test(uat): re-enable GCP serve phase by pinning Frontend to GPU pool - #2233
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Included review availability: Your plan provides up to 12 included reviews per hour; 10 remain after this review. 📝 WalkthroughWalkthroughThe UAT serving path places the Frontend and decode Worker on GPU nodes. The Frontend tolerates the NVIDIA GPU taint and does not request GPU resources. New tests validate rendered placement and selector overrides. AWS and Azure keep the serve step disabled pending validation. GCP re-enables the serve step with a 40-minute timeout and reports its runtime outcome. The coverage matrix marks the inference journey as covered. Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: ⚪ Minimal · up to The PR re-enables the GCP serve phase and pins Frontend to the GPU pool; the remaining issue is limited to updating a stale comment, with no actionable merge-blocking risk. Suggested reviewers: 🚥 Pre-merge checks | ✅ 3 | ❌ 1❌ Failed checks (1 inconclusive)
✅ Passed checks (3 passed)
✨ Finishing Touches📝 Generate docstrings
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Inline comments:
In `@tests/uat/lib/phases.sh`:
- Around line 867-875: Add a regression test covering the Frontend pod
template’s default and overridden node-selector values, asserting the
nvidia.com/gpu NoSchedule toleration and that the Frontend pod does not request
nvidia.com/gpu resources. Reuse the existing UAT test conventions and validation
tooling in the surrounding tests.
- Around line 824-835: Guarantee that Frontend and VllmDecodeWorker are
scheduled on the same node before relying on the cached vllm-runtime image.
Update the pod templates or scheduling configuration around nodeSelector:
nodeGroup=gpu-worker with an explicit same-node affinity or equivalent
single-node guarantee, and add a regression test validating that placement; only
then retain the cache-reuse documentation.
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Coverage Report ✅
Coverage BadgeNo Go source files changed in this PR. |
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Inline comments:
In @.github/workflows/uat-gcp.yaml:
- Around line 697-715: Update the rollout comments at
.github/workflows/uat-gcp.yaml lines 697-715 to state that GCP’s inference phase
chain includes serve; remove the stale AWS/GCP lockstep claim at
.github/workflows/uat-aws.yaml lines 763-774 and the Azure/GCP lockstep claim at
.github/workflows/uat-azure.yaml lines 800-811. Make comments accurately reflect
that only GCP runs serve while AWS and Azure remain disabled.
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Heads-up that the direction of this PR changed materially, so the original premise is no longer what's being proposed. Still draft; not asking for eyes yet. Dropped the pre-pull DaemonSet I had added, and corrected the rationale: the "Frontend reuses the decode worker's cached image" story was wrong (the operator creates both components at once, so there is no pre-existing cache — kubelet just dedupes co-located pulls into one). What the selector actually buys is keeping the Frontend off the CodeRabbit was right that the pool is two nodes with no same-node constraint, and I verified the default scheduler really does split two pods carrying this selector even when both fit on one node. But a split means two pulls running in parallel on two high-bandwidth nodes, so readiness waits out roughly one pull either way — which is why the pre-pull went from "necessary" to "unnecessary complexity". Full reasoning on that thread. The behavioural surface is now 13 lines: 3 in Gate on this: a |
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Appended Regression test for the missing field. #1644 was an absent
Each one was verified to fail when the corresponding field is removed from On the hardware validation. My first run went green without testing anything: I dispatched it with Worth noting independently of this PR: a UAT run where every workload step skips still reports |
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Inline comments:
In `@tests/uat/serve_manifest_test.go`:
- Around line 128-140: Update the test named “worker still requests exactly one
GPU” to count worker containers declaring gpuResource, require exactly one such
container, and retain validation that its limit equals 1; replace the current
found-only assertion with an exact claim-count assertion.
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tests/uat/lib/phases.shtests/uat/serve_manifest_test.go
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🌿 Preview your docs: https://nvidia-preview-fix-1644-serve-frontend-gpu-pin.docs.buildwithfern.com/aicr |
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Appended
UAT validation for a served completion is parked for now (run 32244112044 failed on |
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🔬 Multi-Persona Review — updated for head 2d5b8f0
Method: 3 independent persona reviewers (Correctness · Operability/CI-DX · Domain & Architecture) → adversarial senior meta-reviewer that re-derived each finding from the resolved code. An earlier pass was drafted against d77bad9; the branch has since advanced by 4 commits, so this review is re-anchored to the current head and re-verified.
