Coreweave Core Workflow B

---
name: coreweave-core-workflow-b
description: "'Run distributed GPU training jobs on CoreWeave with multi-node PyTorch. Use when training models across multiple GPUs, setting up distributed training, or running fine-tuning jobs on CoreWeave H100 clusters. Trigger with phrases like \"coreweave training\", \"coreweave multi-gpu\", \"distributed training coreweave\", \"fine-tune on coreweave\". '"
allowed-tools: "Read, Write, Edit, Bash(kubectl:*), Grep"
category: data-science-and-ml
source_repo: jeremylongshore/claude-code-plugins-plus-skills
source_path: "skills/.curated/coreweave-core-workflow-b/SKILL.md"
source_url: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/HEAD/skills/.curated/coreweave-core-workflow-b/SKILL.md
---

# 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

```yaml
# 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

```yaml
# 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

```bash
# 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

| Error | Cause | Solution |
|-------|-------|----------|
| NCCL timeout | Network issue between GPUs | Use NVLink nodes (SXM4/SXM5) |
| OOMKilled | Batch size too large | Reduce batch size or use gradient accumulation |
| Checkpoint save failed | PVC full | Increase storage or prune old checkpoints |
| Job evicted | Preemption | Use on-demand nodes for training |

## Resources

- [CoreWeave CKS](https://docs.coreweave.com/docs/products/cks)
- [PyTorch Distributed Training](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html)

## Next Steps

For troubleshooting, see `coreweave-common-errors`.

---

**Source:** [`jeremylongshore/claude-code-plugins-plus-skills`](https://github.com/jeremylongshore/claude-code-plugins-plus-skills) → `skills/.curated/coreweave-core-workflow-b/SKILL.md`

**Also appears in:** `jeremylongshore/claude-code-plugins-plus-skills/plugins/saas-packs/coreweave-pack/skills/coreweave-core-workflow-b/SKILL.md`