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Check GPU readiness at the same layers where the scheduled agent will run: verify the device on the host, confirm it is exposed inside the container, and run a small application-level smoke test before expensive work. These checks reduce avoidable startup failures; they do not guarantee that a full run will succeed.
What a GPU preflight can—and cannot—tell you
A readiness check should answer progressively more specific questions: is an NVIDIA GPU visible, can the scheduled container access it, can the agent’s own software perform a basic GPU operation, and does the hardware or communication path pass the diagnostics appropriate to the job?
Those questions are not interchangeable. NVIDIA’s nvidia-smi reference describes a management utility for querying GPU state. A successful query confirms visibility to that utility, not that the agent’s framework can execute its workload. Likewise, a successful smoke test or diagnostic is evidence about the tested operation and conditions—not a guarantee of full-run success. There is no standard cron-agent preflight specification or cron-specific integration established by the cited tools.
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1. Verify the intended device on the host
Before the scheduled command starts, query the host GPU with nvidia-smi. Record the output or at least the device identity and relevant state in the job logs. This makes it easier to distinguish a missing device from a visible device that reports a problem. Treat this as a quick inventory and visibility check, not as a workload test.
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2. Confirm GPU access inside the scheduled container
Host visibility does not prove that a container has GPU access. Docker’s GPU access guide documents installing the NVIDIA driver and NVIDIA Container Toolkit, requesting a GPU with --gpus, and checking visibility by running nvidia-smi inside the container. Apply the equivalent GPU-access configuration in the system that launches the scheduled container, then perform the query in that container’s environment.
3. Run a minimal application-level smoke test
Use a small operation that exercises the same framework, libraries, device-selection settings, and container environment as the agent. For example, have the application allocate a small tensor on the intended GPU and perform a basic operation, then exit successfully. This is an engineering recommendation, not a universal vendor-supplied test: tailor it to the agent, and ensure it actually exercises the runtime the job will use.
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4. Add hardware or interconnect diagnostics where appropriate
For Kubernetes workloads that request GPUs, NVIDIA NVSentinel can inject diagnostic init containers. NVIDIA describes preflight as a mutating admission webhook that injects DCGM diagnostic containers, with optional NCCL loopback or all-reduce checks, into GPU-requesting pods in namespaces opted in with labels. See the NVSentinel 1.25.0 preflight configuration.
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This is a Kubernetes mechanism, not a cron integration or a universal agent feature. The chart is disabled by default, DCGM must be reachable, and multi-node checks require gang coordination and scheduler discovery configuration. Configure the prerequisites and opt-in behavior for the actual cluster before depending on the gate.
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Choose a check that matches the risk and startup budget
| Check | What it establishes | Useful when | Important limit |
|---|---|---|---|
nvidia-smi query |
NVIDIA management tooling can see and report the GPU’s state. | You need a fast host- or container-level visibility check. | It does not prove that the agent’s framework or workload will run correctly. See NVIDIA’s reference and Docker’s container guide. |
| Minimal application smoke test | The agent’s selected runtime and framework can complete a small GPU operation. | You need to catch application initialization or device-selection failures before expensive work. | It must be designed for the actual application; no universal test is specified by the cited sources. |
| NVSentinel preflight | Configured DCGM diagnostics and, optionally, NCCL communication checks run before opted-in Kubernetes GPU pods start. | A Kubernetes environment needs a configured pod admission gate for GPU workloads. | It requires Kubernetes integration and dependencies, and diagnostic latency varies. See configuration and the 1.22.0 overview. |
| NVIDIA NGC Pre-Flight Check container | The catalog describes a check of GPU and InfiniBand container-runtime setup. | An HPC or deep-learning host needs a packaged setup check. | The catalog result lists tag 20.11; verify current availability and compatibility before relying on it. See the NGC catalog entry. |
These options cover different layers, not equivalent benchmarks. A management query is lighter than hardware stress or communication diagnostics. Choose based on what could fail, how costly a false start would be, and how much startup delay the scheduled task can tolerate.
Budget diagnostic time before scheduling the job
NVIDIA’s NVSentinel 1.22.0 overview 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. If the scheduled task has a tight startup budget, choose an appropriate diagnostic level and account for its runtime in the schedule rather than treating it as instantaneous.
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Make failure visible and stop before expensive work
- Run the checks before downloading large inputs, allocating costly resources, or beginning the main computation.
- On failure, exit nonzero so the scheduler can observe that the run did not start successfully. NVSentinel reports a failed preflight through a nonzero init-container exit.
- Preserve enough output to identify whether the failure was device visibility, container runtime setup, GPU diagnostics, or application initialization.
- Let the normal scheduler alerting or retry policy handle the failed run. Avoid automatic production GPU resets as a routine recovery step: NVIDIA’s nvidia-smi documentation cautions that reset is not guaranteed to work and is not recommended for production environments at this time.
A layered gate helps keep a job from proceeding when a tested prerequisite is missing or unhealthy. It cannot rule out later failures such as input errors, memory pressure, software defects, or a problem that develops after the check.
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