feat(recipes): add GKE GB200 (A4X) recipe with NVLS NCCL validation - #2338
feat(recipes): add GKE GB200 (A4X) recipe with NVLS NCCL validation#2338mikecook wants to merge 1 commit into
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Request changes: five verified merge blockers in the GKE A4X network model and ownership, health validation, supply-chain pinning, and scheduling scope. CI is green on this head but does not cover these failure directions. One additional documentation mismatch is inline. The branch being behind main is mechanical and separate.
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📝 WalkthroughWalkthroughAdds GB200 GKE COS inference and training recipes with RDMA/RoCE networking, ARM64 NCCL gIB installation, GPU Operator configuration, and workload-specific overlays. Adds NCCL NVLS support with bounded cleanup handling. Adds health checks, rendering tests, recipe coverage, golden fixtures, and documentation for networking, drivers, storage, benchmarking, and deployment validation. Estimated code review effort: 4 (Complex) | ~60 minutes Merge Risk: 🔵 Low · up to This change adds GB200 GKE recipes, RDMA setup, and NVLS validation support. Some documentation, runtime-dependency, fixture-safety, and regression-coverage concerns remain, but the identified impact is bounded and should be addressed with owner awareness. Suggested reviewers: 🚥 Pre-merge checks | ✅ 4✅ Passed checks (4 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Inline comments:
In `@validators/performance/trainer_lifecycle_test.go`:
- Around line 105-156: Refactor TestApplyControllerTolerations into a
table-driven test covering the existing Deployment and non-Deployment cases. Add
cases with missing spec.template.spec and malformed tolerations, asserting
applyControllerTolerations returns an error for each mutation failure while
retaining the current success and preservation assertions.
In `@validators/performance/trainer_lifecycle.go`:
- Around line 169-188: Restrict applyControllerTolerations to only the Trainer
controller and JobSet controller Deployments before mutating
spec.template.spec.tolerations; leave all other Deployments unchanged. Add
coverage verifying a non-controller Deployment is not modified while both
supported controller Deployments retain the blanket toleration behavior.
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docs/integrator/components/nodewright.mddocs/user/container-images.mddocs/user/validation.mdpkg/bundler/testdata/stock_render_golden.yamlpkg/defaults/timeouts.gopkg/recipe/metadata_test.gopkg/recipe/nccl_bandwidth_floor_test.gopkg/recipe/testdata/catalog_parity_golden.yamlpkg/recipe/testdata/coverage_golden.yamlpkg/tuning/compute_test.gorecipes/checks/gke-gb200-rdma/health-check.yamlrecipes/components/gke-gb200-rdma/manifests/nccl-gib-installer-arm64.yamlrecipes/components/gke-gb200-rdma/manifests/network-params.yamlrecipes/gke_gb200_rdma_test.gorecipes/manifest_images_test.gorecipes/overlays/gb200-gke-cos-inference.yamlrecipes/overlays/gb200-gke-cos-training.yamlrecipes/registry.yamlvalidators/performance/consts.govalidators/performance/inference_perf_constraint.govalidators/performance/nccl_all_reduce_bw_constraint.govalidators/performance/nccl_benchmark_profile_test.govalidators/performance/nccl_test.govalidators/performance/testdata/gb200/gke/runtime-nvls.yamlvalidators/performance/trainer_lifecycle.govalidators/performance/trainer_lifecycle_test.go
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Actionable comments posted: 5
🔇 Additional comments (28)
docs/README.md (1)
49-49: LGTM!docs/contributor/validator.md (1)
807-807: LGTM!docs/index.yml (1)
78-79: LGTM!docs/integrator/gke-gb200-networking.md (2)
17-20: 🗄️ Data Integrity & Integration
⚠️ Unverified finding
Sandbox verification was unavailable.Verify the documented DaemonSet name.
