1. Scalable GPU Instances for Deep Learning


    To create scalable GPU instances for Deep Learning, we will use a cloud provider that offers GPU-enabled virtual machines, such as Google Cloud Platform (GCP) or Amazon Web Services (AWS). Both of these providers have specific instances that are optimized for compute-intensive tasks like machine learning and deep learning.

    We will write a Pulumi program in Python that provisions GPU instances in Google Cloud using the gcp.compute.Instance resource. We will also leverage the gcp.compute.InstanceTemplate to create a template that can be used to scale the number of instances easily. The instance template will define the type of machine, the image to use, the disk sizes, and, importantly, the GPU type and count which are crucial for deep learning workloads.

    We will set up a Google Compute Engine instance with an attached NVIDIA T4 GPU, which is commonly used for deep learning tasks. To scale the instances, you would typically set up an Instance Group based on the instance template and use the autoscaler to adjust the number of instances based on the workload.

    Here's how to write the Pulumi program:

    1. Import Pulumi's Google Cloud package to access the necessary resources.
    2. Set up a network and a subnetwork to host our compute instances.
    3. Define the Instance Template with desired properties, including the machine type and the GPU specification.
    4. Define a Managed Instance Group based on the instance template to automate the creation of instances and manage scaling.

    Let's begin with the code:

    import pulumi import pulumi_gcp as gcp # Replace these variables with your own specific settings project = 'your-gcp-project' zone = 'us-central1-a' machine_type = 'n1-standard-4' # example machine type gpu_type = 'nvidia-tesla-t4' gpu_count = 1 # Set up a network and subnetwork for the Compute Engine instances network = gcp.compute.Network("gpu-network", auto_create_subnetworks=False) subnetwork = gcp.compute.Subnetwork("gpu-subnetwork", network=network.id, ip_cidr_range="", region=zone) # Create an instance template with GPU specs for Deep Learning instance_template = gcp.compute.InstanceTemplate("dl-instance-template", description="Instance template for Deep Learning with GPU", properties=gcp.compute.InstanceTemplatePropertiesArgs( machine_type=machine_type, scheduling=gcp.compute.InstanceTemplateSchedulingArgs( on_host_maintenance="TERMINATE", # required for instances with GPU automatic_restart=False, ), network_interfaces=[gcp.compute.InstanceTemplateNetworkInterfaceArgs( network=network.id, subnetwork=subnetwork.id, )], disks=[gcp.compute.InstanceTemplateDiskArgs( auto_delete=True, boot=True, initialize_params=gcp.compute.InstanceTemplateDiskInitializeParamsArgs( image= "projects/deeplearning-platform-release/global/images/family/common-cu116" # example Deep Learning image ), )], guest_accelerators=[gcp.compute.InstanceTemplateGuestAcceleratorArgs( type=gpu_type, count=gpu_count, )], )) # Finally, we set up a Managed Instance Group managed_instance_group = gcp.compute.InstanceGroupManager("gpu-instance-group", base_instance_name="dl-instance", instance_template=instance_template.id, target_size=1, # initially set to 1; can use an autoscaler to adjust zone=zone, ) # Export the instance group manager name pulumi.export("instance_group", managed_instance_group.id)

    This Pulumi program begins by importing Pulumi's GCP package. We define a new virtual network and subnetwork to place our instances in. Then, we create an instance template specifying the machine type, instance image, and the GPU requirements. Finally, we create a Managed Instance Group from the template. This group handles the deployment and scaling of the instances. The target_size is initially set to 1, but you can attach an autoscaler to this instance group to adjust the instance count based on specific metrics like CPU utilization.

    Please replace project, zone, machine_type, gpu_type, image in the initialize_params, and other variables with your own specific settings.

    This is a basic setup for a scalable GPU instance cluster tailored for deep learning tasks. Depending on your specific needs, you might need to add additional configurations like custom startup scripts, specific disk sizes, or more advanced network settings.

    Remember that when working with GPU instances, you must adhere to the provider’s specifications for attaching GPUs to instances, like the necessary host maintenance settings depicted in the scheduling argument in the InstanceTemplate.