Scalable GPU Instances for Deep Learning with OVH

Question

Pulumi · Accepted Answer

Creating scalable GPU instances for deep learning tasks involves provisioning virtual machines (VMs) with powerful GPU capabilities, which are optimized for computational workloads typically seen in machine learning tasks. OVHcloud is a global cloud provider that offers infrastructure capable of such workloads.

Unfortunately, as of my knowledge cut-off in January 2023, Pulumi does not have a dedicated OVHcloud provider. Therefore, I can't directly provide a Pulumi program to manage resources on OVHcloud. To work with cloud providers such as OVHcloud that don't have a dedicated Pulumi provider, you would typically need to use the Pulumi automation API, generic providers, or custom providers, which can be more complex and may require an in-depth understanding of both Pulumi and OVHcloud's API.

However, I can illustrate how you would use Pulumi with a supported cloud provider to create a scalable GPU instance for deep learning. Let's assume that we would be doing this on Google Cloud Platform (GCP), which offers GPU-enabled VMs suitable for such tasks.

Firstly, you'd need to have an account with the cloud provider and the necessary permissions to create and manage these resources. You would also need to install the Pulumi CLI and have the Google Cloud SDK set up with credentials configured.

Here's a high-level overview of steps that you would take with Pulumi when working with a supported cloud provider:

1. **Google Compute Engine VM instances**: You would provision VM instances on GCP using the `google_native.compute.v1.Instance` resource. This resource allows you to specify the machine type, disks, network interfaces, and, importantly, `guestAccelerators`, where you define the GPU type and quantity.
   
2. **Managed Instance Groups (MIGs)**: For scalability, you would use a `InstanceGroupManager` to manage a group of these VM instances. This allows you to apply the same configuration across multiple instances and scale the number of instances up or down in response to demand.

3. **Autoscaling**: Coupled with Managed Instance Groups, you would set up an `Autoscaler` to automatically scale the number of instances based on usage. For a deep learning task, you might scale based on the CPU or GPU utilization.

Below is a simplified Pulumi Python program that shows you how you might define a single GPU-enabled VM instance for deep learning in GCP using Pulumi. An actual production environment would require additional configuration related to networking, security, and scaling policies.

```python
import pulumi
import pulumi_gcp as gcp

# Define a GPU-enabled virtual machine on GCP for deep learning tasks
gpu_instance = gcp.compute.Instance("gpu-instance",
    machine_type="n1-standard-8",  # Standard machine type with 8 vCPUs
    zone="us-west1-a",  # The zone in which the machine should reside
    boot_disk=gcp.compute.InstanceBootDiskArgs(  # Define the boot disk configuration
        initialize_params=gcp.compute.InstanceBootDiskInitializeParamsArgs(
            image="debian-cloud/debian-9",  # The image to use for the boot disk
        )
    ),
    network_interfaces=[gcp.compute.InstanceNetworkInterfaceArgs(  # Network configurations
        network="default",  # Attach to the default network
    )],
    scheduling=gcp.compute.InstanceSchedulingArgs(  # Scheduling options for the VM
        on_host_maintenance="TERMINATE",
        preemptible=False,
    ),
    guest_accelerators=[gcp.compute.InstanceGuestAcceleratorArgs(  # Specification of the GPU
        count=1,
        type="nvidia-tesla-k80",  # The type of the GPU (e.g., Tesla K80, V100, etc.)
    )],
)

# Export the instance's external IP for easy access
pulumi.export('instance_external_ip', pulumi.Output.all(gpu_instance.network_interfaces).apply(lambda interfaces: interfaces[0]['access_configs'][0]['nat_ip']))
```

Each `compute.InstanceGuestAcceleratorArgs` defines the type and count of GPUs for your instance. This is one of the key elements to ensure the instance is suitable for deep learning. Note that the number of GPUs and machine type are often correlated, and you need to choose a machine type that can support the number and type of GPUs you wish to attach.

Additionally, you would have to ensure that the billing account has the necessary quota and billing enabled for GPU usage. You can manage this configuration from the Google Cloud Console or request an increase if necessary.

Remember, this is just a starting point. For scalable solutions, you would add in instance templates, instance groups, and autoscaling configurations as well.

Please, replace the GCP-specific components with the corresponding components of OVHcloud if you would like to deploy this there and they provide similar mechanisms. Do reach out to the OVHcloud API documentation to see how to interact with their services programmatically.

Consider seeking the support of Pulumi for the latest updates on their supported providers, as they are frequently expanding to include more cloud services.