This page names the bundled resource
nccl-rdma-installer, but the component context identifies the manifest asnccl-gib-installer-arm64.yaml. Verifymetadata.namein the manifest. If it differs, update both references so operators can identify the deployed resource by the documented name.Verification command
Also applies to: 74-75
1-16: LGTM!Also applies to: 21-73, 76-94, 98-281
docs/integrator/gke-gpu-setup.md (1)
215-222: LGTM!Also applies to: 441-441
docs/integrator/index.md (1)
25-25: LGTM!docs/user/validation.md (1)
52-54: LGTM!Also applies to: 179-180, 402-405
docs/user/recipe-health.md (1)
43-44: LGTM!Also applies to: 80-85, 91-91
recipes/components/gke-gb200-rdma/manifests/nccl-gib-installer-arm64.yaml (1)
66-69: LGTM!Also applies to: 92-92
recipes/registry.yaml (1)
187-206: LGTM!recipes/checks/gke-gb200-rdma/health-check.yaml (1)
28-143: LGTM!pkg/chainsaw/gke_gb200_rdma_check_states_test.go (1)
33-168: LGTM!docs/user/container-images.md (1)
22-23: LGTM!Also applies to: 43-43, 138-142
pkg/recipe/testdata/coverage_golden.yaml (1)
1042-1097: LGTM!Also applies to: 3496-3624
pkg/bundler/testdata/stock_render_golden.yaml (1)
19-21: LGTM!recipes/overlays/gb200-gke-cos-training-slurm.yaml (1)
108-144: 🗄️ Data Integrity & IntegrationNo change needed.
resourceClaimTemplateName: slinky-slurm-imex-channelsmatches the ComputeDomain manifest and the EKS GB200 Slurm leaf.recipes/overlays/gb200-gke-cos-inference.yaml (1)
21-99: LGTM!recipes/overlays/gb200-gke-cos-training.yaml (1)
20-113: LGTM!recipes/overlays/gb200-gke-cos-inference-dynamo.yaml (1)
15-99: LGTM!pkg/recipe/metadata_test.go (1)
2310-2311: LGTM!Also applies to: 2532-2581
pkg/recipe/testdata/catalog_parity_golden.yaml (1)
19-21: LGTM!docs/integrator/components/nodewright.md (1)
89-89: LGTM!pkg/tuning/compute_test.go (1)
51-51: LGTM!pkg/defaults/timeouts.go (1)
670-670: LGTM!recipes/evidence/allowlist.yaml (1)
84-85: LGTM!pkg/recipe/nccl_bandwidth_floor_test.go (1)
136-148: LGTM!Also applies to: 150-222
validators/performance/testdata/gb200/gke/runtime-nvls.yaml (1)
19-25: 🩺 Stability & AvailabilityNo IMEX setup change is needed. The GB200 GKE recipe selects
nccl-all-reduce-bw-nvlswithoutnccl-benchmark-runtime; the validator loadsvalidators/performance/testdata/gb200/gke/runtime-nvls.yamland creates the IMEXComputeDomainbefore theTrainJob.
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Inline comments:
In `@docs/integrator/gke-gb200-networking.md`:
- Around line 95-97: Update the networking documentation sentence to describe
a4x-highgpu-4g recipes generated with the gpuStack=driver-installer option,
rather than pools built with that option. Keep gpu-driver-version=disabled
stated separately as the node-pool prerequisite.
In `@recipes/components/gke-gb200-rdma/manifests/nccl-gib-installer-arm64.yaml`:
- Around line 87-90: Remove the unused nvidia-dir volume declaration from the
pod manifest; no container mounts it, so do not retain its hostPath
precondition. If the volume is intentionally required by the upstream vendored
configuration, keep it and add a comment documenting that rationale.
In `@recipes/gke_gb200_rdma_test.go`:
- Around line 56-76: Consolidate
TestGB200RDMAInstallerAcceleratedNodeSelectorScopesRender and
TestGB200RDMAInstallerNoAcceleratedNodeSelectorOmitsField into one table-driven
test covering present and absent acceleratedNodeSelector values. Define per-case
values and expected selector state, render through renderGB200RDMAInstaller, and
retain assertions for both the rendered selector contents and omission when
unset.
