volume("trainingDataVolume", availability_zone="us-west-2a", size=50, # The size of the volume (in GBs) tags={ "Name": "TrainingData", }) # Function to create a snapshot of the given volume def create_snapshot(volume_id, snapshot_description): return aws.ebs.Snapshot("trainingDataSnapshot", volume_id=volume_id, description=snapshot_description, tags={ "Name": "TrainingDataSnapshot", }) # Example: Snapshot schedule every day at midnight (UTC) daily_snapshot_schedule = "0 0 * * *" # This is a standard cron expression # Call the snapshot function according to the schedule # Note: The following is a conceptual representation. Pulumi currently does not have built-in cron scheduling. # You would need to use AWS Lambda function and CloudWatch Events or similar mechanisms to schedule snapshots. # In an actual implementation, you'd use the AWS SDK within a Lambda function. scheduled_snapshot = pulumi.Resource("scheduledSnapshot", opts=pulumi.ResourceOptions( custom_timeouts=pulumi.CustomTimeouts( create=daily_snapshot_schedule, # Fake cron schedule for demonstration purposes ) ), create=snapshot_function(training_data_volume.id, "Daily AI Training Data Snapshot")) # Export the ID of the EBS Volume and the latest snapshot pulumi.export("training_data_volume_id", training_data_volume.id) pulumi.export("latest_snapshot_id", scheduled_snapshot.id)

    Before running the code above, you should ensure that your Pulumi CLI is configured for AWS access and that you have set up the correct region.

    As noted in the comments, Pulumi itself does not directly handle cron scheduling, but you can set up an AWS Lambda function, triggered by an Amazon CloudWatch Events rule set to your desired schedule, to create snapshots. The create_snapshot function is defined to show how you would create a snapshot resource, which you would call in the body of your Lambda function.

    For a full implementation, you will need to replace the conceptual scheduled_snapshot resource with actual code that deploys an AWS Lambda function and sets up the corresponding CloudWatch Events rule. This code would be more complex and would utilize additional Pulumi resources such as aws.lambda.Function and aws.cloudwatch.EventRule. If you want to proceed with an end-to-end solution that uses these resources, please let me know, and I can guide you through that process.