Enabling Web-Based ML Applications with S3 CORS

Question

Pulumi · Accepted Answer

Cross-Origin Resource Sharing (CORS) is a security feature that allows or restricts requests to web resources from different origins (domains, schemes, or ports). When building web-based machine learning (ML) applications, it might be necessary to fetch model data stored in an Amazon S3 bucket from a web application hosted on a different domain. Enabling CORS on the S3 bucket will allow web applications to interact with the stored data without violating the browser's same-origin policy.

In this context, to enable CORS for an S3 bucket using Pulumi, we will use the `aws.s3.BucketCorsConfigurationV2` resource from the Pulumi AWS package. This resource allows us to define the CORS rules for an S3 bucket.

Each CORS rule can contain the following settings:
- `allowedHeaders`: Specifies which headers are allowed in a pre-flight `OPTIONS` request through the `Access-Control-Request-Headers` HTTP header.
- `allowedMethods`: Identifies the HTTP methods that the domain/origin is allowed to execute.
- `allowedOrigins`: Specifies the origins that are allowed to access the bucket.
- `exposeHeaders`: Indicates which headers are safe to expose to the API of a CORS API specification.
- `maxAgeSeconds`: The time in seconds that the browser is allowed to cache the results of a pre-flight `OPTIONS` request.

Below is a Pulumi program written in Python that enables CORS on an existing S3 bucket. The program defines a CORS rule that allows `GET` requests from a specified origin:

```python
import pulumi
import pulumi_aws as aws

# An example S3 bucket's name - replace with the name of your bucket.
s3_bucket_name = "my-ml-models-bucket"

# Define a CORS rule for the S3 bucket
cors_rule = aws.s3.BucketCorsConfigurationV2("s3-bucket-cors",
    bucket=s3_bucket_name,
    cors_rules=[
        {
            "allowedHeaders": ["*"],  # Allows all headers
            "allowedMethods": ["GET"],  # Only allows GET requests
            "allowedOrigins": ["https://www.example-ml-app.com"], # Replace with the origin of your web app 
            "exposeHeaders": ["ETag"],  # The ETag header can be exposed to the web app
            "maxAgeSeconds": 3000  # Results can be cached for 3000 seconds
        }
    ])

# Exporting the name of the bucket for reference
pulumi.export("s3_bucket_name", s3_bucket_name)
```

What this script does is set up a CORS configuration on an S3 bucket with the provided name `my-ml-models-bucket`. The configuration includes a single CORS rule that enables `GET` requests from the `https://www.example-ml-app.com` origin. It allows for all headers in requests and exposes the `ETag` header in the response. It also allows browsers to cache the pre-flight response for 3000 seconds.

Please note that you'll have to replace `"https://www.example-ml-app.com"` with the domain that your web-based ML application is served from. The wildcard `"*"` used for `allowedHeaders` means that any headers can be used in the actual request, but for stricter security, you could specify only the headers you expect to receive and send.

For the program to work, ensure you have the AWS provider configured and you have the right permissions to modify S3 bucket configurations. If you don't have an existing bucket and need to create one as part of the script, you can add an `aws.s3.Bucket` resource above the CORS configuration in the script.

Remember, this configuration will affect how web clients interact with your S3 bucket, so it's essential to review your CORS rules for security and compliance with your application requirements.