Dedicated Servers for High-Performance AI Workloads on OVH

Question

Pulumi · Accepted Answer

To set up dedicated servers that are tailored for high-performance AI workloads on OVH cloud using Pulumi, we would typically look for a provider that supports the OVH cloud's infrastructure deployment. However, as of the latest information available to me, there is no direct Pulumi support for OVH Cloud.

Typical resources for AI workloads would include high-memory instances, GPUs, and high-throughput storage and networking. Although we can't directly create dedicated OVH instances with Pulumi, I can suggest an approach:

1. **Use the OVH Cloud Control Panel or API**: You could manually provision the dedicated servers with the desired specifications through the OVH Cloud Control Panel or programmatically using OVH's API.

2. **Infrastructure Management with Pulumi**: When the dedicated servers are up and running, you can still manage other aspects of your cloud infrastructure with Pulumi. For example, you can manage network configurations, security groups, storage volumes, and more using Pulumi with supported cloud providers.

3. **Hybrid Approach**: If you are using other services in your architecture that are available on AWS, Azure, Google Cloud, etc., you can have a hybrid setup where OVH dedicated servers interact with cloud components managed by Pulumi in these other environments.

To give you a feel for how you might use Pulumi for managing infrastructure, we can walk through an example using AWS EC2 dedicated hosts, which provide similar features to what you would expect from OVH dedicated servers, but keep in mind the services and configurations would differ when translated to OVH's offerings.

```python
import pulumi
import pulumi_aws as aws

# Create an AWS EC2 Dedicated Host for high-performance computing
ai_dedicated_host = aws.ec2.DedicatedHost("aiDedicatedHost",
    # Instance type that is suitable for high-performance AI workloads,
    # Compatible with instances that provide GPU or high-memory
    instance_type="m5.metal",
    # Number of instances to launch on the dedicated host
    instance_capacity=2,
    # Automatically replace the dedicated host on failure
    auto_replace_on_failure=True,
    # The Availability Zone in which to allocate the Dedicated Host
    availability_zone="us-west-2a",
)

# Export the host ID so it can be used to reference in other resources or outputs
pulumi.export("aiDedicatedHostId", ai_dedicated_host.id)
```

**Explanation:**
- This program uses the `aws.ec2.DedicatedHost` resource to declare a dedicated physical server. We've chosen an instance type of `m5.metal` which is suitable for high-performance computing. You would adjust the instance type to one that meets your AI workloads best (for instance, equipped with GPUs if your workload requires it).
- The property `instance_capacity` is set to 2, indicating that two instances will be launched on this dedicated host. However, OVH's offering might have different methods to manage the capacity.
- The `auto_replace_on_failure` is set to `True` which means that AWS will automatically replace the dedicated host if a hardware failure occurs.
- The `availability_zone` is set to a specific value (`us-west-2a`). In a real-world scenario, you'd choose the zone that best fits your latency and high-availability needs.

Once you are ready to work in an environment supported by Pulumi, you can apply similar principles to establish your infrastructure. Hopefully, in the future, there might be direct support for OVH Cloud in Pulumi, which would allow for a more integrated setup. In such a case, you'd use OVH-specific provider resources analogous to how `aws.ec2.DedicatedHost` is used here.