From coreweave-pack
Manages CoreWeave persistent storage for ML training data and model artifacts using Kubernetes PVCs and Jobs. For large datasets, storage classes, and GPU data pipelines.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin coreweave-packThis skill is limited to using the following tools:
| Class | Type | Use Case |
Sets up distributed PyTorch GPU training on CoreWeave Kubernetes with multi-node DDP, Jobs, PVCs for H100/A100 clusters and model fine-tuning.
Securely transfers, encrypts, and manages checkpoints for training data and model artifacts on Vast.ai GPU instances using SCP, SSH, Python, and cloud storage.
Runs Python workloads on Hugging Face Jobs with managed CPUs, GPUs, TPUs, secrets, and Hub persistence for data processing, batch inference, experiments, and scheduled tasks without local setup.
Share bugs, ideas, or general feedback.
| Class | Type | Use Case |
|---|---|---|
shared-hdd-ord1 | HDD | Training data archival |
shared-ssd-ord1 | SSD | Model weights, active datasets |
block-nvme-ord1 | NVMe | High-performance training |
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: model-storage
spec:
accessModes: ["ReadWriteMany"]
resources:
requests:
storage: 500Gi
storageClassName: shared-ssd-ord1
apiVersion: batch/v1
kind: Job
metadata:
name: download-model
spec:
template:
spec:
restartPolicy: Never
containers:
- name: downloader
image: python:3.11-slim
command: ["python3", "-c"]
args:
- |
from huggingface_hub import snapshot_download
snapshot_download("meta-llama/Llama-3.1-8B-Instruct", local_dir="/models/llama-8b")
volumeMounts:
- name: models
mountPath: /models
env:
- name: HF_TOKEN
valueFrom:
secretKeyRef:
name: hf-token
key: token
volumes:
- name: models
persistentVolumeClaim:
claimName: model-storage
For RBAC configuration, see coreweave-enterprise-rbac.