|
| 1 | +# GkeGpuDirectTCPXCluster |
| 2 | + |
| 3 | +This example deploys [Google Cloud GPU supercomputer](https://cloud.google.com/kubernetes-engine/docs/how-to/gpu-bandwidth-gpudirect-tcpx) which is accelerator-optimized for scalable, massive models. The RGD is installed by platform administrators who facilitate ML infrastructure for self service teams. |
| 4 | + |
| 5 | +The cluster has: |
| 6 | +* Eight NVIDIA H100 GPUs per machine. |
| 7 | +* Up to 200 Gbps bandwidth on the primary NIC. |
| 8 | +* Secondary NICs (up to four on A3 High machine types), each supporting up to 200 Gbps bandwidth for GPU data transfer. |
| 9 | + |
| 10 | +This deployment maximizes network bandwidth and throughput for high-performance GPU workloads in Google Kubernetes Engine (GKE) Standard clusters by using, GPUDirect-TCPX, gVNIC, and multi-networking. |
| 11 | + |
| 12 | +* GKE cluster |
| 13 | +* Container Node Pools |
| 14 | +* Network |
| 15 | +* Subnetwork |
| 16 | +* GKE Network and NetworkParams |
| 17 | + |
| 18 | +Everything related to these resources would be hidden from the end user, simplifying their experience. |
| 19 | + |
| 20 | + |
| 21 | + |
| 22 | +<!-- |
| 23 | +meta { |
| 24 | + title "Gke GpuDirect TCPX Cluster" |
| 25 | +} |
| 26 | +
|
| 27 | +elements { |
| 28 | + gcp { |
| 29 | + group k8sconfig { |
| 30 | + name "Kubernetes Manifests" |
| 31 | + card kubernetes as config1 { |
| 32 | + name "Network" |
| 33 | + } |
| 34 | + card kubernetes as config2 { |
| 35 | + name "GKENetworkParamSet " |
| 36 | + } |
| 37 | + } |
| 38 | +
|
| 39 | + group Network { |
| 40 | + card firewall as fw1 { |
| 41 | + name "firewall 1" |
| 42 | + } |
| 43 | + card firewall as fw2 { |
| 44 | + name "firewall 2" |
| 45 | + } |
| 46 | + card firewall as fw3 { |
| 47 | + name "firewall 3" |
| 48 | + } |
| 49 | + card firewall as fw4 { |
| 50 | + name "firewall 4" |
| 51 | + } |
| 52 | + |
| 53 | + card network as net1 { |
| 54 | + name "net 1" |
| 55 | + } |
| 56 | + card network as net2 { |
| 57 | + name "net 2" |
| 58 | + } |
| 59 | + card network as net3 { |
| 60 | + name "net 3" |
| 61 | + } |
| 62 | + card network as net4 { |
| 63 | + name "net 4" |
| 64 | + } |
| 65 | + card network as snet1 { |
| 66 | + name "subnet 1" |
| 67 | + } |
| 68 | + card network as snet2 { |
| 69 | + name "subnet 2" |
| 70 | + } |
| 71 | + card network as snet3 { |
| 72 | + name "subnet 3" |
| 73 | + } |
| 74 | + card network as snet4 { |
| 75 | + name "subnet 4" |
| 76 | + } |
| 77 | + } |
| 78 | + group GKE { |
| 79 | + card gke as cluster { |
| 80 | + name "cluster" |
| 81 | + } |
| 82 | + |
| 83 | + group default { |
| 84 | + name "Default Nodepool" |
| 85 | + card gke as defaultNodepool { |
| 86 | + name "nodepool" |
| 87 | + } |
| 88 | + card gce as generalVM { |
| 89 | + name "e2-medium" |
| 90 | + } |
| 91 | + } |
| 92 | + |
| 93 | + group gpu { |
| 94 | + name "GPU Nodepool" |
| 95 | + card gke as gpuNodepool { |
| 96 | + name "nodepool " |
| 97 | + } |
| 98 | + card gce as gpuVM { |
| 99 | + name "a3-highgpu-8g" |
| 100 | + } |
| 101 | + card gpu as nvidia { |
| 102 | + name "Nvidia H100" |
| 103 | + } |
| 104 | + } |
| 105 | + |
| 106 | + } |
| 107 | + |
| 108 | + } |
| 109 | +} |
| 110 | +
|
| 111 | +paths { |
| 112 | + fw1 -\-> net1 |
| 113 | + fw2 -\-> net2 |
| 114 | + fw3 -\-> net3 |
| 115 | + fw4 -\-> net4 |
| 116 | + |
| 117 | + net1 -\-> snet1 |
| 118 | + net2 -\-> snet2 |
| 119 | + net3 -\-> snet3 |
| 120 | + net4 -\-> snet4 |
| 121 | + |
| 122 | + config1 -\-> config2 |
| 123 | + |
| 124 | + defaultNodepool -\-> generalVM |
| 125 | + gpuNodepool -\-> gpuVM |
| 126 | + gpuVM -\-> nvidia |
| 127 | +
|
| 128 | + Network -right-> GKE |
| 129 | + k8sconfig -right-> cluster |
| 130 | +} |
| 131 | +--> |
| 132 | + |
| 133 | + |
| 134 | +## End User: GkeGpuDirectTCPXCluster |
| 135 | + |
| 136 | +The administrator needs to install the RGD first. |
| 137 | +The end user creates a `GkeGpuDirectTCPXCluster` resource something like this: |
| 138 | + |
| 139 | +```yaml |
| 140 | +apiVersion: kro.run/v1alpha1 |
| 141 | +kind: GkeGpuDirectTCPXCluster |
| 142 | +metadata: |
| 143 | + name: gpu-demo |
| 144 | + namespace: config-connector |
| 145 | +spec: |
| 146 | + name: gpu-demo # Name used for all resources created as part of this RGD |
| 147 | + location: us-central1 # Region where the GCP resources are created |
| 148 | +``` |
| 149 | +
|
| 150 | +They can then check the status of the applied resource: |
| 151 | +
|
| 152 | +``` |
| 153 | +kubectl get gkegpudirecttcpxcluster |
| 154 | +kubectl get gkegpudirecttcpxcluster gpu-demo -n config-connector -o yaml |
| 155 | +``` |
| 156 | + |
| 157 | +Navigate to GKE Cluster page in the GCP Console and verify the cluster creation. |
| 158 | + |
| 159 | +Once done, the user can delete the `GkeGpuDirectTCPXCluster` instance: |
| 160 | + |
| 161 | +``` |
| 162 | +kubectl delete gkegpudirecttcpxcluster gpu-demo -n config-connector |
| 163 | +``` |
| 164 | + |
| 165 | +## Administrator: ResourceGraphDefinition |
| 166 | +The administrator needs to install the RGD in the cluster first before the user can consume it: |
| 167 | + |
| 168 | +``` |
| 169 | +kubectl apply -f rgd.yaml |
| 170 | +``` |
| 171 | + |
| 172 | +Validate the RGD is installed correctly: |
| 173 | + |
| 174 | +``` |
| 175 | +kubectl get rgd gkegpudirecttcpxcluster.kro.run |
| 176 | +``` |
| 177 | + |
| 178 | +Once all user created instances are deleted, the administrator can choose to deleted the RGD. |
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