1. High-Performance Training Clusters for Deep Learning on Equinix Metal.


    To set up a high-performance training cluster for deep learning on Equinix Metal with Pulumi, you'll need to create and configure the necessary resources. Here, I'll help you understand and create a simple training cluster setup that includes the following:

    1. A project in Equinix Metal to organize and group your infrastructure.
    2. Bare metal devices/servers with powerful GPUs suitable for deep learning tasks.
    3. Network configurations, such as VLANs, to enable communication between your devices.

    First, we'll set up an Equinix Metal project which acts as a logical grouping for our infrastructure resources. Projects provide features like user access controls, resource management, and usage tracking.

    Next, we'll provision bare metal devices that serve as the nodes of our deep learning cluster. For deep learning purposes, we choose devices with high-performance GPUs. When creating these devices, we have to specify various parameters like the hardware type (plan), operating system, and billing cycle.

    Networking setup such as VLANs is also critical to connect these devices. In an actual production environment, you might want to set up more complex networking, potentially including private networks, gateways, and possible interconnections with other services or data centers.

    Let's write a Pulumi program in Python to create an Equinix Metal project, deploy a couple of GPU-equipped devices, and require networking resources.

    import pulumi import pulumi_equinix as equinix # Create a new Equinix Metal project. project = equinix.metal.Project("deep_learning_project", name="deep-learning-cluster") # Provision a high-performance bare metal device (server) with GPUs. gpu_device_1 = equinix.metal.Device("gpu_device_1", # Choose a hardware plan with GPUs suitable for deep learning. plan="specific-plan-gpu-included", # Select an operating system that is compatible with your deep learning stack. operating_system="ubuntu_20_04", billing_cycle="hourly", metro="sv", project_id=project.id, user_data="""#cloud-config packages: - cuda-drivers - nvidia-docker2""", tags=["deep-learning", "gpu-node"]) # Create a VLAN to enable private networking between devices. vlan = equinix.metal.Vlan("deep_learning_vlan", project_id=project.id, metro="sv", description="Private VLAN for deep learning cluster") # Example of how to export the details of the created resources. pulumi.export("project_id", project.id) pulumi.export("device_1_id", gpu_device_1.id) pulumi.export("vlan_id", vlan.id)

    In this program:

    • Replace "specific-plan-gpu-included" with the actual plan identifier that provides the GPU resources needed for deep learning.
    • The user_data script is used to install CUDA drivers and NVIDIA Docker, which are commonly needed for GPU-based deep learning tasks. Tailor this script to match the specific needs of your deployment, like installing specific deep learning frameworks.
    • Replace "sv" with the metro area that is closest to your location or where you want to deploy the devices to ensure lower latency.
    • Tags like "deep-learning" and "gpu-node" help to identify resources within your Equinix Metal account.

    After running this program with the Pulumi CLI, it will create resources within your Equinix account. You can then login to the Equinix console to manage, monitor, and scale your deep learning cluster as needed.