Tier legend: 🔴 Blocker · 🟠 Major · 🟡 Minor · 🔵 Nitpick
Overall assessment
Clean, well-reasoned, and now well-tested. The change pins both served-graph components to the GPU pool (nodeSelector + nvidia.com/gpu toleration) so the Frontend stops cold-pulling the ~12 GB vLLM runtime on a small CPU-pool node, re-enables the GCP serve step, and adds an off-cluster Go test that renders the manifest and asserts the scheduling contract field-by-field.
Verified locally against this head:
TestServeGraphGPUPlacement+TestServeGraphSelectorIsOverridablepass (go test ./tests/uat/), andgo vet ./tests/uat/...is clean. The test rendersserve_render_manifestoff-cluster and asserts every component selects the GPU pool, the Frontend toleration set is a superset of the worker's (incl. the barenvidia.com/gputaint for AKS), the Frontend claims no GPU device, the worker still requests exactly one, and the selector is env-overridable. This is exactly the "missing-field" class of regression that shellcheck and happy-path cluster runs can't catch.docs/user/coverage-matrix.mdregenerates byte-identically — I re-rantools/coverageand got zero diff, so thecuj2-inference-dynamo→coveredflip is the generator's own output (driven by the now-wired GCP serve step) and passes thecoverage-freshnessmerge gate, not a hand-edit that will drift.- The
nodeGroup: gpu-workerlabel is present on all three clouds' GPU pool, and the toleration list covers each cloud's actual GPU-pool taints.
The central concern from the earlier pass is fully resolved. That pass flagged that node-group pinning over a 2-node GPU pool doesn't guarantee same-node cache reuse, so the "reuses the cached copy" rationale overclaimed. The new phase_serve comment now states this outright — "This selects the POOL, not a node… they may be split… a split yields two pulls running in PARALLEL on two high-bandwidth nodes… Do not read the co-location as load-bearing; it is not, and there is no shared cache to inherit." That is an accurate mechanism, and it's the correct framing.
Confirmed non-issues (examined, not defects)
- Cache-reuse rationale — previously an overclaim, now corrected in the comment; the durable guarantee (a fast GPU-pool node pulls within budget even on a cache miss) is real. No regression to the original CPU-node wedge.
- 3-file workflow comment consistency is correct; no stale "once the pull is addressed" text remains; the restored
${{ steps.serve.outcome }}summary row andid: serveare consistent. - YAML indentation correct; the
nvidia.com/gpuflow-map key (/in a plain scalar) is valid even under selector override. nvidia.com/gputoleration isNoSchedule-only, but it mirrors the worker's (now test-enforced as a superset) — symmetric, pre-existing, not a defect.- Sourcing
phases.shin the test is side-effect-safe: the phase dispatch is inside a function, sosource … ; serve_render_manifestdoesn't run any phase.
Recommendation
🟢 Approve — only two 🔵 nitpicks remain (inline). Nothing blocks; the substantive prior finding was addressed directly.
| 🔴 Blocker | 🟠 Major | 🟡 Minor | 🔵 Nitpick | Recommendation |
|---|---|---|---|---|
| 0 | 0 | 0 | 2 | 🟢 Approve |
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🔬 Multi-Persona Re-Review — head ea03627
Mode: Re-review (delta). Baseline = my prior review (COMMENTED @ 2d5b8f09, 2 🔵 nitpicks). 9 commits landed since; the net-new surface is the GKE driver-lib worker wrapper (6b236b02), the AWS/Azure comment revert (686207d1), and the proving-run comment update (ea036273). Two delta-scoped persona reviewers (Correctness/shell-runtime · CI-DX/Domain) → adversarial re-derivation from the resolved code; all claims reproduced locally.
