Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/coreweave-core-workflow-b/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill coreweave-core-workflow-b. Copyright stays with the author (MIT).

CoreWeave Core Workflow: GPU Training

Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.

Overview

Run distributed GPU training on CoreWeave: single-node multi-GPU and multi-node training with PyTorch DDP, Slurm-on-Kubernetes, and shared storage.

Prerequisites

• CKS cluster with multi-GPU node pools (8xA100 or 8xH100)
• Shared storage (CoreWeave PVC or NFS)
• Training container with PyTorch and NCCL

Instructions

Step 1: Single-Node Multi-GPU Training

# training-job.yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: llm-finetune
spec:
  template:
    spec:
      restartPolicy: Never
      containers:
        - name: trainer
          image: ghcr.io/myorg/trainer:latest
          command: ["torchrun"]
          args:
            - "--nproc_per_node=8"
            - "train.py"
            - "--model_name=meta-llama/Llama-3.1-8B"
            - "--batch_size=4"
            - "--epochs=3"
          resources:
            limits:
              nvidia.com/gpu: "8"
              memory: 512Gi
              cpu: "64"
          volumeMounts:
            - name: data
              mountPath: /data
            - name: checkpoints
              mountPath: /checkpoints
      volumes:
        - name: data
          persistentVolumeClaim:
            claimName: training-data
        - name: checkpoints
          persistentVolumeClaim:
            claimName: model-checkpoints
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                  - key: gpu.nvidia.com/class
                    operator: In
                    values: ["A100_NVLINK_A100_SXM4_80GB"]

Step 2: Persistent Storage for Training Data

# storage.yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: training-data
spec:
  accessModes: ["ReadWriteMany"]
  resources:
    requests:
      storage: 500Gi
  storageClassName: shared-hdd-ord1
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: model-checkpoints
spec:
  accessModes: ["ReadWriteMany"]
  resources:
    requests:
      storage: 200Gi
  storageClassName: shared-ssd-ord1

Step 3: Monitor Training Progress

# Watch training logs
kubectl logs -f job/llm-finetune

# Check GPU utilization
kubectl exec -it $(kubectl get pod -l job-name=llm-finetune -o name) -- nvidia-smi

# Check training metrics
kubectl exec -it $(kubectl get pod -l job-name=llm-finetune -o name) -- \
  cat /checkpoints/training_log.json | tail -5

Error Handling

Output

• A GPU training Job bound to explicitly selected node, storage, and checkpoint resources.
• A repeatable monitoring trail: pod state, GPU utilization, and training metrics are available to the authorized operator without exposing model inputs or credentials.
• Durable checkpoints on the approved PVC so a failed or preempted job can resume from a known state rather than silently restarting training.

Examples

Before scheduling a costly multi-GPU run, submit a small trusted smoke job to the same namespace and inspect its scheduling event and GPU allocation:

kubectl apply -f training-job.yaml
kubectl get job llm-finetune --watch
kubectl get pods -l job-name=llm-finetune -o wide
kubectl logs job/llm-finetune --tail=100

If the job cannot schedule, stop before increasing quota or changing node selectors. Confirm the namespace quota, approved GPU class, and PVC binding with the platform owner; preserve the failed event output with secrets and customer data redacted.

Resources

• CoreWeave CKS
• PyTorch Distributed Training

Next Steps

For troubleshooting, see coreweave-common-errors.