1. Scalable ML Model Training on Hetzner Cloud Servers

    Python

    To set up scalable machine learning (ML) model training on Hetzner Cloud Servers using Pulumi, we will walk through the needed resources and how they connect to achieve your goal. Hetzner does not have specific Pulumi resources, as Hetzner is not directly supported like AWS, GCP, and Azure.

    However, you can use Pulumi to manage the infrastructure by leveraging the Pulumi Cloud-Agnostic resources (pulumi.Random, pulumi.Command, etc.), custom resource providers, or the Pulumi Hetzner Cloud (hcloud) third-party provider available via the Pulumi Registry. This provider is not official and is community-maintained, which means its use in production should be assessed accordingly.

    Here we will proceed with an assumption about what you wish to accomplish – you want to provision a number of virtual servers that will be used for training an ML model and have the ability to scale this setup both up and down based on your requirements.

    1. Create a Hetzner Cloud Project from the Hetzner Cloud Console.
    2. Generate an access token in the Hetzner Cloud Console under the project you created.
    3. Configure Pulumi to use this token for deploying resources on Hetzner Cloud.

    For illustration purposes, let's define the following steps for the Pulumi program:

    • Provision a set of servers with a specified type, which is suitable for ML workloads.
    • Ensure each server has an SSH key set up to allow access.
    • (Optionally) Set up a load balancer or similar infrastructure if needed for distributing ML tasks.
    • (Optionally) Use cloud-compatible tools for orchestration like Kubernetes, Docker Swarm, or simply remote execution frameworks like Ansible or Pulumi Automation API for coordination and scaling.

    For this example, I'll show you how to provision Hetzner Cloud servers via the Pulumi Hetzner Cloud third-party provider.

    import pulumi import pulumi_hcloud as hcloud # Generate a new SSH key or specify an existing one ssh_key = hcloud.SshKey("ml-ssh-key", public_key="ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQ...") # Define the server type. For ML workloads, you might want a server with more CPUs or GPU support. # This will depend on Hetzner's available server types and what's most appropriate for your ML workload. server_type = "cx31" # Example server type, find a suitable one for ML workloads # Create a variable number of servers. number_of_servers = 3 # Define how many servers you want to provision. servers = [hcloud.Server(f"ml-server-{i}", server_type=server_type, image="ubuntu-20.04", # Make sure to select an image that suits your needs. ssh_keys=[ssh_key.id]) for i in range(number_of_servers)] # (Optional) Set up a load balancer if you need to distribute tasks effectively across servers. # You would also write the logic for deploying your ML model and dependencies to these servers. # Export the IPs of the servers to access them if needed. pulumi.export('server_ips', pulumi.Output.concat([server.ipv4_address for server in servers]))

    In the above Pulumi program, we have done the following:

    • Imported the pulumi_hcloud module, which is the Hetzner Cloud provider for Pulumi.
    • Created an SSH key that is used to access the servers. Replace the SSH public key string with your actual public key.
    • Specified the server type to be used for ML tasks. In reality, you would likely want a server with more resources or even specialized ML hardware like GPUs; Hetzner server types such as cx51, ccx11, or others might be more appropriate depending on your specific needs.
    • Set up a list dynamically to create the desired number of servers. You can adjust number_of_servers to scale the number of servers up or down.
    • You also have the option to include a load balancer, although this is not done in this example.

    Install the Pulumi CLI, set up the Hetzner Cloud provider, and run pulumi up to deploy your infrastructure. The first time you create a new stack, Pulumi will ask you to set it up by choosing a cloud and entering configurations such as the access token.

    Remember, while the above code defines a static number of servers, in an actual scalable environment, you'd have some form of automation in place to adjust number_of_servers based on your workload demands.

    For further ML specific architecture like distributed training, model serving, or more complex orchestration, you will need to incorporate additional Pulumi resources or integrate other orchestration tools that fit your workflow. This could include Docker containers for packaging dependencies, Kubernetes for orchestration, or other automation tools to manage the deployment and scaling of your ML jobs.


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