Tier legend: 🔴 Blocker · 🟠 Major · 🟡 Minor · 🔵 Nitpick
Prior-feedback status
| # | Prior nitpick | Disposition | Resolved by |
|---|---|---|---|
| P1 | 🔵 GCP serve re-enabled before any served completion demonstrated | ✔️ Addressed | ea036273 records proving run 32837547329 (prep→serve+verify) in the serve-step comment + PR body. A served completion now exists. |
| P2 | 🔵 "Mirrors the demo" comment omits the Frontend cpu→gpu divergence | ✔️ Addressed | 6b236b02 rewrote the phase_serve comment to call out "Two intentional divergences from the demo" (Frontend placement + worker command). Verified against demos/workloads/inference/vllm-agg.yaml (Frontend cpu-worker, worker gpu-worker). |
Both prior nitpicks resolved.
Overall assessment
The net-new work is solid. The driver-lib wrapper is correct across every axis checked: bash -c '… exec python3 -m dynamo.vllm "$@"' dynamo.vllm --model … gives $0=dynamo.vllm and $@=args so --model stays args[0]; the ${LD_LIBRARY_PATH:+…:} append preserves the image's existing entries with no leading empty :; exec preserves PID-1/SIGTERM semantics; the ENTRYPOINT was already bypassed by the prior command override, so nothing is lost; and the rendered command matches the validators/performance Dynamo template it cites byte-for-byte. The --previous add to serve_debug fails open correctly. coverage-matrix.md's flip to covered is generator-driven — go run ./tools/coverage regenerates it byte-identically, and the coverage-freshness gate would catch a hand-edit. The AWS/Azure revert leaves both workflows byte-identical to main. All three TestServeGraph* tests pass on head.
Two 🔵 nitpicks remain, both on comment/style lines — neither blocking. One is inline; the other is out of the diff range and noted here:
- 🔵 Stale train-step comment (
.github/workflows/uat-gcp.yaml:685-686, unchanged context so not diff-anchorable): theUAT - traincomment still reads "for inference the serve step is currently disabled (see below)…" — this PR enables that very step, so the sibling comment is now inverted. Docs-only; update it to mirror the accurate serve-step comment at 697–703.
Confirmed non-issues (examined, not defects)
- Serve summary row
${{ steps.serve.outcome }}— rendersskippedon training runs (id: servenow exists → never an empty| Serve | |), symmetric with how| Train |rendersskippedon inference runs; summary step isif: always(). - coverage-matrix
coveredflip — generator output, byte-identical on regen; a single wired GCP lane satisfies the generator'srunsJourneycontract (same basis ascuj1). Not an overclaim, won't drift. - Driver-lib wrapper —
/bin/bashempirically present (same image+tag inference-perf wraps and served minutes earlier on the same cluster). - AWS/Azure revert —
git diff origin/mainempty for both; genuinely out of scope.
Recommendation
🟢 Approve — the substantive net-new change is correct and well-tested, both prior nitpicks are addressed, and only two cosmetic comment/style nits remain.
| 🔴 Blocker | 🟠 Major | 🟡 Minor | 🔵 Nitpick | Recommendation |
|---|---|---|---|---|
| 0 | 0 | 0 | 2 | 🟢 Approve |
| image: ${SERVE_RUNTIME_IMAGE} | ||
| workingDir: /workspace/examples/backends/vllm | ||
| command: ["python3", "-m", "dynamo.vllm"] | ||
| # A bare `python3 -m dynamo.vllm` here crash-looped on GKE before |
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🔵 Nitpick — New comment backtick trips shellcheck SC2006; bare shellcheck -x exits 1
The new comment on this line wraps python3 -m dynamo.vllm in backticks, which shellcheck reads as legacy backtick command-substitution and flags SC2006 (style). Bare shellcheck -x tests/uat/lib/phases.sh — the exact command in the PR's Testing section — then exits 1; at --severity=warning it exits 0. No CI shellcheck gate covers this file, so it cannot fail the merge gate.
Blast radius: Contradicts the PR's stated "shellcheck ... green" claim; zero runtime effect.
Fix: Single-quote it — A bare 'python3 -m dynamo.vllm' — or drop the backticks.
