name: coreweave-data-handling description: 'Handle training data and model artifacts on CoreWeave persistent storage.

Use when managing large datasets, configuring storage classes,

or implementing data pipelines for GPU workloads.

Trigger with phrases like "coreweave data", "coreweave storage",

"coreweave pvc", "coreweave dataset management".

' allowed-tools: Read, Write, Edit, Bash(kubectl:*), Grep version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• gpu-cloud
• kubernetes
• inference
• coreweave compatibility: Designed for Claude Code

CoreWeave Data Handling

Overview

CoreWeave GPU cloud workloads involve large-scale data artifacts: model weights (multi-GB safetensors/GGUF), training datasets (parquet, TFRecord, WebDataset), checkpoint snapshots, and inference cache volumes. Data flows through Kubernetes PersistentVolumeClaims backed by region-specific storage classes. Compliance requires encryption at rest via the storage driver, namespace-scoped RBAC for volume access, and audit logging for any data egress from GPU nodes.

Data Classification

Data Import

import { KubeConfig, BatchV1Api } from '@kubernetes/client-node';

async function importDataset(pvcName: string, sourceUrl: string, namespace: string) {
  const kc = new KubeConfig();
  kc.loadFromDefault();
  const batch = kc.makeApiClient(BatchV1Api);
  const job = {
    metadata: { name: `import-${Date.now()}`, namespace },
    spec: { template: { spec: {
      restartPolicy: 'Never',
      containers: [{ name: 'loader', image: 'python:3.11-slim',
        command: ['python3', '-c', `
import urllib.request, hashlib
dest = '/data/dataset.tar.gz'
urllib.request.urlretrieve('${sourceUrl}', dest)
print(f"SHA256: {hashlib.sha256(open(dest,'rb').read()).hexdigest()}")`],
        volumeMounts: [{ name: 'storage', mountPath: '/data' }],
      }],
      volumes: [{ name: 'storage', persistentVolumeClaim: { claimName: pvcName } }],
    }}}
  };
  await batch.createNamespacedJob(namespace, { body: job });
}

Data Export

async function exportCheckpoint(pvcName: string, destBucket: string, ns: string) {
  // Validate export destination is in approved region list
  const APPROVED_REGIONS = ['us-east-1', 'us-central-1', 'eu-west-1'];
  const region = destBucket.split('-').slice(0, 3).join('-');
  if (!APPROVED_REGIONS.some(r => destBucket.includes(r))) {
    throw new Error(`Export blocked: ${region} not in approved regions`);
  }
  // Stream from PVC → object storage with integrity check
  const exportCmd = `tar czf - /models | gsutil cp - gs://${destBucket}/export.tar.gz`;
  console.log(`Exporting from PVC ${pvcName} to ${destBucket}`);
  return exportCmd;
}

Data Validation

interface ModelArtifact {
  name: string; format: 'safetensors' | 'gguf' | 'bin' | 'pt';
  sizeBytes: number; sha256: string;
}

function validateArtifact(artifact: ModelArtifact): string[] {
  const errors: string[] = [];
  if (!artifact.name || artifact.name.length > 255) errors.push('Invalid artifact name');
  if (artifact.sizeBytes <= 0) errors.push('Size must be positive');
  if (!/^[a-f0-9]{64}$/.test(artifact.sha256)) errors.push('Invalid SHA-256 hash');
  if (!['safetensors', 'gguf', 'bin', 'pt'].includes(artifact.format)) errors.push(`Unsupported format`);
  return errors;
}

Compliance

• All PVCs use encrypted storage classes (AES-256 at rest)
• HuggingFace and API tokens stored in Kubernetes Secrets with KMS encryption
• Namespace-scoped RBAC restricts volume mount access to authorized workloads
• Data egress from GPU nodes logged via network policy audit
• Training datasets with PII processed only in approved regions (data residency)
• Checkpoint retention enforced via CronJob garbage collection (30-day default)
• SOC 2 Type II audit trail for all storage provisioning and deletion events

Error Handling

Resources

• CoreWeave Storage Docs
• