1. High-Performance AI Compute with GCP's GPU Acceleration


    To set up a high-performance AI compute instance with GPU acceleration on Google Cloud Platform (GCP) using Pulumi, we will use the google-native.compute/alpha.Instance resource. This resource allows us to create a virtual machine (VM) on GCP's Compute Engine, where we can specify the machine type and attach GPU accelerators for high-performance workloads.

    GPUs can significantly accelerate compute-intensive tasks such as machine learning and data processing. When selecting a machine type and GPU type, it's important to consider the specific requirements of your workload, such as the amount of memory, CPU power, and the type of GPU needed.

    For the purposes of this explanation, I'll assume that you have already set up Pulumi and authenticated with Google Cloud.

    Here's a step-by-step Pulumi program in Python that will create a high-performance AI compute instance with GPU acceleration on GCP:

    1. Import necessary modules from Pulumi.
    2. Specify the machine type that supports attaching GPUs.
    3. Include the GPU accelerator type and quantity you want to attach to your instance.
    4. Configure the disk and network settings of the instance.
    5. Create the VM instance with the specified settings.

    Let's write the Pulumi program:

    import pulumi import pulumi_google_native as google_native # The name of the GCP project and the zone where you want to launch the VM. project = 'your-gcp-project-id' zone = 'us-central1-a' # Define the machine type and GPU accelerator details. machine_type = 'n1-standard-8' # Example machine type gpu_accelerator_type = 'nvidia-tesla-k80' # Example GPU type gpu_count = 1 # Number of GPUs attached to the instance # Configure the VM instance with the specified machine type and attach the GPU. instance = google_native.compute.alpha.Instance( "ai-compute-instance", project=project, zone=zone, name="ai-compute-instance", machine_type=f"zones/{zone}/machineTypes/{machine_type}", # Reference the machine type for the specific zone disks=[{ "boot": True, "autoDelete": True, "initializeParams": { "sourceImage": "projects/debian-cloud/global/images/family/debian-10", # Example image "diskSizeGb": "50" # Disk size } }], network_interfaces=[{ "network": "global/networks/default" # Using the default network }], guest_accelerators=[{ "acceleratorType": f"zones/{zone}/acceleratorTypes/{gpu_accelerator_type}", # GPU type reference "acceleratorCount": gpu_count, # Number of GPUs }], tags={"items": ["http-server", "https-server"]} # Optionally, add network tags to your instance ) # Export the Compute Engine instance details. pulumi.export('instance_name', instance.name) pulumi.export('instance_machine_type', instance.machine_type) pulumi.export('instance_zone', instance.zone)

    In this program:

    • We configure an instance that has a boot disk with a debian-10 image and a size of 50GB.
    • The instance is attached to the default network. You may want to customize the network to meet the needs of your specific use case.
    • We specify the desired GPU type and the number of GPUs.
    • The guest_accelerators property is where we define the GPU details for our instance.
    • At the end of the script, pulumi.export statements provide output values that can be helpful for reference or to use in subsequent Pulumi programs or stacks.

    Make sure to replace your-gcp-project-id with your actual GCP project ID and choose the appropriate machine_type, gpu_accelerator_type, and zone for your workload and location preferences.

    This is a simple VM setup. Depending on your needs, you might want to configure additional properties like different disk types, other GCP services, or advanced VM configurations. You can find more information about the Instance resource in the Pulumi documentation for the google-native compute alpha instance.