Azure Batch provides a HPC Computing environment in Azure for distributed tasks. Azure Batch handles scheduling of discrete jobs and tasks across pools of VM's. It is commonly used for batch processing tasks such as rendering.

The Virtual kubelet integration allows you to take advantage of this from within Kubernetes. The primary usecase for the provider is to make it easy to use GPU based workload from normal Kubernetes clusters. For example, creating Kubernetes Jobs which train or execute ML models using Nvidia GPU's or using FFMPEG.

Azure Batch allows for low priority nodes which can also help to reduce cost for non-time sensitive workloads.

The ACI provider is the best option unless you're looking to utilise some specific features of Azure Batch.

This provider is currently in the experimental stages. Contributions welcome!

The following Terraform template deploys an AKS cluster with the Virtual Kubelet, Azure Batch Account and GPU enabled Azure Batch pool. The Batch pool contains 1 Dedicated NC6 Node and 2 Low Priority NC6 Nodes.

1. Setup Terraform for Azure following this guide here
2. From the commandline move to the deployment folder cd ./providers/azurebatch/deployment then edit vars.example.tfvars adding in your Service Principal details
3. Download the latest version of the Community Kubernetes Provider for Terraform. Get the correct link from here and use it as follows: (Current official Terraform K8s provider doesn't support Deployments)

curl -L -o - PUT_RELEASE_BINARY_LINK_YOU_FOUND_HERE | gunzip > terraform-provider-kubernetes
chmod +x ./terraform-provider-kubernetes

4. Use terraform init to initialize the template
5. Use terraform plan -var-file=./vars.example.tfvars and terraform apply -var-file=./vars.example.tfvars to deploy the template
6. Run kubectl describe deployment/vkdeployment to check the virtual kubelet is running correctly.
7. Run kubectl create -f examplegpupod.yaml
8. Run pods=$(kubectl get pods --selector=app=examplegpupod --show-all --output=jsonpath={.items..metadata.name}) then kubectl logs $pods to view the logs. Should see:

	[Vector addition of 50000 elements]
	Copy input data from the host memory to the CUDA device
	CUDA kernel launch with 196 blocks of 256 threads
	Copy output data from the CUDA device to the host memory
	Test PASSED
	Done

You can update main.tf to increase the number of nodes allocated to the Azure Batch pool or update ./aks/main.tf to increase the number of agent nodes allocated to your AKS cluster.

1. An Azure Batch Account configured
2. An Azure Batch Pool created with necessary VM spec. VM's in the pool must have:
   • docker installed and correctly configured
   • nvidia-docker and cuda drivers installed
3. K8s cluster
4. Azure Service Principal with access to the Azure Batch Account

The provider expects the following environment variables to be configured:

    ClientID:        AZURE_CLIENT_ID
	ClientSecret:    AZURE_CLIENT_SECRET
	ResourceGroup:   AZURE_RESOURCE_GROUP
	SubscriptionID:  AZURE_SUBSCRIPTION_ID
	TenantID:        AZURE_TENANT_ID
	PoolID:          AZURE_BATCH_POOLID
	JobID (optional):AZURE_BATCH_JOBID
	AccountLocation: AZURE_BATCH_ACCOUNT_LOCATION
	AccountName:     AZURE_BATCH_ACCOUNT_NAME

The provider will assign pods to machines in the Azure Batch Pool. Each machine can, by default, process only one pod at a time running more than 1 pod per machine isn't currently supported and will result in errors.

Azure Batch queues tasks when no machines are available so pods will sit in podPending state while waiting for a VM to become available.