1. Object Storage for AI Training Data Sets with AWS S3


    To store AI training data sets, you can use Amazon Simple Storage Service (Amazon S3), which provides highly-scalable object storage. It is ideal for training AI models because it can handle large amounts of unstructured data, provides durability, and enables concurrent access to the data from any geographical location.

    In Pulumi, you use the aws.s3.Bucket resource to create a new S3 bucket. You can then use the aws.s3.BucketObject resource to upload individual data files or a aws.s3.BucketObjectv2 to upload multiple files. Additionally, if you need to set specific permissions, you might also consider using aws.s3.BucketPolicy to define access policies for your bucket.

    Step 1 - Define a new S3 bucket: This step involves creating an instance of the Bucket class, where the bucket parameter specifies the name of the bucket.

    Step 2 - Upload data sets to the S3 bucket: You can upload files to your S3 bucket using instances of the BucketObject or BucketObjectv2 class. Here, the source parameter specifies the local path to the file you want to upload, while the bucket parameter references to the ID of the bucket you created.

    Step 3 - Set a bucket policy (optional): If you want to fine-tune the permissions for your bucket, you may use the BucketPolicy class. This is where you would define who can access the bucket and what actions they can perform.

    Here's a basic Pulumi program in Python that accomplishes these tasks:

    import pulumi import pulumi_aws as aws # Step 1: Create a new S3 bucket for storing AI training data sets. ai_data_bucket = aws.s3.Bucket("ai_data_bucket", bucket="my-ai-data-bucket", acl="private", # Set the access control list to private. You can change this as needed. versioning=aws.s3.BucketVersioningArgs( enabled=True # Enable versioning for the items of the bucket (optional). ) ) # Step 2: Upload an example data set file to the S3 bucket. data_set_file = aws.s3.BucketObject("data_set_file", bucket=ai_data_bucket.id, # Reference to the bucket created earlier. key="training_data/example-dataset.csv", source=pulumi.FileAsset("path/to/your/dataset.csv") # Path to a local file to be uploaded. ) # Optional: Set an S3 bucket policy to manage access to the bucket. bucket_policy = aws.s3.BucketPolicy("bucket_policy", bucket=ai_data_bucket.id, policy=ai_data_bucket.id.apply(lambda id: json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": ["s3:GetObject"], "Effect": "Allow", "Resource": f"arn:aws:s3:::{id}/*", # Grant permissions to all objects in the bucket. "Principal": "*" # Open to everyone. In a real scenario, restrict this to specific IAM roles or accounts. }] })) ) # Export the name of the bucket pulumi.export("bucket_name", ai_data_bucket.id) # Export the URL of the uploaded data set file pulumi.export("data_set_file_url", data_set_file.bucket.apply(lambda bucket: f'https://{bucket}.s3.amazonaws.com/{data_set_file.key}'))

    In this code:

    • We create an S3 bucket called my-ai-data-bucket where the actual training data sets will be stored.
    • We add a data set by creating a BucketObject with the local path pointing to the data set file that needs to be uploaded.
    • We define a policy that, in this example, allows public read access to the objects in the bucket, but in a real-world application, you'd restrict this to specific principles.
    • We export the name of the S3 bucket and the URL of the dataset file for easy access.

    Remember to replace "path/to/your/dataset.csv" with the actual path to the CSV file you want to upload. The ACL and bucket policy should be adjusted based on your preferred access control requirements. If your files contain sensitive information, ensure you properly restrict access.