1. Scalable ML Model Inferencing Backend with GCP Redis


    To create a scalable ML Model Inferencing Backend on Google Cloud Platform (GCP) that utilizes Redis for caching, we would utilize the following resources:

    1. GCP Compute Engine Instances: These will handle the actual ML model inferencing. You could use custom machine types tailored to the computation needs of your machine learning model, possibly utilizing GPUs for faster processing.

    2. GCP Redis Instance: This managed Redis service will be used to cache frequent inferencing requests, which can reduce latency and the computational load on the ML model servers.

    3. GCP Load Balancer (Backend Service): To distribute incoming inferencing requests across your compute engine instances.

    4. GCP Cloud Storage Bucket: To store model files that can be loaded by the compute instances.

    When a user sends a request to the backend for inferencing, the system will first check if the inference result is available in Redis, and if not, it will process the request on the ML Compute Engine instance, store the result in Redis, and return it to the user.

    Below is a Pulumi program in Python that defines the resources needed to set up this backend:

    import pulumi import pulumi_gcp as gcp # Define the GCP Region and project you wish to deploy resources into. gcp_region = 'us-central1' gcp_project_id = 'my-gcp-project' # Create a GCP Redis instance for caching inference results redis_instance = gcp.redis.Instance("ml-infer-redis-instance", tier="STANDARD_HA", region=gcp_region, memory_size_gb=1, authorized_network="default") # Define the Compute Engine configuration for the ML inference servers machine_type = "n1-standard-4" # Customize as per your ML model requirements zone = "us-central1-a" # Ensure the zone is in the same region as your Redis instance # Create an instance template that the Managed Instance Group will use. # The template would be pre-configured with the necessary environment to run your ML model. instance_template = gcp.compute.InstanceTemplate("ml-infer-template", region=gcp_region, machine_type=machine_type, disks=[gcp.compute.InstanceTemplateDiskArgs( source_image="image-id", # Replace with your ML model image-id auto_delete=True, boot=True )]) # Create a Managed Instance Group from the instance template to ensure high availability and scalability. managed_instance_group = gcp.compute.InstanceGroupManager("ml-infer-mig", base_instance_name="ml-infer", instance_template=instance_template.id, zone=zone, target_size=2 # Start with 2 instances and scale as needed ) # Set up a Load Balancer to distribute the inference requests across the instances # First, create a Health Check to ensure traffic is only sent to healthy instances http_health_check = gcp.compute.HealthCheck("http-health-check", http_health_check=gcp.compute.HealthCheckHttpHealthCheckArgs( port=80, request_path="/" )) # Second, create a backend service that uses the health check backend_service = gcp.compute.BackendService("ml-infer-backend-service", health_checks=[http_health_check.id], backends=[gcp.compute.BackendServiceBackendArgs( group=managed_instance_group.instance_group )], load_balancing_scheme="EXTERNAL") # Third, create a URL map to direct the incoming requests to the backend service url_map = gcp.compute.URLMap("url-map", default_service=backend_service.id) # Fourth, create a target HTTP proxy to route requests to the URL map http_proxy = gcp.compute.TargetHttpProxy("http-proxy", url_map=url_map.id) # Finally, create a global forwarding rule to bind the IP, target HTTP proxy, and port range together forwarding_rule = gcp.compute.GlobalForwardingRule("forwarding-rule", target=http_proxy.id, port_range="80") # Exporting the IP address of the Load Balancer to be able to make requests to the ML Inference API pulumi.export('ml_infer_ip', forwarding_rule.ip_address)


    The program begins by defining a Redis instance using the pulumi_gcp.redis.Instance resource that is designed for caching. The Redis tier, region, and memory set determine the specifications of your caching layer and can be adjusted based on the load you anticipate.

    Next, we create Compute Engine resources including an InstanceTemplate for defining the ML server's configuration and an InstanceGroupManager for scalability and high availability. This setup allows your ML backend to handle increases in request load by scaling the number of instances automatically.

    For health checks and load balancing, a HealthCheck, BackendService, URLMap, TargetHttpProxy, and GlobalForwardingRule are set up to handle traffic routing, ensuring requests are evenly distributed among healthy instances.

    The forwarding rule's IP is exported so you and your clients know where to send requests for your ML inferencing service.

    Things to note:

    • Replace "image-id" with the real image ID of the Compute Engine instances pre-configured with your ML model environment.
    • The backend service load-balancing scheme is set to EXTERNAL since you might be exposing the service over the Internet.
    • By default, the load balancer will route traffic to port 80. Make sure your inferencing service listens on this port, or change it as needed.
    • In an actual deployment, you would need to set up firewall rules to control the traffic flow, which is not included in this example for the sake of brevity.

    For a production system, you would also have your ML model files uploaded to GCP Cloud Storage, and the instances would be configured to pull the latest model files during startup or via a continuous deployment setup.

    Feel free to modify and expand this example to better fit your specific requirements and existing infrastructure.