High-Performance Machine Learning with DigitalOcean Droplets

Question

Pulumi · Accepted Answer

In order to create a high-performance machine learning environment using DigitalOcean Droplets, we will be using the `digitalocean` provider for Pulumi. The program I'm providing will outline the steps necessary to create a single DigitalOcean Droplet configured for machine learning tasks. Typically, this involves selecting a powerful size for the Droplet and using a specific image tailored for machine learning.

Here's how you can use Pulumi to set up a Droplet for high-performance machine learning on DigitalOcean:

1. **DigitalOcean Droplet**: This resource allows us to create and manage a virtual machine instance in DigitalOcean's infrastructure, which we can use for our machine learning workloads.

2. **SSH Key**: To securely access the Droplet, we should also set up an SSH key, which will allow us to SSH into the Droplet without using passwords.

3. **DigitalOcean Custom Image**: Optionally, if we have a custom image that we've previously prepared with machine learning libraries and tools, we can use this resource to create Droplets from that image.

For the purpose of this example, we'll assume you want to create a new Droplet with the default Ubuntu image but have already an initial setup script (e.g., for installing Python, CUDA for NVIDIA GPUs, machine learning libraries like TensorFlow or PyTorch, etc.). We'll include a cloud-init `userData` field in the Droplet configuration for bootstrapping these installations.

Note that this is a simple configuration - in a real-world scenario, you might want to create multiple Droplets, configure them to work in a cluster, set up load balancing, etc., depending on your performance and scaling requirements.

Now, let's write a basic program to create a high-performance machine learning Droplet:

```python
import pulumi
import pulumi_digitalocean as digitalocean

# Define your SSH public key to access the Droplet
ssh_key = digitalocean.SshKey("ml-ssh-key",
    public_key="YOUR_SSH_PUBLIC_KEY"
)

# Initialize a DigitalOcean Droplet for high-performance machine learning tasks
machine_learning_droplet = digitalocean.Droplet("ml-droplet",
    # Specifies the name of the Droplet for easy identification.
    name="high-perf-ml-droplet",
    # Specifies the slug identifier for the size of the Droplet. This should be a high-CPU or high-Memory variant.
    size="s-4vcpu-8gb",
    # Specifies the identifier for the image used to create the Droplet. Here we are using a standard Ubuntu image.
    image="ubuntu-20-04-x64",
    # Specifies the slug identifier for the region where the Droplet will be created.
    region="nyc3",
    # Attaches the SSH key created above to the Droplet for secure SSH access.
    ssh_keys=[ssh_key.id],
    # Optional cloud-init userData script to install and configure machine learning tools.
    # This typically contains shell commands to update the OS and install dependencies.
    user_data="""#cloud-config
runcmd:
  - apt-get update
  - apt-get install -y python3-pip python3-dev
  - pip3 install numpy pandas scikit-learn matplotlib seaborn jupyter tensorflow keras
  - ufw allow OpenSSH
  - ufw --force enable""",
)

# Export the IPv4 address of the new Droplet to easily access it
pulumi.export("ipv4_address", machine_learning_droplet.ipv4_address)
```

This program does the following:

- It starts by importing the necessary modules for Pulumi and the DigitalOcean provider.
- It sets up an SSH key using the `SshKey` resource, which you'll need to access your Droplet. Replace `"YOUR_SSH_PUBLIC_KEY"` with your actual SSH public key.
- It then declares a `Droplet` resource named `ml-droplet`. The Droplet is named `high-perf-ml-droplet` for clarity.
- The Droplet size is set to `s-4vcpu-8gb` which provides 4 CPUs and 8GB of RAM, suitable for small to medium-sized machine learning tasks.
- We use the standard Ubuntu image specified by `ubuntu-20-04-x64`, which is a good starting point for a machine learning environment.
- The Droplet is placed in the `nyc3` region, but you can choose a region closer to you.
- We attach the previously defined SSH key to the Droplet for secure access.
- The `user_data` parameter contains a cloud-init script that updates the package list, installs Python and some popular data science libraries, sets up a firewall to allow SSH access, and other initial setup tasks.
- Finally, we export the IPv4 address of the Droplet so you can easily know how to access it via SSH.

Remember to replace `"YOUR_SSH_PUBLIC_KEY"` with your actual SSH key. To apply this Pulumi configuration, save the code in a file (e.g. `main.py`), then simply run `pulumi up` from the same directory where your file is located, and Pulumi will handle the provisioning for you.

Keep in mind that this configuration is quite basic. Depending on the requirements of your machine learning tasks, you may need to adjust the `size` of the Droplet for more CPU, memory, or add GPUS support (if available). Additionally, you can also pre-build a custom image with all your ML tools and environments, and use that custom image to create your Droplets instead of the default Ubuntu image and the cloud-init script.