1. Private Access to S3 for AI Dataset Storage


    In order to set up private access to an Amazon S3 bucket for storing AI datasets, we need to create an S3 bucket and configure it so that it's not publicly accessible. After that, we'll set up an access policy to ensure only certain principals (like an EC2 instance or a certain IAM user) can access this S3 bucket. Finally, we'll demonstrate how to create an S3 object within this bucket to store an AI dataset.

    Here is what we will do in this Pulumi program:

    1. Import the required AWS components.
    2. Create an S3 bucket with public access turned off.
    3. Apply a bucket policy that restricts access to the bucket to specific IAM roles or users.
    4. Optionally, we will create an S3 object to represent the AI dataset file.

    Pulumi Program

    import pulumi import pulumi_aws as aws # Create an AWS S3 bucket for AI datasets that is not publicly accessible ai_datasets_bucket = aws.s3.Bucket('aiDatasetsBucket', acl='private', # This ensures the bucket is not publicly readable or writable # Define more bucket properties here if necessary, such as versioning, logging, etc. ) # Create an S3 bucket policy to restrict access ai_datasets_bucket_policy = aws.s3.BucketPolicy('aiDatasetsBucketPolicy', bucket=ai_datasets_bucket.id, # Reference to the S3 bucket created above # This is a JSON policy document that outlines who can access this bucket and how policy=ai_datasets_bucket.id.apply(lambda id: f"""{{ "Version": "2012-10-17", "Statement": [{{ "Effect": "Allow", "Principal": {{"AWS": "arn:aws:iam::ACCOUNT_ID:user/USERNAME"}}, # Replace with the correct ARN for your IAM user or role "Action": "s3:*", "Resource": [ "arn:aws:s3:::{id}", "arn:aws:s3:::{id}/*" ] }}] }}""") ) # (Optional) Create an S3 object to store an AI dataset ai_dataset_object = aws.s3.BucketObject('aiDatasetObject', bucket=ai_datasets_bucket.id, # Reference to the S3 bucket created above key='dataset.csv', # Name of the file as it will appear in the bucket source=pulumi.FileAsset('./path/to/your/dataset.csv'), # Path to a local file to be uploaded # Additional object properties here if necessary, such as server-side encryption, content type, etc. ) # Export the bucket name and the object URL of the dataset file pulumi.export('bucket_name', ai_datasets_bucket.id) pulumi.export('dataset_object_url', ai_dataset_object.bucket.apply(lambda bucket: f"s3://{bucket}/{ai_dataset_object.key}"))

    This program starts by importing the pulumi core and pulumi_aws module, which contains classes and functions to work with AWS resources. We define an S3 bucket named aiDatasetsBucket with private access control to block public access. A bucket policy is applied to allow access only to a specified IAM user or role.

    Optionally, we create an S3 object named aiDatasetObject which could be a dataset in a CSV format (dataset.csv). This part of the script assumes you have a file called 'dataset.csv' in your local directory specified by ./path/to/your/dataset.csv.

    Lastly, we export the bucket name and the accessible URL of this AI dataset object so that you can easily locate these resources in the AWS console or through the Pulumi CLI.

    Please replace ACCOUNT_ID and USERNAME with your AWS account ID and an IAM username or role that will have access to this S3 bucket. Also, follow the path to your actual dataset file if you're going to upload it.

    Before running this Pulumi program, ensure you have the AWS CLI set up with the proper configurations and credentials. Also, ensure you have Pulumi installed and set up to deploy to your AWS account.