Getting Started

WorkloadRun Quick Start

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WorkloadRun is a simplified API for running a single workload against a set of nodes. It is useful for one-off benchmarks, smoke tests, and validating specific node groups without running the full certification suite.

When to use WorkloadRun vs Certification: Use WorkloadRun when you want to run a single workload — for example, a quick NCCL bandwidth check or a training smoke test — against a specific set of nodes. Use Certification when you need a full burn-in suite across all categories and node groups, with per-node pass/fail results and a structured report.

Before you begin

Install nvcrectl and set up the cluster before continuing.

Define a WorkloadRun

1apiVersion: cre.nvidia.com/v1alpha1
2kind: WorkloadRun
3metadata:
4  name: nccl-all-reduce
5spec:
6  image: nvcr.io/nvidia/pytorch:26.01-py3
7  framework:
8    mpi:
9      binary: /usr/local/bin/all_reduce_perf_mpi
10      args: ["-b", "8", "-e", "32G", "-f", "2", "-n", "100"]
11      mpirunPath: /usr/local/mpi/bin/mpirun
12  numNodes: 4
13  bandwidthMeasurement:
14    logProfileRef: nccl-bandwidth
15    testType: all_reduce

Run it

$nvcrectl workloadrun run \
>  --workload-registry nvcr.io \
>  --workload-registry-username '$oauthtoken' \
>  --workload-registry-password "$NGC_API_KEY" \
>  --wait \
>  nccl-all-reduce.yaml

nvcrectl auto-detects the platform and GPU architecture from the cluster’s node labels, applies the appropriate overrides, and streams log output until the workload completes.

View results

$nvcrectl workloadrun report nccl-all-reduce

For workloads with bandwidthMeasurement configured, the report includes per-bus bandwidth results parsed from the NCCL output.

Next steps