Multi-node GPU Clusters for Deep Learning on Azure Virtual Network

Question

Pulumi · Accepted Answer

Creating a multi-node GPU cluster for deep learning on an Azure Virtual Network involves several steps and a combination of Azure resources. Below I will give you an overview of the Pulumi program in Python that creates such an infrastructure. Here's what we want to accomplish with the program:

1. Provision a Virtual Network (VNet) in Azure to host our GPU cluster.
2. Create a Batch Account and Batch Pool within the Virtual Network. The Batch Pool will consist of multiple compute nodes, each equipped with GPUs, which will be used to run deep learning tasks. (Note: Azure Batch Pools allow you to manage a collection of virtual machines as a pool for running computing tasks.)
3. Attach a Network Security Group (NSG) to the subnet to manage access and security rules.

Here's a Pulumi program written in Python that will set up this infrastructure:

```python
import pulumi
import pulumi_azure as azure

# Create an Azure Resource Group
resource_group = azure.core.ResourceGroup('gpu-cluster-rg')

# Create an Azure Virtual Network
vnet = azure.network.VirtualNetwork('gpu-cluster-vnet',
                                    resource_group_name=resource_group.name,
                                    address_spaces=['10.0.0.0/16'])

# Create a subnet inside the Virtual Network
subnet = azure.network.Subnet('gpu-cluster-subnet',
                              resource_group_name=resource_group.name,
                              virtual_network_name=vnet.name,
                              address_prefix='10.0.1.0/24')

# Create an Azure Batch Account
batch_account = azure.batch.Account('gpu-cluster-batch-account',
                                    resource_group_name=resource_group.name,
                                    location=resource_group.location,
                                    pool_allocation_mode='BatchService')

# Create a Network Security Group and associate it with the subnet
nsg = azure.network.NetworkSecurityGroup('gpu-cluster-nsg',
                                         resource_group_name=resource_group.name,
                                         security_rules=[])

subnet.update(azure.network.SubnetArgs(
    network_security_group_id=nsg.id
))

# Create a Batch Pool with GPU-enabled virtual machines
batch_pool = azure.batch.Pool('gpu-cluster-batch-pool',
                              resource_group_name=resource_group.name,
                              account_name=batch_account.name,
                              vm_size='Standard_NC6',  # GPU enabled VM
                              display_name='GPUPool',
                              vnet_id=vnet.id,
                              fixed_scale=azure.batch.PoolFixedScaleArgs(
                                  target_dedicated_nodes=2,
                                  target_low_priority_nodes=1
                              ))

# Export the VNet ID, Batch Account, and Batch Pool ID
pulumi.export('vnet_id', vnet.id)
pulumi.export('batch_account_name', batch_account.name)
pulumi.export('batch_pool_id', batch_pool.id)
```

In this script, we start by creating a `ResourceGroup` which is a container that holds related resources for an Azure solution. Then, we provision a `VirtualNetwork` and a `Subnet` which will form the network foundation for our GPU cluster.

After establishing the network, a `BatchAccount` is created. This account provides access to the Azure Batch service that manages the cluster.

Next, we create a `NetworkSecurityGroup` (NSG) with no specific `security_rules` defined. You can define inbound and outbound rules as per your requirement. We then associate this NSG with the subnet we created earlier to control the network traffic to and from the Azure Batch resources within our Virtual Network.

Finally, we define a `BatchPool` with GPU-enabled VMs. Here, we're using `Standard_NC6`, which is an Azure VM size that includes a GPU meant for computing tasks like deep learning. We've decided on a configuration with 2 dedicated nodes and 1 low-priority node for our pool but you can adjust these numbers according to your needs.

By the end of this program, you'll have a multi-node GPU cluster configured within an Azure Virtual Network, ready for running deep learning workloads. The program also exports the IDs of the created resources, which can be useful if you need to reference these resources in subsequent Pulumi programs or other tools.