1. Versioned Machine Learning Model Storage with AWS S3


    Storing machine learning models in a versioned manner is crucial for tracking experiments, managing model updates, and ensuring reproducibility in your machine learning workflow. AWS S3 buckets can be configured to maintain versioning information for each object stored in them, making it a great choice for versioned storage of machine learning models.

    Below is a Pulumi program in Python that demonstrates how to create an AWS S3 bucket with versioning enabled. We will also include a way to upload a machine learning model to this bucket.

    First, we will import the necessary Pulumi AWS SDK. Then, we will create an S3 bucket with the versioning configuration enabled. After that, we will upload a dummy model file as an example; in a real-world scenario, this would be your trained machine learning model.

    Here is the complete Pulumi program that accomplishes this:

    import pulumi import pulumi_aws as aws # Create an AWS S3 bucket with versioning enabled versioned_ml_model_bucket = aws.s3.Bucket("versioned-ml-model-bucket", versioning=aws.s3.BucketVersioningArgs( enabled=True ) ) # Example model file (In real-world scenarios, replace 'path-to-model' with your actual model file path) model_file_path = "path-to-model/model.pkl" # Upload the machine learning model to the S3 bucket as an object model_file = aws.s3.BucketObject("ml-model-object", bucket=versioned_ml_model_bucket.id, key="model.pkl", # The key is the 'filename' that will be used to reference the object in the bucket source=pulumi.FileAsset(model_file_path) ) # Export the URL of the uploaded model file pulumi.export("model_bucket_url", versioned_ml_model_bucket.website_endpoint.apply( lambda endpoint: f"http://{endpoint}/model.pkl") ) # Run pulumi up to deploy this infrastructure and pulumi destroy to tear it down


    • We initialize the Pulumi AWS SDK which provides a way to interact with AWS services.
    • We then declare a new S3 bucket resource using aws.s3.Bucket. In the configuration we specify an argument versioning which accepts aws.s3.BucketVersioningArgs, setting enabled to True. This action enables versioning on the S3 bucket, which means every change to an object will maintain a distinct version.
    • The model.pkl file is represented by aws.s3.BucketObject. We set bucket to the id of the S3 bucket we created earlier. This ensures that the object will be created inside this bucket. The key argument is the name that will be assigned to the model in the bucket, and the source argument points to our local model file using pulumi.FileAsset.
    • Finally, we export the URL of the model file. Here we derive the S3 bucket's endpoint and append the model file's key to it. Note that the actual URL might differ, and you'll often use presigned URLs for private buckets, or host the model behind a CDN like AWS CloudFront for faster and secure access.

    Please replace 'path-to-model/model.pkl' with the actual path to your machine learning model file before running the program.

    To deploy this configuration, you would execute pulumi up in your CLI after setting up your Pulumi stack, which initializes your cloud resources as per the Pulumi program. Using pulumi destroy, you can remove all resources if needed.

    Remember to have AWS credentials configured for the Pulumi AWS provider, which typically involves setting AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY as environment variables or through configuration files.