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- Builds on the changes in #1579 Co-authored-by: Geetika Batra <[email protected]> Signed-off-by: Puneet Katyal <[email protected]>
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# GPU enabled clusters using vGPU | ||
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## Overview | ||
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You can choose to create a cluster with both worker and control plane nodes having vGPU devices attached to them. | ||
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Before we begin, a few important things to note: | ||
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- [NVIDIA GPU Operator](https://github.com/NVIDIA/gpu-operator) is used to expose the GPU PCI devices to the workloads running on the cluster. | ||
- The OVA templates used for cluster creation should have the VMX version (Virtual Hardware) set to 17 or higher. This is necessary because Dynamic DirectPath I/O was introduced in this version, which enables the Assignable Hardware intelligence for passthrough devices. | ||
- Since we need the VMX version to be >=17, this way of provisioning clusters with PCI passthrough devices works for vSphere 7.0 and above. This is the ESXi/VMX version [compatibility list](https://kb.vmware.com/s/article/2007240). | ||
- UEFI boot mode is recommended for the OVAs used for cluster creation. | ||
- Most of the setup is similar to [GPU enabled clusters via PCI Passthrough](https://github.com/kubernetes-sigs/cluster-api-provider-vsphere/blob/main/docs/gpu-pci.md#create-the-cluster). | ||
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## An example GPU enabled cluster | ||
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Let's create a CAPV cluster with vGPU enabled nodes. | ||
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### Prerequisites | ||
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- Refer the [NVIDIA Virtual GPU Software Quick Start Guide](https://docs.nvidia.com/grid/latest/grid-software-quick-start-guide/index.html) to download and install the vGPU software and configure vGPU licensing. | ||
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- Ensure vGPU compatibility for your vSphere installation and the GPU devices using the [VMware Compatibility Guide - Shared Pass-through Graphics](https://www.vmware.com/resources/compatibility/search.php?deviceCategory=vgpu) | ||
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- Enable Shared Passthrough for the GPU device on the ESXi Host | ||
- Browse to a host in the vSphere Client navigator. | ||
- On the **Configure** tab, expand **Hardware** and click **Graphics**. | ||
- Under **GRAPHICS DEVICES**, select the GPU device to be used for vGPU, click **EDIT...** and select **Shared Direct**. Repeat this for additional GPU devices as needed. | ||
- Select **HOST GRAPHICS**, click **EDIT...** and select **Shared Direct** and select a shared passthrough GPU assignment policy, for example **Group VMs on GPU until full (GPU consolidation)**. | ||
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- Build an OVA template | ||
We can build a custom OVA template using the [image-builder](https://github.com/kubernetes-sigs/image-builder) project. We will build a Ubuntu 20.04 OVA with UEFI boot mode. More documentation on how to use image-builder can be found in the [image-builder book](https://image-builder.sigs.k8s.io/capi/providers/vsphere.html) | ||
- Clone the repo locally and go to the `./images/capi/` directory. | ||
- Create a `packer-vars.json` file with the following content. | ||
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```shell | ||
$ cat packer-vars.json | ||
{ | ||
"vmx_version": 17 | ||
} | ||
``` | ||
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- Run the make file target associated to ubuntu 20.04 UEFI OVA as follows: | ||
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```shell | ||
> PACKER_VAR_FILES=packer-vars.json make build-node-ova-vsphere-ubuntu-2004-efi | ||
``` | ||
