1. ESXi Hosting GPU-Accelerated AI Workloads


    In order to host GPU-accelerated AI workloads on an ESXi environment using Pulumi, there are several steps we must undertake:

    1. Provision an ESXi Host: The host is the server hardware that will run the virtual machines (VMs). It needs to be capable of GPU passthrough if you're planning to use physical GPUs for acceleration.

    2. Create Virtual Switches: These are needed for VMs to communicate with each other and potentially with the outside world.

    3. Create a Resource Pool: Resource Pools allow for managing and allocating resources like CPU and memory for a collection of VMs.

    4. Provision Virtual Machines: VMs are the environments where the AI workloads actually run. They will need to have the necessary virtual hardware configured, such as CPU, memory, and GPU capabilities.

    5. Allocate GPUs to VMs: For GPU-accelerated workloads, you'll need to configure the VMs to have access to the GPUs. This typically involves configuring the host to allow for GPU passthrough and adding the appropriate hardware to the VM configurations.

    Pulumi provides the resources to manage this entire process, using different providers to interface with the respective technologies.

    Let's write a Pulumi program in Python to set up an ESXi host suitable for GPU-accelerated AI workloads.

    In this program, we will illustrate the provisioning of an ESXi host and a virtual machine that will be configured for GPU use. We'll use the vsphere Pulumi provider which allows us to interact with VMware's vSphere products, including ESXi.

    Note: This is a conceptual program and assumes that you have the required permissions and access to a vCenter server and an ESXi host. The specifics of the GPU hardware and how it is exposed to the VM may depend on the actual hardware and ESXi configuration which are beyond the scope of this example.

    import pulumi import pulumi_vsphere as vsphere # Configure Pulumi to use the VMware vSphere provider vcenter_server = "vcenter.mydomain.com" vcenter_user = "user@mydomain.com" vcenter_password = "password" vsphere_provider = vsphere.Provider("vsphere_provider", vsphereserver=vcenter_server, user=vcenter_user, password=vcenter_password, allow_unverified_ssl=True) # Get the ESXi host and datastore on which to create the resources esxi_host = vsphere.HostSystem.get("esxiHost", "hostname.mydomain.com", provider=vsphere_provider) datastore = vsphere.Datastore.get("datastore", "/mydomain.com/datacenter/datastore/mydatastore", provider=vsphere_provider) # Now create a Resource Pool for our AI workloads resource_pool = vsphere.ResourcePool("aiResourcePool", parent_resource_pool_id=esxi_host.resource_pool_id, provider=vsphere_provider) # Create a Virtual Machine for GPU-accelerated AI workload vm = vsphere.VirtualMachine("aiVm", name="ai-virtual-machine", resource_pool_id=resource_pool.id, datastore_id=datastore.id, num_cpus=4, memory=8192, # 8GB of RAM guest_id=vsphere.VirtualMachineGuestId.LINUX_64_GUEST, network_interfaces=[{ "network_id": vsphere_network.id, "adapter_type": vsphere.VirtualMachineNetworkInterfaceAdapterType.E1000, }], disks=[{ "size": 50, "eagerly_scrub": True, "thin_provisioned": True, }], provider=vsphere_provider) # Configuration for GPU pass-through would be done here. # This is highly hardware-specific and requires manual setup on the ESXi host. # Export the IP address of the VM once it's available pulumi.export("aiVmIp", vm.default_ip_address)

    This program starts by setting up the required vSphere provider with the necessary credentials. It then retrieves the existing ESXi host and datastore. The resource pool is created to manage the resources for AI workloads. After that, we configure a virtual machine tailored for AI purposes with certain specifications, like number of CPUs and amount of memory.

    Regarding the GPU pass-through setup, it is imperative that you consult the documentation for your specific GPU hardware, as well as VMware's documentation on configuring hardware pass-through to VMs.

    The last step is to export the IP address of the virtual machine for later use, perhaps to connect to it and deploy the AI software stack.

    You would run this Pulumi program by saving it to a Python file (e.g., main.py) and using the Pulumi CLI to create the stack and update your infrastructure as code. This allows you to version, share, and reuse your infrastructure as easily as you do with your application source code.