1. Custom Machine Learning Environment Setup on GCP Compute Instances


    To set up a custom Machine Learning (ML) environment on Google Cloud Platform (GCP) using Compute Instances, we'll need to create an instance with the necessary configurations. This usually includes selecting a machine type with enough computational power (CPUs, GPUs, memory) and installing ML frameworks like TensorFlow, PyTorch, or others.

    In this Pulumi program, we'll create a GCP Compute Instance with the following characteristics ideal for an ML environment:

    1. A predefined machine type suitable for ML workloads.
    2. A boot disk image with a common ML environment installed, such as a deep learning VM image provided by GCP.
    3. A GPU accelerator attached to the instance for computation-intensive tasks.

    The gcp.compute.Instance Pulumi resource is used to create and manage a VM instance in GCP. We specify the machine type, image, and hardware accelerators within this resource's configuration.

    Here is a basic Pulumi Python program to set up a GCP Compute Instance tailored for ML tasks:

    import pulumi import pulumi_gcp as gcp # Initialize a Pulumi project and config for Google Cloud settings. config = pulumi.Config() project = config.require("project") zone = config.require("zone") # Define a GCP Compute Instance with ML-specific configurations ml_instance = gcp.compute.Instance( "ml-instance", machine_type="n1-standard-8", # Example machine type, can be adjusted to your needs boot_disk=gcp.compute.InstanceBootDiskArgs( initialize_params=gcp.compute.InstanceBootDiskInitializeParamsArgs( image="projects/deeplearning-platform-release/global/images/family/common-cu113" # A common ML image ), ), hardware_accelerator=gcp.compute.InstanceHardwareAcceleratorArgs( type="nvidia-tesla-k80", # Adjust the accelerator type according to your needs count=1, ), zone=zone, tags=["ml", "gpu"], # Custom tags can help identify the instance purpose and characteristics network_interfaces=[ gcp.compute.InstanceNetworkInterfaceArgs( network="default", access_configs=[gcp.compute.InstanceNetworkInterfaceAccessConfigArgs( # Configures this instance to have a public IP, remove if not needed )] ) ], service_account=gcp.compute.InstanceServiceAccountArgs( email="default", scopes=["https://www.googleapis.com/auth/cloud-platform"], ), # You can also add a startup script to further configure the instance on boot metadata_startup_script="echo 'Starting Machine Learning Environment Setup'", project=project ) # Export the instance name and external IP for reference pulumi.export("instance_name", ml_instance.name) external_ip = ml_instance.network_interfaces[0].access_configs[0].nat_ip pulumi.export("instance_external_ip", external_ip)

    In this code:

    • We define a VM instance named ml-instance.
    • We use n1-standard-8 as a placeholder machine type, which can be replaced based on the computation requirements. For ML applications, you may need a machine with more CPUs, memory, or specialized hardware such as GPUs.
    • We initialize the boot disk with a common ML environment image. GCP offers images that come with popular ML frameworks and libraries pre-installed.
    • We attach an NVIDIA Tesla K80 GPU to the instance. Depending on the workload, you could choose a different accelerator type.
    • Network interfaces and service accounts are configured to provide network access and set the necessary permissions.
    • A startup script is included in the metadata, which allows for further customizations or installations when the instance boots up.

    Remember to replace the placeholders with your actual project and zone settings in the Pulumi configuration. The GCP project ID and zone should be configured before running this program. You can do this with Pulumi configuration commands such as pulumi config set gcp:project PROJECT_ID and pulumi config set gcp:zone ZONE.

    Lastly, we export the instance name and external IP address. These can be useful for connecting to and managing the instance after it's been created.

    Please note that when working with GPUs and specialized machine types on GCP, you might need to request quota increases from Google, as they have limits on the usage of these resources.