GPU-enabled hcloud Servers for Deep Learning Workloads

Question

Pulumi · Accepted Answer

To create GPU-enabled Hetzner Cloud (hcloud) servers for deep learning workloads, you would typically need to instantiate servers using a specific image that has GPU support as well as installing any necessary deep learning frameworks and drivers.

Please note that at the time of this response, Pulumi does not have an official integration for Hetzner Cloud in its registry, nor a specific resource akin to 'hcloud.Server' that can be used to create GPU-enabled servers directly. Therefore, I'll guide you on how to set up an infrastructure with a similar cloud provider that offers GPU-enabled machines and is supported by Pulumi, such as Google Cloud Platform (GCP) which has GPU-enabled compute instances.

We're going to:

1. Create a GPU-enabled virtual machine instance in Google Cloud.
2. Ensure that the machine type is suitable for deep learning workloads.
3. Attach a GPU to the instance.
4. Install necessary CUDA drivers and deep learning libraries via startup scripts.

### Detailed Explanation

- **Compute Instance**: We'll use the Google Compute Engine (GCE) `Instance` class to create a virtual machine. For deep learning tasks, we will select a machine type that has enough CPU, RAM, and supports attaching GPUs.
  
- **GPU Accelerator**: Google Cloud supports various types of GPUs (like Nvidia Tesla K80, T4, V100, etc.). We will attach a suitable GPU accelerator to our compute instance for deep learning purposes.
  
- **CUDA Drivers and Libraries**: To use the GPU, we need to install CUDA, cuDNN, and possibly other libraries. This can be achieved by providing a startup script to our instance that will run these installations on boot.

- **Disk Image**: We'll select an OS image that supports CUDA and GPUs. For instance, we can use an Ubuntu image with pre-installed NVIDIA drivers.

Let's write the Pulumi program to set up such an environment:

```python
import pulumi
import pulumi_gcp as gcp

# Replace these variables according to your requirements
project = 'your-gcp-project'
zone = 'us-central1-a'
machine_type = 'n1-standard-4'  # Modify with your desired machine type
gpu_type = 'nvidia-tesla-k80'  # Replace with your desired GPU type
gpu_count = 1
instance_name = 'deep-learning-instance'

# Create a GCP Compute Instance with an attached GPU for deep learning workloads
compute_instance = gcp.compute.Instance(instance_name,
    machine_type=machine_type,
    zone=zone,
    boot_disk=gcp.compute.InstanceBootDiskArgs(
        initialize_params=gcp.compute.InstanceBootDiskInitializeParamsArgs(
            image='ubuntu-os-cloud/ubuntu-minimal-2004-focal-v20220202',  # A supported image for CUDA
        ),
    ),
    network_interfaces=[gcp.compute.InstanceNetworkInterfaceArgs(
        network="default",
    )],
    metadata_startup_script="""#!/bin/bash
        # Install CUDA and other necessary deep learning libraries
        echo "Installing CUDA..."
        # Add commands to install CUDA and deep learning frameworks...
    """,
    guest_accelerators=[gcp.compute.InstanceGuestAcceleratorArgs(
        type=gpu_type,
        count=gpu_count,
    )],
    # Ensure the GCP project and zone are set
    project=project,
)

# Export the instance name and IP to easily access it later
pulumi.export('instance_name', compute_instance.name)
pulumi.export('instance_ip', compute_instance.network_interfaces[0].network_ip)
```

### What the Program Does

- **Instance Creation**: This program defines a new Google Compute Engine instance with the type and GPU configurations we specified.

- **Startup Script**: A startup script is provided to install CUDA and other libraries. Replace the placeholder instructions in the startup script with actual installation commands.

- **Resource Exports**: The instance name and IP are exported so you can easily locate and connect to the VM instance after it's been created.

Make sure you have Pulumi installed and configured with your Google Cloud credentials. You'll also want to update project and zone variables, along with any other specific configuration you might need for your deep learning workloads. Once your program is ready, run `pulumi up` to deploy your infrastructure.

Remember, this is a basic setup. Depending on your requirements, you might need to configure additional settings like VPC, firewall settings, persistent storage, and more.