1. Low-Latency Inference Serving on Equinix Metal Bare Metal.

    Python

    In order to set up low-latency inference serving on Equinix Metal using bare metal servers, you'll need to provision the infrastructure to support it. Pulumi can help automate the provisioning of the required resources. Here, I'll explain how you can use Pulumi to deploy an Equinix Metal bare metal server suitable for running an inference serving application.

    The key resources we are going to use for this setup include:

    • equinix.metal.Device: This resource is used to create a bare metal server on Equinix Metal. We will specify the server's properties, such as its operating system and hardware specifications.

    • equinix.metal.Project: A project on Equinix Metal helps organize resources. It will contain the device we create.

    You can find more detailed documentation about these resources and their properties through the Pulumi Registry here:

    The program below provides a configuration for deploying a project and a device within it. Here's what the process will look like:

    1. We'll create an Equinix Metal Project.
    2. Within this project, we'll provision a bare metal device with the desired specifications suitable for inference serving, such as an instance with accelerated GPU hardware.
    3. We'll configure the device with the necessary OS and network settings.

    Here's how the Pulumi Python program for setting this up might look:

    import pulumi import pulumi_equinix as equinix # Step 1: Create a new Equinix Metal Project. # Replace 'your_project_id' with your actual Equinix project ID. project = equinix.metal.Project("inference-serving-project", name="inference-serving") # Step 2: Create a new Equinix Metal Device within the project. # This will be the bare metal server configured for running low-latency inference serving. # You would need to specify proper 'plan', 'metro', and 'operating_system' as per your needs. # For example, to use a server grade CPU with GPU acceleration suitable for ML inference, # you might choose a plan like 'x1.small.x86' or similar, checking Equinix's plans for one with a GPU. device = equinix.metal.Device("inference-serving-device", hostname="inference-server", plan="x1.small.x86", # Replace with the actual plan required for inference. metro="sv", # Replace with the metro close to your users for low-latency. operating_system="ubuntu_20_04", # The OS compatible with your inference stack. billing_cycle="hourly", project_id=project.id) # Step 3: Export the IP address of the device so you can SSH into it. # You will SSH into your server to set it up for inference. public_ip = pulumi.Output.all(device.access_public_ipv4, device.access_public_ipv6).apply(lambda ips: ips[0] if ips[0] else ips[1]) pulumi.export("device_ip", public_ip)

    Remember to replace the placeholders like your_project_id, the plan, and the metro with actual values that you choose based on the location of your users and the inference workload you're planning to serve.

    When you run this Pulumi program, it will provision the required infrastructure in your Equinix Metal account. After the server is ready, you can then deploy your machine learning models and inference serving software onto it.

    To SSH into your server, you can use the exported IP address outputted by Pulumi at the end of the deployment process.

    Ensure you have the Pulumi CLI and Equinix Metal provider configured with your credentials before running the program. You can then apply this configuration by running pulumi up. This command will prompt you with the resources that will be created before performing any actual changes to your infrastructure.


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