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### Source the vGPU profile(s) for the GPU device | ||
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See "2. Choosing the vGPU Profile for the Virtual Machine" at [Using GPUs with Virtual Machines on vSphere](https://blogs.vmware.com/apps/2018/09/using-gpus-with-virtual-machines-on-vsphere-part-3-installing-the-nvidia-grid-technology.html) to see what vGPU profiles are available for your GPU device. | ||
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We are using NVIDIA Tesla V100 32GB cards for this example and will use the `grid_v100d-4c` vGPU profile for this card that allocates 4GB GPU memory to the worker node's vGPU device. | ||
### Create the cluster template | ||
```shell | ||
$ make dev-flavors | ||
/Applications/Xcode.app/Contents/Developer/usr/bin/make generate-flavors FLAVOR_DIR=/Users/pkatyal/.cluster-api/overrides/infrastructure-vsphere/v0.0.0 | ||
go run ./packaging/flavorgen --output-dir /Users/pkatyal/.cluster-api/overrides/infrastructure-vsphere/v0.0.0 | ||
``` | ||
Edit the generated Cluster template (`cluster-template.yaml`) to set the values for the `vgpuDevices` array. Here we are editing the VSphereMachineTemplate object for the worker nodes. This will create a worker node with a single NVIDIA 16GB vGPU device attached to the VM. | ||
```yaml | ||
--- | ||
apiVersion: infrastructure.cluster.x-k8s.io/v1beta1 | ||
kind: VSphereMachineTemplate | ||
metadata: | ||
name: ${CLUSTER_NAME}-worker | ||
namespace: '${NAMESPACE}' | ||
spec: | ||
template: | ||
spec: | ||
cloneMode: linkedClone | ||
datacenter: '${VSPHERE_DATACENTER}' | ||
datastore: '${VSPHERE_DATASTORE}' | ||
diskGiB: 25 | ||
folder: '${VSPHERE_FOLDER}' | ||
memoryMiB: 8192 | ||
network: | ||
devices: | ||
- dhcp4: true | ||
networkName: '${VSPHERE_NETWORK}' | ||
numCPUs: 2 | ||
os: Linux | ||
powerOffMode: trySoft | ||
resourcePool: '${VSPHERE_RESOURCE_POOL}' | ||
server: '${VSPHERE_SERVER}' | ||
storagePolicyName: '${VSPHERE_STORAGE_POLICY}' | ||
template: '${VSPHERE_TEMPLATE}' | ||
thumbprint: '${VSPHERE_TLS_THUMBPRINT}' | ||
vgpuDevices: | ||
- profileName: "grid_v100d-4c" <============ value from above | ||
``` | ||
Set the required values for the other fields and the cluster template is ready for use. The similar changes can be made to a template generated using clusterctl generate cluster command as well. | ||
### Create the cluster | ||
Set the size of the GPU nodes appropriately, since the Nvidia gpu-operator requires additional CPU and memory to install the device drivers on the VMs. | ||
Note: For GPU nodes (PCI Passthrough or vGPU), all memory of the nodes must be reserved. CAPV will automatically do this for nodes that have a PCI Passthrough GPU or a vGPU device in the spec. See "Memory Reservation" at [Using GPUs with Virtual Machines on vSphere](https://blogs.vmware.com/apps/2018/09/using-gpus-with-virtual-machines-on-vsphere-part-2-vmdirectpath-i-o.html) | ||
Apply the manifest from the previous step to your management cluster to have CAPV create a workload cluster with worker nodes that have vGPUs. | ||
From this point on, the setup is exactly the same as [GPU enabled clusters via PCI Passthrough](https://github.com/kubernetes-sigs/cluster-api-provider-vsphere/blob/main/docs/gpu-pci.md#create-the-cluster). |
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test/e2e/data/infrastructure-vsphere/main/vgpu/kustomization.yaml
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apiVersion: kustomize.config.k8s.io/v1beta1 | ||
kind: Kustomization | ||
resources: | ||
- ../base | ||
patchesStrategicMerge: | ||
- vgpu-device-template.yaml |
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test/e2e/data/infrastructure-vsphere/main/vgpu/vgpu-device-template.yaml
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--- | ||
apiVersion: infrastructure.cluster.x-k8s.io/v1beta1 | ||
kind: VSphereMachineTemplate | ||
metadata: | ||
name: ${CLUSTER_NAME}-worker | ||
namespace: ${NAMESPACE} | ||
spec: | ||
template: | ||
spec: | ||
vgpuDevices: | ||
- profileName: ${PROFILE_NAME} |