- Around line 45-48: Update the pod-spec lookup before the final assertion to
validate each nested map conversion for doc["spec"], its "template", and the
template's "spec"; on any missing or incorrectly typed level, call t.Fatalf with
the rendered manifest and avoid chained type assertions that can panic. Preserve
the existing successful extraction into spec.
In `@recipes/overlays/gb200-gke-cos-training-kubeflow.yaml`:
- Around line 38-47: The kubeflow-trainer component reference currently includes
only the generic distributed training runtime, so add the GB200 NVLS-specific
runtime manifest with its IMEX resourceClaims wiring. Ensure the overlay also
provisions or references the matching ComputeDomain and ResourceClaimTemplate,
and registers any required manifest or dependency references alongside
kubeflow-trainer.
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docs/README.mddocs/contributor/validator.mddocs/index.ymldocs/integrator/components/nodewright.mddocs/integrator/gke-gb200-networking.mddocs/integrator/gke-gpu-setup.mddocs/integrator/index.mddocs/user/container-images.mddocs/user/recipe-health.mddocs/user/validation.mdpkg/bundler/testdata/stock_render_golden.yamlpkg/chainsaw/gke_gb200_rdma_check_states_test.gopkg/defaults/timeouts.gopkg/recipe/metadata_test.gopkg/recipe/nccl_bandwidth_floor_test.gopkg/recipe/testdata/catalog_parity_golden.yamlpkg/recipe/testdata/coverage_golden.yamlpkg/tuning/compute_test.gorecipes/checks/gke-gb200-rdma/health-check.yamlrecipes/components/gke-gb200-rdma/manifests/nccl-gib-installer-arm64.yamlrecipes/evidence/allowlist.yamlrecipes/evidence/gb200-gke-cos-inference-dynamo-gpustack-driver-installer/2e85f8702c6214cafcc0ed714d928720/sha256-03abdc89a75fc91e9cf01767ceeadf74735642c9fd267348a7346946c9f34873.yamlrecipes/evidence/gb200-gke-cos-training-gpustack-driver-installer/2e85f8702c6214cafcc0ed714d928720/sha256-6fb01e4fe1550814f1a45d91a9528cb005fabbd1d5210b3e915614782085cdad.yamlrecipes/evidence/gb200-gke-cos-training-kubeflow-gpustack-driver-installer/2e85f8702c6214cafcc0ed714d928720/sha256-2575ba7d248136c7a93704daf7e48b262ddee1a05d4e3644329682e858c7e19b.yamlrecipes/evidence/gb200-gke-cos-training-slurm-gpustack-driver-installer/2e85f8702c6214cafcc0ed714d928720/sha256-6436674d5fb875a03c0dacf9d0cf3c1b558d27c75fa9c7922f2b095996160af4.yamlrecipes/gke_gb200_rdma_test.gorecipes/overlays/gb200-gke-cos-inference-dynamo.yamlrecipes/overlays/gb200-gke-cos-inference.yamlrecipes/overlays/gb200-gke-cos-training-kubeflow.yamlrecipes/overlays/gb200-gke-cos-training-slurm.yamlrecipes/overlays/gb200-gke-cos-training.yamlrecipes/registry.yamlvalidators/performance/consts.govalidators/performance/inference_perf_constraint.govalidators/performance/nccl_all_reduce_bw_constraint.govalidators/performance/nccl_benchmark_profile_test.govalidators/performance/nccl_test.govalidators/performance/testdata/gb200/gke/runtime-nvls.yamlvalidators/performance/trainer_lifecycle.govalidators/performance/trainer_lifecycle_test.go
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Inline comments:
In `@docs/integrator/gke-gb200-networking.md`:
- Around line 27-32: Update the GKE networking documentation to describe the
supported topology: two VPCs total, with one gVNIC VPC and one RDMA VPC
containing four subnets. Revise the additionalNodeNetworkConfigs and related
naming examples to match, and set deviceMode to RDMA for rdma-0 through rdma-3
while retaining NetDevice only for the gVNIC configuration.
