1. Publishing Machine Learning Benchmark Results on S3 Website


    To publish machine learning benchmark results on an S3 website, you would typically follow these steps:

    1. Create an S3 bucket: This acts as the storage container for your website's files.

    2. Configure the bucket for website hosting: You must set the appropriate properties on the bucket to serve a website, which includes specifying the index document and optionally the error document.

    3. Set up permissions: Ensure that the bucket's permissions allow public read access to the website files, but maintain secure access control over administrative actions.

    4. Upload the benchmark results: You can upload static files, such as HTML, CSS, JavaScript, and your machine learning benchmark results, which could be in various formats like CSV, JSON, or visualizations in PNG/JPG.

    5. Use Route 53 (optional): If you have a custom domain name, you can use Amazon Route 53 to set up a more user-friendly URL for your website. Otherwise, you'll use the endpoint provided by S3.

    6. Enable versioning and logging (optional): For better management and tracking of changes, you can enable versioning and logging for the S3 bucket.

    Let's write a Pulumi program in Python which accomplishes these steps:

    import pulumi import pulumi_aws as aws # Step 1: Create an S3 bucket for storing the benchmark results and hosting a static website. bucket = aws.s3.Bucket('benchmark-results', # Step 2: Configure the bucket for website hosting. website=aws.s3.BucketWebsiteArgs( index_document='index.html', ), ) # Step 3: Set up bucket permissions to allow public access to the website files. bucket_policy = aws.s3.BucketPolicy("bucket-policy", bucket=bucket.id, policy=bucket.arn.apply(lambda arn: json.dumps({ "Version": "2012-10-17", "Statement": [{ "Sid": "PublicReadForGetBucketObjects", "Effect": "Allow", "Principal": "*", "Action": ["s3:GetObject"], "Resource": [f"{arn}/*"] }] })) ) # Step 4: Upload your benchmark results. # For example, say you have a 'benchmark-results.html' file containing the results. # You would upload this file to the S3 bucket. content_html = """<html> <head><title>Benchmark Results</title></head> <body><h1>Machine Learning Benchmark Results</h1></body> </html>""" index_html = aws.s3.BucketObject("index", bucket=bucket.id, key="index.html", content=content_html, content_type="text/html", ) # Step 5 (optional): Use Route 53 to set up a more user-friendly URL for your website. # To be implemented as required. Route 53 configuration isn't covered in this specific example. # Export the website endpoint for easy access. pulumi.export('website_url', pulumi.Output.concat("http://", bucket.website_endpoint)) # Note: For steps 6 (versioning and logging) and other optional configurations, additional Pulumi resources can be provisioned as needed.

    In this program, we:

    • Defined an S3 bucket configured for website hosting.
    • Created a policy that permits public read access to the bucket's contents.
    • Uploaded an example HTML file representing the machine learning benchmark results.
    • Exported the website endpoint URL, which you can visit to view the published benchmark results.

    Please ensure that you have the AWS Pulumi provider configured correctly with your credentials and desired region. After writing this Pulumi program in a file, say __main__.py, run pulumi up from your terminal in the same directory as the file to launch your S3 website.

    Remember, regularly update the content on your S3 bucket with new benchmark results as needed and use the URL exported by the Pulumi program to access the website.