docs: add KubeflowExecutor section to execution guide

Documents PyTorchJob and TrainJob usage, configuration fields,
workdir sync, and packager support in docs/guides/execution.md.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Signed-off-by: oliver könig <okoenig@nvidia.com>

Author: ko3n1g

docs/guides/execution.md

@@ -53,6 +53,7 @@ The packager support matrix is described below:
 | SkypilotExecutor | run.Packager, run.GitArchivePackager, run.PatternPackager, run.HybridPackager |
 | DGXCloudExecutor | run.Packager, run.GitArchivePackager, run.PatternPackager, run.HybridPackager |
 | LeptonExecutor   | run.Packager, run.GitArchivePackager, run.PatternPackager, run.HybridPackager |
+| KubeflowExecutor | run.Packager |
 
 `run.Packager` is a passthrough base packager.
 
@@ -293,6 +294,49 @@ def your_dgx_executor(nodes: int, gpus_per_node: int, container_image: str):
 
 For a complete end-to-end example using DGX Cloud with NeMo, refer to the [NVIDIA DGX Cloud NeMo End-to-End Workflow Example](https://docs.nvidia.com/dgx-cloud/run-ai/latest/nemo-e2e-example.html).
 
+#### KubeflowExecutor
+
+The `KubeflowExecutor` integrates with the [Kubeflow Training Operator](https://github.com/kubeflow/training-operator) to run distributed training jobs on any Kubernetes cluster. It submits CRDs directly via the Kubernetes API — no `kubectl` required.
+
+Two job kinds are supported via the `job_kind` parameter:
+
+- **`"PyTorchJob"`** (default) — Training Operator v1 (`kubeflow.org/v1`)
+- **`"TrainJob"`** — Training Operator v2 (`trainer.kubeflow.org/v1alpha1`)
+
+Kubernetes configuration is loaded automatically: local kubeconfig is tried first, falling back to in-cluster config when running inside a pod.
+
+Here's an example configuration:
+
+```python
+# PyTorchJob (default)
+executor = run.KubeflowExecutor(
+    namespace="runai-nemo-ci",
+    image="nvcr.io/nvidian/nemo:nightly",
+    num_nodes=3,          # total pods: 1 Master + (num_nodes-1) Workers
+    gpus_per_node=8,      # also sets nproc_per_node unless overridden explicitly
+    cpu_requests="16",
+    memory_requests="64Gi",
+    volumes=[
+        {"name": "model-cache", "persistentVolumeClaim": {"claimName": "nemo-ci-datasets-project-nkf5l"}}
+    ],
+    volume_mounts=[{"name": "model-cache", "mountPath": "/nemo-workspace"}],
+    labels={"app": "nemo-ci-training"},
+    env_vars={"NCCL_DEBUG": "INFO"},
+)
+
+# TrainJob (Training Operator v2)
+executor = run.KubeflowExecutor(
+    job_kind="TrainJob",
+    runtime_ref="torch-distributed",  # name of the ClusterTrainingRuntime
+    namespace="runai-nemo-ci",
+    image="nvcr.io/nvidian/nemo:nightly",
+    num_nodes=3,
+    gpus_per_node=8,
+)
+```
+
+`cancel(wait=True)` polls until both the CR and all associated pods are fully terminated before returning.
+
 #### LeptonExecutor
 
 The `LeptonExecutor` integrates with an NVIDIA DGX Cloud Lepton cluster's Python SDK to launch distributed jobs. It uses API calls behind the Lepton SDK to authenticate, identify the target node group and resource shapes, and submit the job specification which will be launched as a batch job on the cluster.