KubeflowExecutor

Run distributed training jobs on any Kubernetes cluster via the Kubeflow Training Operator v2. Submits TrainJob CRDs (trainer.kubeflow.org/v1alpha1) directly via the Kubernetes API — no kubectl required.

Kubernetes configuration is loaded automatically: local kubeconfig is tried first, falling back to in-cluster config when running inside a pod.

Prerequisites

• A Kubernetes cluster with the Kubeflow Training Operator v2 installed
• A ClusterTrainingRuntime named runtime_ref in the target namespace; "torch-distributed" is the conventional name for PyTorch distributed workloads
• kubectl configured with access to your cluster (kubectl cluster-info should succeed), or in-cluster service account credentials when running inside a pod

Executor configuration

import nemo_run as run
from nemo_run.core.execution.kubeflow import KubeflowExecutor

executor = KubeflowExecutor(
    launcher=run.Torchrun(),
    runtime_ref="torch-distributed",   # ClusterTrainingRuntime in your cluster
    namespace="my-namespace",
    image="nvcr.io/nvidia/nemo:25.04",
    num_nodes=4,
    gpus_per_node=8,
    cpu_requests="16",
    memory_requests="64Gi",
    image_pull_secrets=["ngc-registry-secret"],
    # Simple key=value env vars
    env_vars={
        "NCCL_DEBUG": "INFO",
        "PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True",
    },
    # Full env var dicts — use for secretKeyRef, fieldRef, etc.
    env_list=[
        {
            "name": "WANDB_API_KEY",
            "valueFrom": {"secretKeyRef": {"name": "my-secrets", "key": "WANDB_API_KEY"}},
        },
    ],
    labels={"app": "my-training-job"},
    tolerations=[
        {"effect": "NoSchedule", "key": "nvidia.com/gpu", "operator": "Exists"},
    ],
    volumes=[
        {"name": "dshm", "emptyDir": {"medium": "Memory"}},
        {"name": "model-cache", "persistentVolumeClaim": {"claimName": "model-cache"}},
    ],
    volume_mounts=[
        {"name": "dshm", "mountPath": "/dev/shm"},
        {"name": "model-cache", "mountPath": "/nemo-workspace"},
    ],
    # Sync the generated launch script to the pod before launch,
    # and pull results back after the job completes.
    workdir_pvc="model-cache",
    workdir_pvc_path="/nemo-workspace",
)

cancel(wait=True) polls until both the CR and all associated pods are fully terminated before returning.

Advanced options

Parameter	Purpose
nprocs_per_node	Override processes per node; defaults to gpus_per_node when unset
extra_resource_requests / extra_resource_limits	Non-GPU extended resources, e.g. {"vpc.amazonaws.com/efa": "32"} for AWS EFA NICs
pod_spec_overrides	Merge arbitrary fields into podTemplateOverrides[].spec, e.g. {"nodeSelector": {...}}
container_kwargs	Extra container-level fields, e.g. {"securityContext": {"privileged": True}}
workdir_local_path	Local directory merged into the job dir before PVC sync — useful for hand-written scripts not managed by the packager
annotations	Kubernetes annotations added to the TrainJob CR
affinity	Pod scheduling affinity rules

Limitations

Attributes like resourceClaims are not supported natively and must be injected via Mutating Webhooks or pod_spec_overrides.

End-to-end example

A self-contained end-to-end example — including volume setup, secret injection, and workdir PVC sync — is available at examples/kubeflow/hello_kubeflow.py.