1. Real-time Model Inference with GCP Cloud Functions


    Creating a real-time model inference system on Google Cloud Platform (GCP) can be done effectively using Cloud Functions. Cloud Functions allow you to run serverless, event-driven code that responds to various events. In this case, you can deploy a machine learning model and use Cloud Functions to perform inference in real-time as data becomes available, say, from HTTP requests or Pub/Sub messages.

    For this scenario, you would typically:

    1. Have a machine learning model that is ready for inference.
    2. Use Google Cloud Functions to deploy that model and perform the real-time inference.
    3. Optionally, trigger the Cloud Function based on events, such as a message on a Pub/Sub topic.

    Below is a simplified Pulumi program in Python, which will create a Google Cloud Function that can be used for real-time model inference. In a real-world scenario, the function code would load the machine learning model and perform the inference based on incoming data.

    import pulumi import pulumi_gcp as gcp # Define the name of the project and location for the resources project = 'your-gcp-project-id' location = 'us-central1' # You can choose a different region if needed # Content of the main.py file for GCP Cloud Function # This is where you would load your ML model and define the inference process. function_code = """ def model_inference(request): # Load your machine learning model here # Get the data for inference from `request` data = request.get_json() # Perform inference # For example: prediction = model.predict(data) # Return the inference results # For example: return prediction """ # Define GCP Cloud Function resource model_inference_function = gcp.cloudfunctions.Function( "model-inference-function", entry_point='model_inference', # The name of the function inside your Python file runtime='python39', # You can choose the runtime that fits your model and code project=project, region=location, source_archive_bucket=gcp.storage.Bucket( "source-bucket", project=project, ).name, source_archive_object=gcp.storage.BucketObject( "source-archive", bucket=gcp.storage.Bucket("source-bucket").name, source=pulumi.FileArchive(".", content=lambda: function_code), project=project, ).name, trigger_http=True, # This allows direct invocation through HTTP available_memory_mb=256 # Compute resources available for the function, tailor as needed ) # Export the trigger URL so you know how to invoke the function pulumi.export('model_inference_function_url', model_inference_function.https_trigger_url)


    In the above program:

    • We define Python code that would be used as the Cloud Function. We are simulating the presence of an ML model and data acquisition from an HTTP request, within the model_inference function.
    • We create a new pulumi_gcp.cloudfunctions.Function (docs) which defines the Cloud Function.
    • We specify an entry point (entry_point), which corresponds to the Python function we defined for handling requests.
    • The runtime is the environment in which the function runs; we indicated Python 3.9 here.
    • The Cloud Function is configured to be triggered via HTTP requests (trigger_http=True), and you can scale this function by adjusting the amount of memory made available to it.
    • We are using a couple of GCP storage products to store the function's source code: gcp.storage.Bucket and gcp.storage.BucketObject, creating a new storage bucket and uploading the function's code as a zip file.
    • We use the pulumi.FileArchive to create an archive of the function's local directory, which in this case is dynamically created from the function_code variable.
    • Finally, we export the URL that can be used to trigger the Cloud Function via HTTP (model_inference_function.https_trigger_url), making it easy to call the function from other services or applications.

    In a real use case, your Cloud Function would be more complex, including properly packaging the ML model along with any necessary pre-processing and post-processing, managing dependencies, and handling errors or edge cases in the inference pipeline.

    Keep in mind that model inference functions can grow in complexity based on the type of model, the input and output requirements, and how the function integrates with other cloud services. You would need to manage those aspects according to your specific needs.