1. GPU-Optimized Instances for Deep Learning on Hetzner Cloud


    Creating GPU-optimized instances for deep learning on Hetzner Cloud with Pulumi involves several steps. On Hetzner Cloud, the infrastructure for deep learning can typically be set up using virtual servers that offer dedicated GPUs. These instances are known as "Dedicated vGPU Instances".

    To start with, Pulumi does not have a dedicated Hetzner Cloud provider. However, you can use the Pulumi Terraform bridge to work with the terraform-provider-hcloud which allows provisioning resources on Hetzner Cloud.

    Here's what you need to do in order to create GPU-optimized instances on Hetzner Cloud using Pulumi with Python:

    1. Set up the provider: We'll use the terraform-provider-hcloud which needs to be set up with the required credentials and configuration.

    2. Create a server: Hetzner Cloud servers are provisioned using the hcloud.Server resource. For a deep learning setup, you would look for a server type that supports GPUs.

    3. Configure your server: You'll select an appropriate image (e.g., Ubuntu) and potentially set up SSH keys for access.

    4. Initialize GPUs (if applicable): After your instance is running, if needed, initialize the GPU drivers. This step often requires executing commands on the server after it has been created, which is typically done outside of Pulumi's provisioning.

    Since the third-party Terraform provider needs to be set up manually, we will focus on steps 2 and 3 using a generic provider setup. However, please remember that for production use, you will need to configure your provider correctly with Hetzner Cloud credentials.

    Here’s a program that outlines the creation of a deep learning server with pseudo configurations assuming there's an existing Hetzner Cloud Provider for Pulumi:

    import pulumi import pulumi_terraform as terraform # Configuration for the Hetzner Cloud Provider # Replace 'my_api_token' with your Hetzner Cloud API token hcloud_provider = terraform.Provider("hcloud", api_token='my_api_token') # Provision a GPU-optimized server instance # Note: 'cx31' is specified as a placeholder, replace it with the actual GPU instance type. gpu_server = terraform.Resource("gpu_server", resource_type="hcloud_server", # This dictionary represents the configuration arguments for your GPU instance config={ "name": "deep-learning-instance", "image": "ubuntu-18.04", # Replace with the desired image for deep learning "server_type": "cx31", # Replace with the actual server type that offers GPU "location": "fsn1", # Replace with your preferred location "ssh_keys": ["ssh-rsa AAA... user@example.com"] # Replace with your SSH public key }, opts=pulumi.ResourceOptions(provider=hcloud_provider)) # Export the server's IP address to access it later pulumi.export("ip", gpu_server.output["ipv4_address"])

    Keep in mind that Hetzner Cloud provider is not directly supported by Pulumi, and the above code is for illustrative purposes of how you would write it if it were. You'll need to use the Pulumi Terraform bridge with the official Hetzner Terraform Provider. The setup for these providers would require a Terraform configuration block and potential use of the pulumi_terraform bridge module.

    After your server is running, you'd typically set up your environment for deep learning, which may include installing CUDA, cuDNN, and your deep learning frameworks like TensorFlow or PyTorch, again often done with additional provisioning scripts or manual setup after the instance is running.

    For a complete setup, including managing the Terraform bridge and the initialization of the GPU environment, it would be advisable to reference the official Pulumi documentation on using Terraform Providers, Hetzner Cloud’s documentation, and the documentation of the deep learning tools you wish to use.