Why do PyTorch DataLoaders crash with Bus error in Kubernetes GPU pods?

PyTorch multi-worker DataLoaders utilize shared memory (/dev/shm) for inter-process communication. Default Kubernetes pods allocate a minimal 64MB shared memory volume, which instantly overflows during high-throughput tensor loading, triggering SIGBUS crashes.

What is the difference between Kubeflow PyTorchJob and native Kubernetes Indexed Job?

Kubeflow PyTorchJob provides specialized orchestrators for master and worker node coordination with automatic environment injection for distributed rendezvous. Native Kubernetes Indexed Jobs rely on completionMode: Indexed with manual rank indexing.

Why is the IPC_LOCK security capability required for multi-node RDMA/RoCE training?

NCCL utilizes GPUDirect RDMA to bypass CPU memory during inter-node communication. This requires locking virtual memory pages in physical RAM using mlock, which fails with permission errors unless the IPC_LOCK security capability is explicitly added.

How do Kubernetes Guaranteed QoS class requirements affect GPU scheduling?

Kubernetes assigns the Guaranteed QoS class only when container resource limits and requests are identical for CPU, memory, and accelerator devices. This prevents unexpected node eviction under high memory pressure.

When should you use Volcano gang scheduling over standard Kubernetes scheduler?

Volcano gang scheduling ensures all pods in a multi-node distributed training job are scheduled simultaneously. Without gang scheduling, partial cluster allocations can leave jobs hanging indefinitely waiting for missing worker pods.

Kubernetes Multi-Node GPU Job Generator and Linter

Generate production-ready Kubeflow PyTorchJob, Volcano, and Indexed Job manifests. Prevent cluster deadlocks by linting shared memory, IPC_LOCK, and GPU QoS configurations.

Cluster and Job Parameters

Cloud Provider Preset

Job Name

Namespace

Job Framework

GPU Model

Node Count (4)

GPUs Per Node

Container Image

Command Arguments (comma separated)

Shared Memory (/dev/shm) Size64 GB

hostIPChostNetworkRDMA / IPC_LOCK

Total GPUs

2560 GB

Total VRAM

Guaranteed QoS

CPU / Memory

kubernetes-gpu-job.yaml

apiVersion: "kubeflow.org/v1"
kind: "PyTorchJob"
metadata:
  name: llama-training
  namespace: ai-training
  labels:
    app.kubernetes.io/name: llama-training
    app.kubernetes.io/component: gpu-job
spec:
  pytorchReplicaSpecs:
    Master:
      replicas: 1
      template:
    spec:
      hostIPC: true
      
      containers:
      - name: gpu-container
        image: pytorch/pytorch:2.4.0-cuda12.4-cudnn9-devel
        command:
        - "torchrun"
        - "--nproc_per_node=8"
        - "train.py"
        env:
        - name: PET_MASTER_PORT
          value: "29500"
        - name: PET_NUM_NODES
          value: "4"
        - name: NCCL_DEBUG
          value: "INFO"
        - name: NCCL_IB_DISABLE
          value: "0"
        resources:
          limits:
            nvidia.com/gpu: "8"
            memory: "256Gi"
            cpu: "32"
          requests:
            nvidia.com/gpu: "8"
            memory: "256Gi"
            cpu: "32"
        volumeMounts:
        - mountPath: /dev/shm
          name: dshm
        securityContext:
          capabilities:
            add:
            - IPC_LOCK
      volumes:
      - name: dshm
        emptyDir:
          medium: Memory
          sizeLimit: 64Gi
      tolerations:
      - key: nvidia.com/gpu
        operator: Exists
        effect: NoSchedule
    Worker:
      replicas: 3
      template:
    spec:
      hostIPC: true
      
      containers:
      - name: gpu-container
        image: pytorch/pytorch:2.4.0-cuda12.4-cudnn9-devel
        command:
        - "torchrun"
        - "--nproc_per_node=8"
        - "train.py"
        env:
        - name: PET_MASTER_PORT
          value: "29500"
        - name: PET_NUM_NODES
          value: "4"
        - name: NCCL_DEBUG
          value: "INFO"
        - name: NCCL_IB_DISABLE
          value: "0"
        resources:
          limits:
            nvidia.com/gpu: "8"
            memory: "256Gi"
            cpu: "32"
          requests:
            nvidia.com/gpu: "8"
            memory: "256Gi"
            cpu: "32"
        volumeMounts:
        - mountPath: /dev/shm
          name: dshm
        securityContext:
          capabilities:
            add:
            - IPC_LOCK
      volumes:
      - name: dshm
        emptyDir:
          medium: Memory
          sizeLimit: 64Gi
      tolerations:
      - key: nvidia.com/gpu
        operator: Exists
        effect: NoSchedule

Lint Findings & Recommendations0 Checks

Manifest passed all validation checks successfully. No deadlocks or scheduling risks detected.

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