Versioned Machine Learning Model Storage with AWS S3

Question

Pulumi · Accepted Answer

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:

```python
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

```

### Explanation

- 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.