vSphere for AI Model Training Environments

Question

Pulumi · Accepted Answer

When setting up an AI model training environment on vSphere, you typically need a robust computing cluster with high-performance storage and networking capabilities to manage and process the large volume of data associated with machine learning tasks. Using Pulumi with the vSphere provider, we'll prepare a foundation for such an environment.

Our Pulumi program will perform these tasks:

1. Create a vSphere Datacenter as a container for our compute and storage resources.
2. Set up a Compute Cluster with DRS (Distributed Resource Scheduler) enabled for resource management.
3. Define a VM Storage Policy that ensures our virtual machines have the performance required for AI training tasks.
4. Create a Role with appropriate privileges needed for automation or management tasks.

Here's a Pulumi program in Python that accomplishes these tasks:

```python
import pulumi
import pulumi_vsphere as vsphere

# Configure the vSphere provider
# Ensure the Pulumi vSphere provider is correctly configured with the settings
# for your vSphere environment, such as user credentials and endpoint.

# Create a new Datacenter
datacenter = vsphere.Datacenter("ai-training-dc",
    name="ai_model_training_datacenter"
)

# Create a new Compute Cluster within the Datacenter
compute_cluster = vsphere.ComputeCluster("ai-training-cluster",
    name="ai_model_training_cluster",
    datacenter_id=datacenter.id,
    ha_enabled=True, # High Availability
    drs_enabled=True, # Distributed Resource Scheduler
    vsan_enabled=False # We assume external storage is provided. If not, set to True.
)

# Define a VM Storage Policy for machine learning workloads
vm_storage_policy = vsphere.VmStoragePolicy("ai-training-storage-policy",
    name="ai_model_training_storage_policy",
    # Performance tag rules to identify high-performance storage resources
    tag_rules=[
        vsphere.VmStoragePolicyTagRulesArgs(
            tags=["high-performance"],
            tag_category="storage-tier",
            include_datastores_with_tags=True
        )
    ]
)

# Create a Role with privileges for AI training tasks
ai_training_role = vsphere.Role("ai-training-role",
    name="ai_model_training_role",
    role_privileges=["Datastore.AllocateSpace", "Network.Assign"]
)

# Outputs secrets
pulumi.export("datacenter_id", datacenter.id)
pulumi.export("compute_cluster_id", compute_cluster.id)
pulumi.export("vm_storage_policy_id", vm_storage_policy.id)
pulumi.export("ai_training_role_id", ai_training_role.id)
```

Here is a brief rundown of what each section of the code is doing:

1. **Datacenter**: We're defining a virtual container (`Datacenter`) to hold our training environment within vSphere. This helps in managing resources hierarchically.

2. **Compute Cluster**: This is a collection of host systems that provides a pool of resources for running virtual machines. We're enabling high availability (`ha_enabled`) and distributed resource scheduling (`drs_enabled`), both of which are important for ensuring our training environment can handle node failures and load balancing.

3. **VM Storage Policy**: VM Storage Policies are used in vSphere to ensure that the VMs get the storage they require. Here, we're creating a policy that focuses on high performance by looking for storage tagged (`tags`) as "high-performance."

4. **Role**: We're creating a custom Role within vSphere that can be assigned to users or automation scripts. This Role includes specific privileges that are typically needed for managing AI model training workloads.

To use this program, save it as `main.py` and run it with the Pulumi CLI. Make sure your environment is appropriately configured for the vSphere provider. After running `pulumi up` and accepting the plan, the resources will be provisioned in your vSphere environment.

The `pulumi.export` lines at the end of the program output the identifiers of the created resources, which can be used for reference or in automation scripts.