Apply the same fix in `@docs/integrator/gke-gb200-networking.md` around lines 63 -
65: Covered by the consolidated requirement to set RDMA mode on all four RDMA
network parameter sets.
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Inline comments:
In `@docs/integrator/gke-gb200-networking.md`:
- Around line 89-90: Update the networking documentation around the expected
Network names to separate the default Network from the five prerequisite custom
Networks; state the spec.parametersRef binding requirement only for gvnic-1 and
rdma-0 through rdma-3, and describe default independently.
- Around line 76-80: Update the prerequisite networking documentation near the
AICR and GKE version-floor guidance to explicitly state that AICR GB200 recipes
require Kubernetes server version 1.34 or later, including the inherited
requirement for the training Slurm overlay.
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Inline comments:
In `@docs/integrator/gke-gb200-networking.md`:
- Around line 103-106: Update the networking documentation paragraph to describe
default as GKE’s built-in Pod network, created automatically when
multi-networking is enabled; remove the claim that operators will see a
default-named Network/GKENetworkParamSet pair and that it is excluded only by
name.
- Around line 91-99: Update the verification commands in the GKE networking
prerequisites section to use explicit custom columns or YAML output that exposes
each Network’s spec.parametersRef and each GKENetworkParamSet’s spec.deviceMode,
while retaining the existing resource checks.
- Around line 255-260: Update the documentation around the standard-rwo warning
to clarify that GKE Standard does not inherently make it the default
StorageClass. Retain the pd-balanced incompatibility warning for a4x-highgpu-4g
nodes, and instruct users to inspect their cluster’s default StorageClass,
provisioner, and disk type before choosing hyperdisk-balanced.
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docs/integrator/gke-gb200-networking.mdrecipes/evidence/gb200-gke-cos-inference-dynamo-gpustack-driver-installer/2e85f8702c6214cafcc0ed714d928720/sha256-b773e3c3cab4cab45a54d362ac1ba186ba323e3ebed8a5f06f7a219795f863d6.yamlrecipes/evidence/gb200-gke-cos-training-gpustack-driver-installer/2e85f8702c6214cafcc0ed714d928720/sha256-f3d73f18294befb970a1b4e06221e29532db2f1aabb238a92f76de92374b62b5.yamlrecipes/evidence/gb200-gke-cos-training-kubeflow-gpustack-driver-installer/2e85f8702c6214cafcc0ed714d928720/sha256-dff411a2a9abd4437d83b102a44daa684818a97d3cdd5a88fc5f9917ad425577.yamlrecipes/evidence/gb200-gke-cos-training-slurm-gpustack-driver-installer/2e85f8702c6214cafcc0ed714d928720/sha256-f28dad641515d8b87872e5795dc7fa4eb12e64c9ddfd433d0161d560fe007eae.yaml
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Actionable comments posted: 1
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Inline comments:
In `@validators/performance/nccl_all_reduce_bw_constraint.go`:
- Around line 490-498: Register the namespace cleanup defer immediately after
ensureNamespace succeeds, before calling ensureTrainerInstalled, so all
subsequent failure paths remove the generated namespace. Preserve the existing
cleanup behavior and add coverage for Trainer installation failure to verify the
namespace is deleted.
