GPU-Enabled DigitalOcean Droplets for Deep Learning

Question

Pulumi · Accepted Answer

To create GPU-enabled DigitalOcean Droplets for deep learning applications, you need to use DigitalOcean's Droplet resource in Pulumi. Droplets are virtual machines (VMs) that run on top of virtualized hardware. Each Droplet you create is a new server that you can use to host your web applications, databases, and other computing tasks.

For deep learning, it's important to select a Droplet size that comes with dedicated GPU resources. Unfortunately, as of my knowledge cutoff in early 2023, DigitalOcean doesn't offer GPU-optimized Droplets directly—unlike other cloud providers with specific GPU-based instances. However, you might be able to set up a CPU-optimized Droplet and manage deep learning workloads that way, and then keep an eye out for any updates on GPU offerings from DigitalOcean.

Below I'll show you a simple Pulumi program that creates a CPU-optimized DigitalOcean Droplet with Pulumi's DigitalOcean provider and Python. Note that this is a CPU-based instance, as GPUs are not directly available.

First, make sure to install the required Pulumi DigitalOcean package by running:

```sh
pip install pulumi_digitalocean
```

Now, let's move on to the program:

```python
import pulumi
import pulumi_digitalocean as digitalocean

# Create a CPU-optimized Droplet on DigitalOcean for deep learning tasks.
# For now, DigitalOcean doesn't offer GPU-optimized droplets,
# but you can utilize these CPU-optimized droplets for certain workloads.
cpu_optimized_droplet = digitalocean.Droplet("cpu-optimized-droplet",
    # You should choose a slug that represents a CPU-optimized droplet size.
    size="s-2vcpu-4gb", # This is an example slug, you should update it based on your workload requirements.
    image="ubuntu-20-04-x64", # Choose an image. Ubuntu is often used for deep learning environments.
    region="nyc1", # Select the region for your Droplet.
    # Optionally, attach an SSH key to your Droplet for secure access.
    ssh_keys=[YOUR_SSH_KEY_ID], # Replace YOUR_SSH_KEY_ID with your actual SSH key ID.
)

# Export the public IP address of the Droplet
pulumi.export("droplet_ip", cpu_optimized_droplet.ipv4_address)

```

Here's a breakdown of what the program does:

- We import the Pulumi and Pulumi DigitalOcean libraries to interact with DigitalOcean.
- We create a new CPU-optimized Droplet using the `Droplet` resource class from the DigitalOcean provider. You should select an appropriate slug based on your needs from DigitalOcean's documentation.
- We specify the base image for the Droplet, which in this case is `ubuntu-20-04-x64`. This is a common image used for setting up deep learning environments containing the necessary software for such tasks.
- We set the region where the Droplet will be created; it's important to choose a region closer to your location or your target audience for reduced latency.
- Optionally, we include an SSH key for the Droplet. SSH keys provide secure, passwordless access to your Droplet. You'd need to replace `YOUR_SSH_KEY_ID` with the identifier for your SSH key stored in DigitalOcean.

Finally, we export the public IP address of the Droplet, which you can use to access the server remotely, for example, through SSH.

Remember that this setup, while not GPU optimized, can still be used for lightweight deep learning tasks or could be part of an initial development or educational environment. If you require GPUs for your deep learning workloads, consider using other cloud providers like AWS or GCP, which provide instances with dedicated GPUs for such tasks. Keep an eye out for any future announcements from DigitalOcean regarding GPU support.