1. Virtualized Data Science Workstations on ESXi Platforms


    Virtualized data science workstations involve setting up virtual machines (VMs) tailored for data science tasks on ESXi (a hypervisor platform part of VMware's suite of virtualization products). These VMs typically require substantial computational resources, appropriate networking settings, and a software environment optimized for data analysis and machine learning.

    To create virtualized data science workstations using Pulumi, you would use the vsphere provider, which allows you to interact with and manage resources in a vSphere environment.

    Below is a Pulumi Python program that outlines how to:

    1. Set up a virtual machine on an ESXi host,
    2. Configure the hardware specifications and networking settings,
    3. Install a data science-friendly operating system,
    4. Prepare the environment for data analysis workloads.

    Before we start writing the code, make sure you have the following prerequisites in place:

    • Pulumi CLI installed and configured to manage resources in your desired cloud provider.
    • Access to an ESXi server or a vSphere environment with sufficient privileges to create and manage VMs.
    • The vSphere Pulumi provider plugin installed.
    • Credentials to access vSphere (like username and password or a session token) stored securely, possibly using Pulumi's configuration system or environment variables.

    Let's write a program for creating a virtual machine in a vSphere environment using Pulumi:

    import pulumi import pulumi_vsphere as vsphere # Pulumi program to provision a virtualized Data Science Workstation on an ESXi Platform using vSphere. # Define the virtual machine's characteristics. For a data science workstation, it's common to need # substantial CPU and memory resources. Adjust these values based on your specific needs and capabilities of your ESXi infrastructure. vm_name = "data-science-workstation" guest_id = "ubuntu64Guest" # An identifier for the guest OS. Ubuntu is a popular choice for data science. num_cpus = 8 # Number of CPU cores memory = 32768 # Amount of memory in MB (e.g. 32GB for this example) disk_size = 500 # Size of the disk in GB datastore_id = "YOUR_DATASTORE_ID" # The identifier of the datastore where you want to store the VM network_id = "YOUR_NETWORK_ID" # The identifier of the network to connect the VM to host_system_id = "YOUR_HOST_SYSTEM_ID" # The identifier of the ESXi host system where you want to run the VM resource_pool_id = "YOUR_RESOURCE_POOL_ID" # The identifier of the resource pool to use # Create a virtual machine resource data_science_vm = vsphere.VirtualMachine(vm_name, # For the sake of demonstration, we have hardcoded the resource pool, host system, and datastore where the VM will reside. # Make sure to fill in with actual values from your environment. resource_pool_id=resource_pool_id, datastore_id=datastore_id, num_cpus=num_cpus, memory=memory, guest_id=guest_id, network_interfaces=[vsphere.VirtualMachineNetworkInterfaceArgs( # Configure network settings network_id=network_id, )], disks=[vsphere.VirtualMachineDiskArgs( # Configure the VM's disk size=disk_size, label="disk0", eagerly_scrub=False, thin_provisioned=True, )], # Replace `template_uuid` with the ID of an existing VM template for the workstation's OS. clone=vsphere.VirtualMachineCloneArgs( template_uuid="TEMPLATE_UUID_HERE", ), ) # Output the VM properties for reference pulumi.export('vm_id', data_science_vm.id) pulumi.export('vm_name', vm_name)

    In this program:

    • We import Pulumi and the pulumi_vsphere provider which enables us to work with the vSphere API.
    • We set parameters such as the number of CPUs, memory, disk size, and network information and assign them to our virtual machine.
    • We create an instance of vsphere.VirtualMachine. This is the representation of the VM we are creating on our ESXi host.
    • Within the resources, we specify the properties for the network interface and disk. A virtual disk of 500GB is specified with thin provisioning to save storage space.
    • We reference a VM template that our VM will be cloned from, which is a neat way to quickly replicate a pre-configured environment. You must replace 'TEMPLATE_UUID_HERE' with the actual UUID of a prepared VM template in your vSphere environment.
    • Finally, we export the VM's unique ID and name as output, so you know the identifiers for the newly created resource. These outputs are useful to track the provisioning status or for integration with other systems.

    This code must be adapted to your specific environment by providing the correct IDs for the datastore_id, network_id, host_system_id, and resource_pool_id.

    Make sure to consult the Pulumi vSphere provider documentation for full details on the available properties you can define for your virtual machines.