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pkg/bundler/testdata/stock_render_golden.yamlpkg/defaults/timeouts.gopkg/recipe/testdata/catalog_parity_golden.yamlrecipes/checks/aws-efa/health-check.yamlrecipes/checks/gke-nccl-tcpxo/health-check.yamlrecipes/checks/nfd/health-check.yamlrecipes/checks/nvidia-dra-driver-gpu/health-check.yamlrecipes/checks/slinky-topograph/health-check.yamlvalidators/performance/inference_perf_constraint.govalidators/performance/nccl_all_reduce_bw_constraint.govalidators/performance/nccl_roce_apply_test.govalidators/performance/trainer_lifecycle.govalidators/performance/trainer_lifecycle_test.go
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| slog.Debug("RoCE ResourceClaimTemplate not present (non-RoCE variant), skipping", "name", ncclRoceClaimName) | ||
| default: | ||
| slog.Warn("failed to delete RoCE ResourceClaimTemplate", "error", err, "name", ncclRoceClaimName) | ||
| err := clientset.CoreV1().Namespaces().Delete(cleanupCtx, namespace, metav1.DeleteOptions{}) |
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MAJOR — Wait for namespace termination before reporting cleanup success. Namespaces().Delete only starts asynchronous deletion; Kubernetes can leave this namespace Terminating while child finalizers run (pinned Kubernetes docs). This function immediately logs Deleted and returns nil, so a successful benchmark can report clean teardown while its ComputeDomain, ResourceClaimTemplate, and namespace remain. The existing five-minute wait when a later run encounters a terminating namespace confirms this state is expected, but it is too late to fail the run that leaked the resources.
Minimum correction: after issuing the delete, wait boundedly for the namespace to disappear and return a cleanup error on timeout; add a finalizer-held/terminating namespace regression case.
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Done — cleanupNCCLResources (validators/performance/nccl_all_reduce_bw_constraint.go)
now returns the waitForNamespaceGone error instead of logging a warning and
returning nil on timeout, so a namespace stuck on a finalizer fails the
check instead of reporting a clean "Deleted" while a ComputeDomain or RoCE
ResourceClaimTemplate leaks behind it.
Made the wait bound an explicit terminationWait parameter (production
still passes defaults.InferenceNamespaceTerminationWait; tests inject a
short duration) and added a regression test,
TestCleanupNCCLResources_ReturnsErrorOnTerminationTimeout
(nccl_roce_apply_test.go), that holds the namespace with a
finalizer/DeletionTimestamp reactor and asserts an ErrCodeTimeout-wrapped
cleanup failure.
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Actionable comments posted: 2
🤖 Prompt for all review comments with AI agents
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.
Inline comments:
In `@docs/contributor/validator.md`:
- Line 816: Update the AICR_INFERENCE_PERF_MODEL_CACHE_STORAGE_CLASS guidance
and its corresponding GKE Storage Prerequisites documentation to instruct users
to inspect the cluster’s actual default StorageClass and disk type before
choosing the model-cache class, rather than assuming standard-rwo on all GKE
Standard clusters. Preserve the requirement that A4X/GB200 nodes use a
Hyperdisk-backed class because pd-balanced cannot attach to a4x-highgpu-4g, and
apply the consistent wording in both documents.
In `@pkg/recipe/nccl_bandwidth_floor_test.go`:
- Around line 157-180: Add Kubeflow and Slurm cases to
TestGB200GKENCCLBandwidthFloor: expect Kubeflow GB200 GKE COS training to have
performance enabled with a “>= 250” floor, and add the
gb200-gke-cos-training-slurm case expecting no performance or constraint.