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Force-pushed a gate-required rebase onto
CI will re-run. @njhensley please re-approve if the rebase looks clean — squash-merge was blocked by the up-to-date-branch rule. |
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The DynamoGraphDeployment's Frontend and VllmDecodeWorker share the ~12GB nvcr.io/nvidia/ai-dynamo/vllm-runtime image, but only the worker carried a nodeSelector. The Frontend therefore scheduled onto a fresh non-GPU node with no cached copy and wedged in ContainerCreating pulling the image from scratch, blowing past SERVE_READY_TIMEOUT_SECONDS (30m) and failing the phase: DynamoGraphDeployment vllm-agg did not become ready within 1800s status: Resources not ready: podclique/vllm-agg-0-frontend: desired=1, ready=0 Pin the Frontend to the same GPU node via the existing SERVE_GPU_NODE_SELECTOR_* knobs so it reuses the node-cached image, and add the nvidia.com/gpu toleration it now needs (the AKS GPU pool carries only that taint). Co-location allocates no device: the Frontend declares no nvidia.com/gpu limit. This matches how the inference-perf validator places every component on the GPU cohort. phase_serve is shared by all three cloud runners, so this fixes the root cause on AWS, GCP, and Azure. The workflow steps stay commented out pending an inference UAT run that demonstrates a served completion on the pinned layout; their stale "once the pull is addressed" comments are updated to say what remains. Refs: #1644 Signed-off-by: Rohit Rajani <rorajani@nvidia.com>
The committed rationale was wrong: it claimed the Frontend "reuses the decode worker's cached vllm-runtime image". The operator creates both components at once, so there is no pre-existing cache to inherit — kubelet merely dedupes co-located pulls into one. What the selector actually buys is getting the Frontend off the CPU pool. Unselected it landed on an e2-standard-4 / n2-standard-8, where a ~12GB image is slow both to download and to decompress on 4-8 vCPUs, and it wedged in ContainerCreating past SERVE_READY_TIMEOUT_SECONDS. The GPU pool's a3-megagpu-8g class pulls the same image in a fraction of the budget. Also record why co-location is not load-bearing, since the selector picks the POOL and the pool has two nodes: a split places the two pulls on two high-bandwidth nodes in PARALLEL, so readiness waits out roughly one pull either way. Verified on kind that the default scheduler does split two pods carrying this selector even when both fit on one node, so the comment documents observed behaviour rather than an assumption. Re-enable the serve step on GCP only, since #1644 tracks the GKE x inference cell, and restore its summary row. aws/azure keep their steps commented until this lane demonstrates a served completion; their stale rationale is corrected in place. Refs: #1644 Signed-off-by: Rohit Rajani <rorajani@nvidia.com>
The #1644 regression was a missing field -- the Frontend carried no nodeSelector -- which no shell lint and no on-cluster happy path can catch. Extract the serve manifests into serve_render_manifest, a pure function that writes to STDOUT and touches no cluster, so the placement contract is assertable from a unit test; phase_serve now pipes it into kubectl apply. The renderer reproduces the previous heredoc byte for byte. The test asserts the invariants a future edit would break rather than today's literal bytes: every component (not just the Frontend) selects the GPU pool, the Frontend's tolerations are a superset of the worker's so it can land anywhere the worker can, it tolerates the bare nvidia.com/gpu taint the AKS pool relies on, it claims no GPU device, and the selector stays env-driven. Each assertion was verified to fail when the corresponding field is removed. Signed-off-by: Rohit Rajani <rorajani@nvidia.com>
Regenerated by `make coverage-docs`. The generator derives each journey's status from whether its per-cloud UAT runner step is enabled, so re-enabling the GCP serve step flips cuj2-inference-dynamo from stubbed to covered — the outcome #1644 exists to produce, and what ROADMAP §1 requires for an in-scope cell. Keeps the gated doc in step with the workflow change in the same PR. Signed-off-by: Rohit Rajani <rorajani@nvidia.com>
