1. GPU-Accelerated Computing Instances for Training LLMs on OpenStack.


    When you want to create GPU-accelerated computing instances for training large language models (LLMs), you need to provision the necessary cloud resources that support GPU workloads. However, it is important to note that OpenStack is not directly supported by Pulumi as a cloud provider. Pulumi works with major cloud providers like AWS, Azure, GCP, and several others, but it does not have a dedicated provider for OpenStack at the time of writing this.

    However, if you are using a cloud service that is built on top of OpenStack and exposes a compatible API for managing resources, you might be able to manage it using one of the generic Pulumi providers such as the OpenStack Terraform provider through the Pulumi Terraform Bridge.

    For the purpose of explanation, let's assume you are using Google Cloud Platform (GCP) which is supported by Pulumi and offers GPU-accelerated compute instances through its Compute Engine service.

    To create a GPU-accelerated compute instance on GCP, you would typically use the pulumi_gcp package and specifically the Instance class from the compute module to provision a virtual machine with the desired GPU type. You would need to ensure that the machine type you select is compatible with GPUs and then attach the GPU as an accelerator to the instance.

    Here's a Pulumi program in Python that demonstrates how to create a GPU-accelerated compute instance on GCP for our hypothetical example:

    import pulumi import pulumi_gcp as gcp # Set the zone for the resources, e.g., 'us-west1-b' zone = 'us-west1-b' # Choose the machine type, e.g., 'n1-standard-1' machine_type = 'n1-standard-1' # Choose the GPU type and the number of GPUs, e.g., 'nvidia-tesla-v100' gpu_type = gcp.compute.get_accelerator_type(name="nvidia-tesla-v100", zone=zone) # Provision a new GCP compute instance with an attached GPU for accelerated computing tasks. gpu_accelerated_instance = gcp.compute.Instance("gpu-accelerated-instance", machine_type=machine_type, zone=zone, boot_disk=gcp.compute.InstanceBootDiskArgs( initialize_params=gcp.compute.InstanceBootDiskInitializeParamsArgs( image="gcp-image-with-cuda" # A hypothetical GCP image with CUDA installed for GPU workloads. ), ), # Attach the desired GPU as a guest accelerator. guest_accelerators=[gcp.compute.InstanceGuestAcceleratorArgs( type=gpu_type.id, count=1 # The number of GPUs to attach. )], network_interfaces=[gcp.compute.InstanceNetworkInterfaceArgs( network="default", # Assuming a default VPC network is to be used. access_configs=[gcp.compute.InstanceNetworkInterfaceAccessConfigArgs( # Access configuration to assign a public IP. Omit this if you do not need external access. nat_ip=gcp.compute.address.Address("instance-access-ip", region=zone[:-2]).address )] )] ) # Export the instance's external IP address. pulumi.export('instance_external_ip', gpu_accelerated_instance.network_interfaces[0].access_configs[0].nat_ip)

    In the program above:

    • We first define some variables, such as the zone and machine_type, which are important for determining where the instance will live and its capacity.
    • We select a GPU type (gpu_type) available in the specified zone.
    • We create a compute instance (gpu_accelerated_instance) with an attached GPU (guest_accelerators) using the Instance class. The class takes various parameters to configure the instance, including the machine type, boot disk, and network interfaces.
    • We use an InstanceBootDiskArgs with initialize_params to select an image pre-loaded with the required environment for GPU workloads. In this example, "gcp-image-with-cuda" is a placeholder for an actual image that has GPU computing frameworks such as CUDA installed.
    • We configure the network settings, specifying the network to use (default) and the external access configuration that allows assigning a public IP address to the instance (access_configs).
    • Finally, we export the external IP address of the instance as a stack output.

    Please ensure that you have the right permissions and configurations in your GCP environment before running this Pulumi program. This includes having the Compute Engine API enabled and sufficient GPU quota for your project. Remember to replace placeholder values such as 'gcp-image-with-cuda' and 'default' with actual resource names from your GCP project.