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docs/README.mddocs/contributor/validator.mddocs/index.ymldocs/integrator/components/nodewright.mddocs/integrator/gke-gb200-networking.mddocs/integrator/gke-gpu-setup.mddocs/integrator/index.mddocs/user/container-images.mddocs/user/recipe-health.mddocs/user/validation.mdpkg/bundler/testdata/stock_render_golden.yamlpkg/chainsaw/gke_gb200_rdma_check_states_test.gopkg/recipe/metadata_test.gopkg/recipe/nccl_bandwidth_floor_test.gopkg/recipe/testdata/catalog_parity_golden.yamlpkg/recipe/testdata/coverage_golden.yamlpkg/tuning/compute_test.gorecipes/checks/gke-gb200-rdma/health-check.yamlrecipes/components/gke-gb200-rdma/manifests/nccl-gib-installer-arm64.yamlrecipes/gke_gb200_rdma_test.gorecipes/overlays/gb200-gke-cos-inference-dynamo.yamlrecipes/overlays/gb200-gke-cos-inference.yamlrecipes/overlays/gb200-gke-cos-training-kubeflow.yamlrecipes/overlays/gb200-gke-cos-training-slurm.yamlrecipes/overlays/gb200-gke-cos-training.yamlrecipes/registry.yamlvalidators/performance/nccl_all_reduce_bw_constraint.govalidators/performance/nccl_benchmark_profile_test.govalidators/performance/nccl_roce_apply_test.govalidators/performance/nccl_test.govalidators/performance/testdata/gb200/gke/runtime-nvls.yaml
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In `@pkg/recipe/nccl_bandwidth_floor_test.go`:
- Around line 220-221: Update the wantPerf-disabled branch around
findPerformanceConstraint and performanceCheckPresent to assert that the
validation result contains zero performance checks and zero constraints,
regardless of leaf type or check name; preserve the existing enabled-branch
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Add the gb200-gke-cos-{training,training-kubeflow,training-slurm,
inference,inference-dynamo} recipe leaves, covering GB200 (A4X) on
GKE with COS. New gke-gb200-rdma component wires the NCCL gIB ARM64
plugin installer needed for GPUDirect-RDMA over RoCE, plus its
health check and BOM/tuning docs. The GKE multi-networking objects
(GKENetworkParamSet/Network: gvnic-1, rdma-0..rdma-3) are provisioned
with the cluster before the node pool exists, not by this component:
AICR treats them as a prerequisite and validates all 5 objects,
including deviceMode and parametersRef linkage, via health check.
GB200 on GKE is NVLS-only: MNNVL across the A4X nodes' IMEX domain is
the fabric that actually carries all-reduce traffic, so
nccl-all-reduce-bw-nvls (not the plain check) is wired into the
training leaves' performance phase, backed by a new runtime-nvls.yaml
TrainingRuntime template with IMEX ComputeDomain wiring. GPU NIC
discovery in the NCCL validator is skipped for this accelerator/service
pair since it uses the gke-gb200-rdma Network CRs instead of the TCPXO
gpu-nic-* fabric.
GB200 already has a Kubeflow leaf overlay on EKS and OKE; adds the
same kubeflow-trainer component here so GKE isn't the only GB200
platform missing one, giving robust-controller conformance a
supported operator to validate instead of always skipping.
Also adds a gb200-gke-cos-inference-dynamo leaf (grove + dynamo-platform,
DRA-gated to Kubernetes 1.34+), mirroring the GB200 EKS/OKE Dynamo
overlays' performance-gate thresholds until a GKE-specific baseline is
published. This turns the bare gb200-gke-cos-inference overlay from a
leaf into a base shared by both the plain and Dynamo inference leaves,
the same base/platform-variant pattern already used above for
training/training-kubeflow.
And a gb200-gke-cos-training-slurm leaf (Slinky operator + a
Slinky-managed Slurm cluster), mirroring gb200-eks-ubuntu-training-slurm's
GPU GRES, task isolation, and NVLS/IMEX ComputeDomain wiring for the same
4-GPU-per-node accelerator shape. Unlike the Kubeflow Trainer/JobSet
controllers above, Slinky's controller/restapi/nodeset Deployments already
go through AICR's ordinary nodeScheduling tolerationPaths, so this leaf
needs no Trainer-style toleration workaround.
Floor calibrated on a4x-highgpu-4g (4x GB200/node): 2-node/8-GPU
all_reduce_perf measured 281.936 GB/s avg bus bandwidth, and
gb200-gke-cos-inference-dynamo measured 103,971 tokens/sec throughput /
1388.55ms TTFT p99. Both runs, plus deployment and conformance for all
5 leaves (including the GB200-specific slinky-slurm-imex-channel health
check), were exercised on a live A4X cluster; gb200-gke-cos-training-slurm
has no NVLS performance phase by design, since the K8s-scheduled check
would bypass slurmd.
