1. Serving Machine Learning Model Artifacts with DigitalOcean Spaces


    To serve machine learning model artifacts with DigitalOcean Spaces, you will need to perform a few steps:

    1. Create a new Spaces Bucket in DigitalOcean where your models will be stored.
    2. Upload your artifacts to the Spaces Bucket.
    3. Configure the Bucket with proper access controls so models can be securely accessed.

    In this Pulumi program, we'll first set up a DigitalOcean Spaces Bucket to store your machine learning model artifacts. We'll then demonstrate how to upload an example model artifact to the bucket.

    Here is the program that accomplishes these tasks:

    import pulumi import pulumi_digitalocean as digitalocean # Replace 'your-model-artifact-path' with the path to your model artifact file local_model_artifact_path = 'your-model-artifact-path' # Provide a unique name for your DigitalOcean Spaces Bucket and replace 'unique-bucket-name' spaces_bucket_name = 'unique-bucket-name' # Region where the bucket will be created. Choose the region closest to your users. spaces_bucket_region = 'nyc3' # Create a new Spaces Bucket spaces_bucket = digitalocean.SpacesBucket('my-spaces-bucket', name=spaces_bucket_name, acl='public-read', # This makes the bucket publicly readable. Use 'private' if you want it to be private. region=spaces_bucket_region, ) # Upload the model artifact to the Spaces Bucket model_artifact = digitalocean.SpacesBucketObject('model-artifact', bucket=spaces_bucket.name, key='model.tar.gz', # The name which the upload will be saved as in the bucket source=pulumi.FileAsset(local_model_artifact_path), # Path to the model artifact on your local filesystem content_type='application/gzip', # Content-type, change it if your artifact type is different acl='public-read', # Access control to allow public read access ) # Export the URL of the bucket and the model artifact pulumi.export('bucket_url', spaces_bucket.website_endpoint) pulumi.export('model_artifact_url', pulumi.Output.concat("https://", spaces_bucket.name, ".", spaces_bucket_region, ".digitaloceanspaces.com/", model_artifact.key))

    In this program, we first import the necessary Pulumi and DigitalOcean modules. We then set the path to your local model artifact and the unique name for the Spaces Bucket. Note that the name must be globally unique across all DigitalOcean Spaces.

    The SpacesBucket resource is created with an ACL (Access Control List) of 'public-read' to allow anyone to read the files, which is common for public assets. Change the acl to 'private' if you require the contents to only be accessible by authorized users or services.

    We then upload the model artifact file using SpacesBucketObject, specifying the bucket it will be stored in, the key (name it will have within the bucket), the source (a path to the local file), and the content_type. We also set the ACL for the object itself to 'public-read'. Again, adjust the ACL according to your privacy requirements.

    Lastly, we export the Spaces Bucket URL and the direct URL to the uploaded model artifact for easy access. These URLs are useful for accessing the model artifact directly or programmatically in applications or services that use the machine learning model.

    Remember to replace 'your-model-artifact-path', 'unique-bucket-name', and 'nyc3' with the actual path to your model artifact, your desired bucket name, and the region you'd like to use, respectively.