Exposing AI Research Papers and Datasets via Public S3 Bucket

Question

Pulumi · Accepted Answer

To expose AI Research Papers and Datasets via Public S3 Bucket on AWS using Pulumi, we will need to create an S3 Bucket and then configure it for public access. We will use a `Bucket` to store the data and a `BucketPolicy` to set the necessary permissions for public read access.

Here is a step-by-step explanation of what we are going to do:

1. **Create an S3 Bucket**: This will be the storage container for your AI Research Papers and Datasets.
2. **Set up a Bucket Policy**: This specifies who is allowed to access the bucket and what permissions they have. In this case, we'd allow public read access to the contents of the bucket.
3. **Export the S3 Bucket URL**: For convenient access, we'll output the website endpoint URL where the data can be accessed.

Below is the Pulumi Python program that accomplishes these steps:

```python
import json
import pulumi
import pulumi_aws as aws

# Step 1: Create an S3 Bucket
bucket = aws.s3.Bucket('ai-research-data',
    acl='public-read', # Grants read permission to everyone.
    website=aws.s3.BucketWebsiteArgs(
        index_document='index.html', # Typical name for the homepage document.
    ))

# Step 2: Set up a Bucket Policy to allow public read access to the bucket contents
# Note: It's critical to understand the implications of allowing public access to S3 data.
# The following policy grants read-only access to all objects in the bucket to anyone on the Internet.
bucket_policy = aws.s3.BucketPolicy('bucket-policy',
    bucket=bucket.id, # Associate this policy with the bucket we created earlier.
    policy=pulumi.Output.all(bucket.arn).apply(lambda arn: json.dumps({
        'Version': '2012-10-17',
        'Statement': [{
            'Effect': 'Allow',
            'Principal': '*',
            'Action': ['s3:GetObject'],
            'Resource': [f'{arn}/*']  # Refers to all objects in the bucket.
        }]
    })))

# Step 3: Export the S3 Bucket URL so that you can easily access it from the browser.
pulumi.export('bucket_endpoint', pulumi.Output.concat('http://', bucket.website_endpoint))
```

This Pulumi program uses the `aws.s3.Bucket` resource to create a new S3 bucket and the `aws.s3.BucketPolicy` resource to attach a policy to the bucket. The policy we have applied allows public read access using the `'public-read'` ACL option and grants consumable permissions via the bucket policy JSON.

**Important**: Please be aware that making digital assets available publicly can lead to unexpected charges if widely accessed. Always monitor your AWS usage for unexpected costs and consider using AWS Budgets to create alarms for when your costs exceed thresholds that you define.

Before running any Pulumi commands, make sure your [AWS CLI is configured correctly](https://www.pulumi.com/docs/intro/cloud-providers/aws/setup/) and the credentials have the necessary permissions.

After having this program ready, the typical flow to deploy your infrastructure with Pulumi would be:
1. Save this code in a file with a `.py` extension, for example, `s3_website.py`.
2. Run `pulumi up` to preview and deploy the changes. It will show you a summary of the infrastructure you are about to create.
3. Confirm the deployment by selecting `yes`.

To access your data, navigate to the S3 website endpoint URL outputted by Pulumi after deployment._exempt