Distributed Training of Machine Learning Models on Hetzner Cloud

Question

Pulumi · Accepted Answer

Distributed training of machine learning models involves splitting a large training dataset or complex model across multiple machines, or nodes, to leverage parallel processing power for faster computation. Unfortunately, as of my knowledge cutoff in early 2023, Pulumi does not directly support Hetzner Cloud, which means there are no dedicated Pulumi providers for managing Hetzner Cloud resources.

However, managing resources on unsupported cloud providers with Pulumi is still possible through other means such as the Command Provider, which allows you to run arbitrary commands and treat their outputs as Pulumi resources. To use the Command Provider with Hetzner Cloud, you would first install the Hetzner Cloud command-line interface (hcloud CLI) in your local environment, and then use Pulumi to orchestrate the commands to create and manage your machines for distributed training.

Here's a basic Pulumi program using the Command Provider to create multiple Hetzner Cloud instances for distributed training of machine learning models. The program relies on the existence of the `hcloud` CLI tool and assumes that you have the appropriate API token configured for `hcloud`.

In this program example, we'll simulate creating two Hetzner Cloud servers and installing a simple dummy script that represents the distributed training process. In a real-world scenario, you would replace this with commands to set up your training environment (e.g., installing TensorFlow, PyTorch, or your chosen ML framework).

Please replace `YOUR_SSH_KEY_NAME` with the name of your SSH key you have already registered in your Hetzner Cloud account, and `YOUR_IMAGE` and `YOUR_SERVER_TYPE` with the image and server type you wish to use.

```python
import pulumi
from pulumi_command import local

# Function to create a Hetzner Cloud server using the `hcloud` command line tool.
def create_server(name, server_type, image, location, ssh_key):
    return local.Command(
        # Resource names are unique identifiers used to reference the resources in the Pulumi State.
        f'create-server-{name}',
        # The `create` argument specifies the CLI command to create the resource.
        create=f'hcloud server create --name {name} --type {server_type} --image {image} --location {location} --ssh-key {ssh_key}',
        # The `delete` argument specifies the CLI command to delete the resource.
        delete=f'hcloud server delete {name}',
        # The `update` argument is set if updates to the resource are possible with subsequent `pulumi up` invocations.
        update=f'hcloud server update --name {name} --type {server_type}',
        # Parses the server ID from the `hcloud` create command output and makes it available for export.
        opts=pulumi.CommandOptions(stdout=pulumi.CommandOutput.parse_json(lambda x: x['server']['id']))
    )

# List of server names to create for distributed training.
# In real-world usage, you might generate or read these from configuration.
server_names = ['ml-server-1', 'ml-server-2']

# Iterate over server names to create multiple servers.
servers = [create_server(name, 'cx11', 'ubuntu-20.04', 'fsn1', 'YOUR_SSH_KEY_NAME') for name in server_names]

# Export the server IDs for reference.
for i, server in enumerate(servers):
    pulumi.export(f'server_{i+1}_id', server.stdout)
```

In this program, we defined a function `create_server` which uses the Command Provider to create a new Hetzner Cloud server with the `hcloud` CLI. When you run `pulumi up`, Pulumi invokes the CLI commands specified in the `create`, `delete`, and `update` arguments to manage the lifecycle of the server resources.

If you decided to use this in a production context, be sure to secure your server configurations, handle error cases, and fully configure your distributed training environment, including installing all necessary dependencies and securely managing your Hetzner API token.

Remember that this is a rudimentary example; production-grade distributed training systems involve more complex setup, including networking, storage, and error handling considerations. If Hetzner Cloud becomes officially supported by Pulumi in the future, using a dedicated Pulumi provider would be more straightforward and secure than orchestrating CLI commands.