Count nvidia.com/gpu limits across worker containers so two containers each claiming 1 GPU cannot pass as "exactly one". Drop the AWS/Azure "lockstep with uat-gcp" wording now that only GCP runs the serve step. Signed-off-by: Rohit Rajani <rorajani@nvidia.com>
The serve graph matches the demo topology but not its cpu-worker Frontend placement. Spell that out, and note that the first GCP inference nightly is the proving run for #1644. Signed-off-by: Rohit Rajani <rorajani@nvidia.com>
The #1644 proving run (32732018329) reached phase_serve for the first time and the VllmDecodeWorker crash-looped before binding its health port. GKE mounts the node driver at /usr/local/nvidia without putting it on LD_LIBRARY_PATH, so vLLM cannot dlopen libcuda.so.1 — the same hole inference-perf already works around, which is why it served the same runtime on the same cluster minutes earlier. Wrap the worker command with the append used by the validators/performance Dynamo templates, and capture --previous container logs in serve_debug so a crash loop is diagnosable instead of dumping the empty stdout of its replacement container. Signed-off-by: Rohit Rajani <rorajani@nvidia.com>
Those files do not change serve behavior. #1644 is the GKE inference cell; AWS/Azure steps stay disabled as on main. Signed-off-by: Rohit Rajani <rorajani@nvidia.com>
Nathan's nitpick was to stay draft until a served completion. Run 32837547329 is that completion; the comment no longer calls the next nightly the proving run. Signed-off-by: Rohit Rajani <rorajani@nvidia.com>
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Summary
Re-enable the GCP
UAT - servestep, pin both Dynamo graph components to the GPU pool so the Frontend's ~12GB image pull finishes inside the readiness budget, and wrap the decode worker so GKE can findlibcuda.so.1.Motivation / Context
Frontendcarried nonodeSelector, so it scheduled onto a CPU-pool node and wedged inContainerCreating:The instance classes explain it:
On 4-8 vCPUs with modest egress, a ~12GB image is slow to download and slow to decompress (layer extraction is CPU-bound). The GPU pool's
a3-megagpu-8gclass pulls the same image in a fraction of the budget. That is why the serve step has been off, leaving GKE x inference without sustained validation (ROADMAP §1 requires both training and inference per in-scope cell).A first proving run then crash-looped the decode worker: GKE mounts the host driver at
/usr/local/nvidiawithout putting it onLD_LIBRARY_PATH. The same appendinference-perfalready uses is now on the worker command.Fixes: #1644
Related: #1276, #1327
Type of Change
Component(s) Affected
docs/)tests/uat/lib/phases.sh) + GCP UAT workflowImplementation Notes
phases.sh:nodeSelectoron Frontend and Worker, plus thenvidia.com/gputoleration the AKS-shaped GPU pool needs (without it Frontend isPendingthere). Worker command wrapspython3 -m dynamo.vllmwith anLD_LIBRARY_PATHappend for/usr/local/nvidia/lib64(GKE hole; no-op when that path is absent).uat-gcp.yaml: uncomment the serve step and restore the summary row to${{ steps.serve.outcome }}. GCP only — Re-enable GCP UAT serve phase: fix ~12GB vllm-runtime Frontend image pull #1644 is the GKE x inference cell. AWS/Azure serve steps stay disabled onmain.docs/user/coverage-matrix.md:cuj2-inference-dynamomarked covered now that the served step runs on GCP.Sharing the pool allocates no GPU.
Frontenddeclares nonvidia.com/gpulimit, so the device plugin never assigns it a device. This matches how the inference-perf validator places every component on the GPU cohort.Co-location is deliberately not guaranteed. The selector picks the pool, and the pool is two nodes, so the components may be split.
Testing
go test ./tests/uat/... shellcheck -x tests/uat/lib/phases.sh yamllint .github/workflows/uat-gcp.yamlCI on this SHA is green. Placement and worker-command assertions live in
tests/uat/serve_manifest_test.go.Proving run: UAT gcp-h100 inference (
686207d1) — prep, install, validate, serve, verify, teardown all succeeded.Risk Assessment
SERVE_GPU_NODE_SELECTOR_*knobs; reverting the GCP serve step restores today's skip. The library-path wrap is the same shape as the inference-perf Dynamo templates.Rollout notes: AWS/Azure serve remain commented; re-enable per-cloud after this lands if those lanes should run the served CUJ.
Checklist
make testwith-race) —go test ./tests/uat/...tests / Lintgreendocs/user/coverage-matrix.mdgit commit -S)