Scalable AI Training Workloads with OVH Public Cloud GPUs

Question

Pulumi · Accepted Answer

To set up a scalable AI training workload environment using OVH Public Cloud GPUs, you would need to follow a series of steps. Since OVHcloud is not directly supported by Pulumi as a native provider, the approach to automating infrastructure would typically involve using the OpenStack provider, which is compatible with OVH Public Cloud as it exposes an OpenStack API.

However, as of my last update, Pulumi does not have an OpenStack provider ready out of the box. Therefore, a more general approach would involve using Pulumi to create and configure a Kubernetes cluster and then leverage that cluster to perform AI training workloads using GPU-enabled nodes.

This Pulumi program in Python will guide you through setting up a Kubernetes cluster on a cloud provider, such as Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), or Azure Kubernetes Service (AKS), and then configuring it with a node pool or nodes that are GPU-enabled.

We'll go with Google Kubernetes Engine (GKE) for this example. To accomplish this, you would use the `gcp.container.Cluster` and `gcp.container.NodePool` resource types to create and manage the Kubernetes cluster and the node pool with GPU capabilities.

Required setup before running the code:
- You must have a GCP account with billing enabled.
- You must have the `gcloud` CLI tool installed and authenticated.
- You must have Pulumi CLI and Python installed.

Let's start with the Pulumi program:

```python
import pulumi
import pulumi_gcp as gcp

# Replace these variables with your own desired settings
project = "your-gcp-project-id"
zone = "us-central1-a"
cluster_name = "gpu-enabled-cluster"
node_pool_name = "gpu-node-pool"

# Create a GKE cluster
gke_cluster = gcp.container.Cluster(cluster_name,
    project=project,
    zone=zone,
    initial_node_count=1,
    node_config={
        "oauth_scopes": [
            "https://www.googleapis.com/auth/compute",
            "https://www.googleapis.com/auth/devstorage.read_only",
            "https://www.googleapis.com/auth/logging.write",
            "https://www.googleapis.com/auth/monitoring"
        ],
    }
)

# Create a node pool with GPU-enabled nodes
gpu_node_pool = gcp.container.NodePool(node_pool_name,
    project=project,
    cluster=gke_cluster.name,
    zone=zone,
    initial_node_count=1,
    node_config={
        "machine_type": "n1-standard-4",  # This is an example machine type
        "oauth_scopes": [
            "https://www.googleapis.com/auth/compute",
            "https://www.googleapis.com/auth/devstorage.read_only",
            "https://www.googleapis.com/auth/logging.write",
            "https://www.googleapis.com/auth/monitoring",
        ],
        # This accelerators block specifies the GPU type and count
        "accelerators": [{
            "acceleratorCount": 1,
            "acceleratorType": "nvidia-tesla-k80"
        }],
        # Ensure that the node pool has the requisite Drivers for the accelerators
        "guest_accelerator": [{
            "type": "nvidia-tesla-k80",
            "count": 1
        }],
        # Setting preemptible to True can reduce costs for training jobs that can be interrupted
        "preemptible": True,
        "disk_size_gb": 100,
        "disk_type": "pd-standard",
    },
    autoscaling={
        "minNodeCount": 1,
        "maxNodeCount": 5,  # Setting an upper limit to control costs
    },
    management={
        "autoRepair": True,
        "autoUpgrade": True
    },
    depends_on=[gke_cluster]
)

# Export the cluster name and node pool name
pulumi.export('cluster_name', gke_cluster.name)
pulumi.export('node_pool_name', gpu_node_pool.name)
```

This program creates a GKE cluster and a node pool configured with NVIDIA Tesla K80 GPUs. The node pool is also configured to be autoscalable, which means it can automatically scale the number of nodes between the specified minimum and maximum count depending on the workload.

In the `node_config`, we specify the GPU accelerator type and the number of GPUs per node. In this case, we have chosen `nvidia-tesla-k80`, which is suitable for many AI workloads, but this choice would depend on the specific requirements of your AI application.

Remember to substitute your project details, the desired region and zone for deployment, and the specific machine types and GPU counts that meet your needs.

Once executed, this program will provision all the necessary infrastructure on GCP for running your scalable AI workloads with the power of GPUs. You then would use Kubernetes jobs or other similar mechanisms to run your training jobs on the cluster.