1. AI Model Checkpointing with S3 BucketObjectV2


    To achieve AI model checkpointing using AWS S3, you will create an S3 bucket and store the model's checkpoint data as objects within this bucket. Checkpointing is an essential practice in machine learning that involves saving the state of a model at various stages during training. By using AWS S3, you can reliably and securely store these checkpoints that can be later used to resume training or to deploy the model.

    To create the necessary infrastructure for this task using Pulumi with Python, you'll perform the following steps:

    1. Create an S3 bucket where the checkpoint files will be stored.
    2. Define an S3 object (using the BucketObjectV2 resource) for storing the model checkpoints.

    Below is a Python program using Pulumi's AWS SDK that sets up an S3 bucket for model checkpointing:

    import pulumi import pulumi_aws as aws # Step 1: Create an S3 bucket to store the AI model checkpoints ai_model_checkpoints_bucket = aws.s3.Bucket('aiModelCheckpointsBucket', # The following properties such as versioning and server-side encryption # can be configured as needed for your use case versioning=aws.s3.BucketVersioningArgs( enabled=True, # To keep every version of an object in the same bucket ), server_side_encryption_configuration=aws.s3.BucketServerSideEncryptionConfigurationArgs( rule=aws.s3.BucketServerSideEncryptionConfigurationRuleArgs( apply_server_side_encryption_by_default=aws.s3.BucketServerSideEncryptionConfigurationRuleApplyServerSideEncryptionByDefaultArgs( sse_algorithm='AES256', # To encrypt objects at rest ), ), ) ) # Step 2: Define an S3 Object to store an AI model checkpoint # Note: You would typically upload a file related to your AI model's checkpoint in the "source" property. # For demonstration, we presume a 'checkpoint.tar.gz' file exists on your local machine. ai_model_checkpoint_object = aws.s3.BucketObjectv2('aiModelCheckpointObject', bucket=ai_model_checkpoints_bucket.id, # Referencing the bucket created above key='model-checkpoint.tar.gz', # The file name which will be used as the object key in S3 source=pulumi.FileAsset('path/to/your/local/checkpoint.tar.gz'), # Local checkpoint file to be uploaded acl='private', # Access control list - private means only the bucket and object owners have access storage_class='STANDARD', # Using "STANDARD" storage class for frequent access server_side_encryption='AES256', # To encrypt the object at rest ) # Export the bucket name and object key to access later pulumi.export('bucket_name', ai_model_checkpoints_bucket.id) pulumi.export('checkpoint_object_key', ai_model_checkpoint_object.key)

    In this program:

    • We first create an S3 bucket which will store the AI model checkpoint files. This bucket has versioning enabled to keep the history of checkpoints and server-side encryption for security.

    • We then create an S3 object (BucketObjectV2) within the bucket that represents a single checkpoint file. The file checkpoint.tar.gz is assumed to be the checkpoint you want to store. It is referenced via a local path (replace path/to/your/local/checkpoint.tar.gz with the path to your actual file).

    • Finally, we use pulumi.export to output the bucket's name and the checkpoint object's key. These values can be used to reference the checkpoint object, for example, to download the checkpoint or to programmatically reference the checkpoint in your machine learning application.

    To run this program, ensure that you have AWS credentials configured for Pulumi, either through the AWS CLI or by setting the appropriate environment variables. Also, make sure you have the Pulumi CLI installed and configured.

    Save this program to a file (e.g., main.py), and you can deploy your infrastructure by running pulumi up from your terminal in the directory where the file resides. Pulumi will execute the code and provision the resources in your AWS account. After confirming the changes, Pulumi provides outputs that can be used to interact with your S3 bucket and objects.