fix(validators): pair the TCPXO sidecar with the installer AICR ships

The performance validator's GKE H100 TrainingRuntime pinned tcpgpudmarxd-dev
v1.0.20, which Google pairs with plugin installer v1.0.14. AICR's recipe
deploys installer v1.0.15, whose partner is v1.0.21. That runtime is rendered
and run on a live cluster during the performance phase, so the benchmark
executed against a pair Google documents as unsupported.

Renovate excludes **/testdata/** so the pin could never be updated
automatically, and ignoreDeps listed only the installer half of the pair,
leaving the daemon free to move on its own. Add the daemon so the coupling is
symmetric.

Also move the demo workload from the v1.0.14/v1.0.20 pair to v1.0.15/v1.0.21
so it matches the installer the recipe deploys, and update the two doc notes
that described it as pinned to the earlier pair.

Signed-off-by: Yuan Chen <yuanchen97@gmail.com>

Author: yuanchen8911

.github/renovate.json5

@@ -363,9 +363,14 @@
   //   workload-side daemon (tcpgpudmarxd-dev) must move together; an
   //   independent Renovate bump on this image alone would create
   //   stack version skew. See docs/integrator/gke-tcpxo-networking.md.
+  // - tcpgpudmarxd-dev: the workload-side half of the same TCPXO pair. Listed
+  //   alongside the installer so the coupling is symmetric — excluding only
+  //   the installer would leave the daemon free to move on its own, which is
+  //   the same stack version skew from the opposite direction.
   ignoreDeps: [
     "602401143452.dkr.ecr.us-west-2.amazonaws.com/eks/aws-efa-k8s-device-plugin",
     "us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/nccl-plugin-gpudirecttcpx-dev",
+    "us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/tcpgpudmarxd-dev",
   ],
 
   // Paths Renovate should not scan.

demos/workloads/training/gke-nccl-test-tcpxo.yaml

@@ -127,7 +127,7 @@ spec:
     cloud.google.com/gke-accelerator: nvidia-h100-mega-80gb
   containers:
     - name: tcpxo-daemon
-      image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/tcpgpudmarxd-dev:v1.0.20
+      image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/tcpgpudmarxd-dev:v1.0.21
       imagePullPolicy: Always
       command: ["/bin/sh", "-c"]
       args:
@@ -150,7 +150,7 @@ spec:
         - name: LD_LIBRARY_PATH
           value: /usr/local/nvidia/lib64
     - name: nccl-test
-      image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/nccl-plugin-gpudirecttcpx-dev:v1.0.14
+      image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/nccl-plugin-gpudirecttcpx-dev:v1.0.15
       imagePullPolicy: Always
       command:
         - /bin/sh
@@ -242,7 +242,7 @@ spec:
     cloud.google.com/gke-accelerator: nvidia-h100-mega-80gb
   containers:
     - name: tcpxo-daemon
-      image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/tcpgpudmarxd-dev:v1.0.20
+      image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/tcpgpudmarxd-dev:v1.0.21
       imagePullPolicy: Always
       command: ["/bin/sh", "-c"]
       args:
@@ -265,7 +265,7 @@ spec:
         - name: LD_LIBRARY_PATH
           value: /usr/local/nvidia/lib64
     - name: nccl-test
-      image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/nccl-plugin-gpudirecttcpx-dev:v1.0.14
+      image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/nccl-plugin-gpudirecttcpx-dev:v1.0.15
       imagePullPolicy: Always
       command:
         - /bin/sh

docs/integrator/gke-tcpxo-networking.md

@@ -179,7 +179,7 @@ Running a **Kubeflow TrainJob** rather than a bare Pod? A TrainJob cannot add
 the `tcpxo-daemon` sidecar, so the wiring must live in a `TrainingRuntime` — see
 [Attaching a Training Workload to the Cluster Fabric](../user/fabric-attached-training.md).
 
-See [`demos/workloads/training/gke-nccl-test-tcpxo.yaml`](https://github.com/NVIDIA/aicr/blob/main/demos/workloads/training/gke-nccl-test-tcpxo.yaml) for a complete 2-node NCCL benchmark example. (pinned to an earlier coupled pair, plugin `v1.0.14` with daemon `v1.0.20`)
+See [`demos/workloads/training/gke-nccl-test-tcpxo.yaml`](https://github.com/NVIDIA/aicr/blob/main/demos/workloads/training/gke-nccl-test-tcpxo.yaml) for a complete 2-node NCCL benchmark example. (pinned to the same coupled pair the recipe ships, plugin `v1.0.15` with daemon `v1.0.21`)
 
 ## NCCL Plugin Version Matching
 
@@ -258,8 +258,9 @@ NRI profile (recommended, no `hostNetwork`):
 
 ```shell
 kubectl create ns nccl-test
-# Note: this manifest is pinned to the earlier v1.0.14 / v1.0.20 pair.
-# Update both images to your cluster's pair before applying.
+# Note: this manifest is pinned to the v1.0.15 / v1.0.21 pair, matching the
+# installer the recipe deploys. If your cluster runs a different installer
+# version, update both images to that cluster's pair before applying.
 kubectl apply -f demos/workloads/training/gke-nccl-test-tcpxo.yaml -n nccl-test
 
 # Wait for pods to be 2/2 Running

validators/performance/testdata/h100/gke/runtime.yaml

@@ -215,7 +215,7 @@ spec:
                 # alongside the worker. Uses NRI device injection (devices.gke.io
                 # annotation) for GPU device access instead of privileged mode.
                 - name: tcpxo-daemon
-                  image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/tcpgpudmarxd-dev:v1.0.20
+                  image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/tcpgpudmarxd-dev:v1.0.21
                   imagePullPolicy: Always
                   restartPolicy: Always
                   command: ["/bin/sh", "-c"]