coreweave-core-workflow-b
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".
Best use case
coreweave-core-workflow-b is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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".
Teams using coreweave-core-workflow-b should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/coreweave-core-workflow-b/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How coreweave-core-workflow-b Compares
| Feature / Agent | coreweave-core-workflow-b | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# CoreWeave Core Workflow: GPU Training
## 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`.Related Skills
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