Before a scheduled GPU agent begins costly work, check that the intended device is visible where the agent runs, then verify the application can use it. A successful preflight reduces avoidable startup failures; it does not guarantee the full job will succeed.
What a GPU preflight can—and cannot—tell you
There is no standard cron-agent GPU preflight specification in the cited documentation. Treat readiness as a sequence of checks at different layers, not as one universal test. A host query can show that NVIDIA management software sees a device; a container check can establish that the scheduled environment sees it; an application smoke test can check that the agent’s own runtime can perform a small operation.
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These checks answer different questions. A device may be visible to the host but unavailable inside the container, or visible to the container while the agent’s framework fails to initialize. Even a successful application smoke test cannot predict every failure during a full workload.
Build the checks in the order the job uses them
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Check the host before the scheduled run
Run
nvidia-smion the host to see whether NVIDIA management tooling can query the intended GPU and report its state. Record the device identity and relevant output in the job’s logs. This is a visibility and inventory check, not a complete workload test. NVIDIA documentsnvidia-smias a management utility and describes its query capabilities in the nvidia-smi reference.#1 Best Overall
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Verify GPU access at the container boundary
Confirm that the scheduler starts the container with GPU access configured. Docker’s documented setup requires an NVIDIA driver and NVIDIA Container Toolkit; the documented launch pattern uses
--gpus. Then runnvidia-smiinside the container. A successful host query alone does not prove that the scheduled container can see the device. See Docker’s GPU support guide. -
Test the agent’s actual runtime
Run a small GPU operation using the same framework, libraries, device-selection settings, and runtime that the agent will use. This is an engineering check to design for your application, not a universal vendor-provided test. Keep it small enough to fit the schedule’s startup budget, but representative enough to expose initialization or device-selection problems.
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Add a health or communication diagnostic when needed
For Kubernetes workloads, NVIDIA NVSentinel can inject DCGM diagnostic init containers, with optional NCCL loopback or all-reduce checks, into opted-in pods that request GPUs. DCGM diagnostics check GPU health; optional NCCL tests can cover relevant communication paths. These checks go beyond a management query, but they are not a cron integration or a feature automatically applied to every agent.
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Choose a check that matches the failure you need to catch
| Option | What it checks | Best fit | Important limitation |
|---|---|---|---|
nvidia-smi query |
Whether NVIDIA management tooling can see and query a GPU and report state | Fast host or container visibility check | Does not prove the agent’s framework or workload can run correctly. |
| Minimal application smoke test | Whether the agent’s runtime and framework can perform a small GPU operation | Per-agent readiness check before expensive work | Must be designed for the actual application; there is no universal vendor-supplied test described here. |
| NVIDIA NVSentinel preflight | DCGM GPU diagnostics and optional NCCL communication checks | Configured Kubernetes environments that need a gate for opted-in GPU-requesting pods | Requires Kubernetes integration and dependencies; diagnostic time varies by level. |
| NVIDIA NGC Pre-Flight Check container | GPU and InfiniBand container-runtime setup | HPC or deep-learning hosts needing a packaged setup check | The NGC catalog entry lists tag 20.11; check current availability and compatibility before relying on it. |
These options are not interchangeable benchmarks. A management query is materially lighter than a health or communication diagnostic. Choose based on the layer that has failed before, the time available at startup, how the scheduler can enforce a failure, and the infrastructure you already operate.
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What Kubernetes NVSentinel preflight requires
NVSentinel’s preflight is an opt-in Kubernetes mechanism. NVIDIA describes it as a mutating admission webhook that injects GPU diagnostic init containers into GPU-requesting pods in namespaces opted in through labels. It is not a general-purpose cron hook. See NVIDIA’s NVSentinel Preflight configuration documentation.
- The preflight chart is disabled by default; configure and enable it for the intended namespaces.
- DCGM must be reachable for diagnostics to run.
- Multi-node checks require gang coordination and scheduler discovery configuration.
- The check runs in init containers, so diagnostic work can delay application startup.
NVIDIA’s version 1.22.0 documentation gives a DCGM diagnostic duration of 30 seconds to 15 minutes, depending on diagnostic level. That is a documented range for those diagnostics, not a benchmark for all GPU checks. Set the level with the scheduled workload’s startup budget in mind.
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Make failure visible and stop expensive work
A preflight is useful only if a failed check prevents the costly command from starting and leaves an outcome the scheduler can act on. Return a nonzero exit status on failure, retain diagnostic output, and make logs distinguish among device visibility, container runtime setup, hardware diagnostics, and application initialization. Let the scheduler’s established alerting or retry policy handle the failed run.
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Check the scheduled environment, not just the machine
The practical sequence is host visibility, container visibility, then an application-level smoke test; add deeper health or interconnect diagnostics where the deployment warrants their cost. Make each failure block the expensive work and surface clearly to the scheduler. Readiness is evidence that specific checks passed—not a promise that the complete agent run will succeed.
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