1. Hosting Static Machine Learning Model Files on GCP


    To host static machine learning model files on Google Cloud Platform (GCP), we'll create a storage solution using Google Cloud Storage (GCS). Google Cloud Storage provides a durable and highly available object storage service which is ideal for storing and serving static files, such as machine learning model artifacts.

    The following Pulumi program will achieve two primary objectives:

    1. Create a Google Cloud Storage bucket to hold the static model files.
    2. Upload the model files to the newly created storage bucket.

    For this program:

    • We will use the pulumi_gcp library to interact with GCP resources.
    • The machine learning model files are assumed to be in a local directory named model_files/. You will need to replace this with the actual path to your model files.

    Here's the complete Pulumi Python program that you can use to set up the hosting for your model files:

    import pulumi import pulumi_gcp as gcp # Create a Google Cloud Storage bucket to store the static model files. model_files_bucket = gcp.storage.Bucket("model-files-bucket", # Set the location to the appropriate region. location="US", # Optionally, define the bucket's access control. Here it's set to private. # You can change this based on your requirements. uniform_bucket_level_access=True, ) # Loop through the local machine learning model files and upload them to the bucket. # In this example, there are three model files. You can adjust the list based on your files. model_filenames = ["model.pb", "variables.data-00000-of-00001", "variables.index"] for filename in model_filenames: # Create a BucketObject resource for each file to upload it to the created bucket. gcp.storage.BucketObject(filename, bucket=model_files_bucket.name, source=pulumi.FileAsset(f"model_files/{filename}"), # You can set the content type based on your file type. content_type="application/octet-stream" ) # Export the bucket's self link URL, so you can access it later on. pulumi.export("bucket_self_link", model_files_bucket.self_link) # Export the URLs to the model files in the bucket for easy access. for filename in model_filenames: pulumi.export(f"{filename}_url", pulumi.Output.concat("https://storage.googleapis.com/", model_files_bucket.name, "/", filename))


    • The Bucket class from the pulumi_gcp.storage module is used to create a new storage bucket.
    • We set uniform_bucket_level_access to true to enforce that all access to the bucket's objects is controlled by IAM (Identity and Access Management) permissions. This setting is optional and ensures that the bucket permissions are uniformly controlled.
    • We iterate over a predefined list of filenames and create BucketObject resources for each, uploading them to the created bucket.
      • The source for each object is generated by the pulumi.FileAsset constructor. It points to the local file path where your model files are stored.
      • The content_type attribute is set to application/octet-stream, which is a general content type for binary data. For other file types, you may need to set the appropriate MIME type.
    • In the end, we export the self link of the bucket and URLs for the model files deployed in the bucket. These URLs can be used to access your static machine learning model files.

    To use this program:

    1. Install Pulumi and set up the GCP provider. Make sure you have the pulumi-gcp package installed in your Python environment.
    2. Replace model_files/ with the path to your actual machine learning model files.
    3. Run the program with pulumi up. This command will provision the GCP resources as defined in the program.
    4. Once the deployment is successful, Pulumi will print the output variables, including the bucket URL and the URLs of your machine learning model files.

    By following these steps, you'll have a secure and scalable way to host your static machine learning model files on GCP.