1. GPU-accelerated Deep Learning Training on GCP Compute Engine


    To create a GPU-accelerated deep learning training job on Google Cloud Platform (GCP) using Compute Engine with Pulumi, you need to create a GCP Compute Engine instance that is equipped with the necessary GPU accelerators.

    Below is a comprehensive guide that walks you through setting up a GCP Compute Engine instance with a GPU accelerator for deep learning tasks.

    First, we'll need to import the required Pulumi packages for GCP, construct the compute instance, attach the GPU accelerator, and then provide all necessary settings related to the machine type and the boot disk. In our case, we will select a machine type that is compatible with GPU-based operations, configure the boot disk to use a public deep learning image provided by Google, and specify the type and count of the GPU accelerator.

    Here's what we're doing step by step:

    1. Importing Pulumi's Google Native provider package.
    2. Creating a Compute Engine instance resource:
      • We specify the zone where the instance will be created.
      • We set up the machine type which complies with the requirements for GPU acceleration.
      • We attach a boot disk image which is pre-configured with deep learning frameworks.
      • We add the GPU accelerator to the guestAccelerators field by specifying its type and count.
      • We ensure that the Compute Engine service account has the necessary permissions to access other GCP services.

    Let's write the Pulumi program for your GPU-accelerated deep learning training:

    import pulumi import pulumi_google_native as google_native # Initialize the configuration. project = 'your-gcp-project' # Replace with your GCP project ID. zone = 'us-west1-b' # Replace with your GCP zone. machine_type = "n1-standard-4" # Replace with the machine type you want to use. gpu_type = "nvidia-tesla-k80" # Replace with the GPU type you want to use. gpu_count = 1 boot_disk_image = "projects/deeplearning-platform-release/global/images/family/common-cu113" # Deep learning image. # Create a GCP compute instance with the specified machine type, image, and GPU accelerator. compute_instance = google_native.compute.v1.Instance("dl-training-instance", project=project, zone=zone, name="dl-training-instance", machine_type=machine_type, tags=google_native.compute.v1.InstanceTagsArgs( items=["http-server", "https-server"], ), disks=[ google_native.compute.v1.InstanceDiskArgs( boot=True, initialize_params=google_native.compute.v1.InstanceDiskInitializeParamsArgs( source_image=boot_disk_image, ), ), ], guest_accelerators=[ google_native.compute.v1.InstanceGuestAcceleratorsArgs( accelerator_type=f"zones/{zone}/acceleratorTypes/{gpu_type}", accelerator_count=gpu_count, ), ], network_interfaces=[ google_native.compute.v1.InstanceNetworkInterfaceArgs( network="global/networks/default", ), ], can_ip_forward=True, ) # Export the instance name and IP address. pulumi.export('instance_name', compute_instance.name) pulumi.export('instance_ip', compute_instance.network_interfaces[0].network_i_p)

    To run this program:

    1. Replace 'your-gcp-project' with your actual GCP project ID.
    2. Replace 'us-west1-b' with the zone where you would like your instance to be created.
    3. Change 'machine_type' to the appropriate type that satisfies your specific GPU needs.
    4. Change 'gpu_type' to the type of GPU you wish to use (check GCP's documentation for available GPU types).
    5. Set up GCP credentials and authenticate the Pulumi CLI with GCP.

    Once the Pulumi script is ready, you can run it using the Pulumi CLI to provision the resources in your GCP account. The pulumi up command will apply the configuration, and after successful deployment, it will output the instance name and IP address.

    Remember that GPU resources are often subject to quota limitations, so ensure that your GCP project has the quota to provision the desired GPU type and count. If not, you might need to request a quota increase from the GCP console.