That cluster and its capacity leases have since been deleted, and
validators/performance/nccl_all_reduce_bw_constraint.go changed after
those runs, so no recipes/evidence/gb200-gke-* pointer is committed here;
live-hardware evidence is pending re-validation.
Signed-off-by: Mike Cook <micook@nvidia.com>
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Summary
Adds GB200 (A4X) recipes on GKE — training, training-kubeflow, training-slurm, inference, and inference-dynamo — with NVLS-based NCCL bandwidth validation.
Motivation / Context
GB200 on GKE (A4X node pools) wasn't a supported recipe target. This adds the
gke-gb200-rdmacomponent (gIB NCCL plugin installer for GPUDirect-RDMA over RoCE) plus its health check, five COS overlay leaves, and wires GB200-on-GKE into the NVLS NCCL all-reduce-bw validator path (GB200's NVLink/IMEX topology, not TCPXO).Fixes: N/A
Related: N/A
Type of Change
Component(s) Affected
pkg/recipe)validators/performance)docs/)recipes/(registry, overlays, checks, component manifests, tuning/coverage goldens)Implementation Notes
gb200-gke-cos-trainingis the base for-training-kubeflowand-training-slurm;gb200-gke-cos-inferenceis the base for-inference-dynamo. Both bases are themselves independently deployable leaves — the same base/platform-variant pattern already used for the EKS/OKE GB200 overlays.supportedNCCLCombinationsmaps GKE+GB200 tovariantNVLS, and GPU↔NIC (TCPXOgpu-nic-*) discovery is skipped specifically for the GKE+GB200 pair — it uses thegke-gb200-rdmaNetworkCRs instead. Other GKE accelerators (a100/b200/h100) are unaffected.runtime-nvls.yaml: new KubeflowTrainingRuntimetemplate for GKE GB200 with IMEXresourceClaimsand NVLS-specific env vars, required for the all-reduce job to exercise NVLS instead of falling back/erroring.Network/GKENetworkParamSetobjects (gvnic-1,rdma-0..rdma-3) must exist before the node pool does; thegke-gb200-rdmahealth check validatesdeviceMode/parametersReflinkage on all 5, it doesn't create them. Documented in the newdocs/integrator/gke-gb200-networking.md.main), Slinky's controller/restapi/nodesetDeployments already go through AICR's ordinarynodeScheduling.tolerationPaths.gb200-gke-cos-training-slurmsetsperformance: { checks: [], constraints: [] }— the K8s-scheduled NCCL check would bypassslurmdentirely on a Slinky-managed cluster. Slurm-specific health is covered by conformance checks instead (mirrorsgb200-eks-ubuntu-training-slurm).K8s.server.version >= 1.34(DRA GA), inherited throughgb200-gke-cos-training/gb200-gke-cos-inference.a4x-highgpu-4gnodes rejectstandard-rwo's defaultpd-balanceddisks; docs point operators at ahyperdisk-balanced-backed StorageClass instead.docs/integrator/gke-gpu-setup.mdanddocs/user/validation.mdcross-reference this.catalog_parity_golden.yaml,coverage_golden.yaml,stock_render_golden.yaml) and generated docs (container-images.md,recipe-health.md) regenerated to reflect the new GB200/GKE coverage surface.Testing
make qualifypasses in full (test-coverage, lint, tuning-check, e2e, scan, license-check, api-diff, openapi-diff).Live-hardware evidence for these 5 leaves is pending re-validation: the qualification runs performed earlier against a real A4X cluster were invalidated by subsequent validator/component changes, and that cluster plus its capacity leases have since been torn down. No
recipes/evidence/gb200-gke-*pointers are committed yet — the recipe-evidence-check bot will (correctly, per ADR-007) flag all 5 leaves as warning-only "no evidence yet." Evidence will be added in a follow-up once fresh hardware access is available.Risk Assessment
accelerator != GB200guard) that doesn't change behavior for existing GKE accelerators or other services.Rollout notes: N/A — new recipe leaves, no migration required.
Checklist
make testwith-race)make lint)git commit -S)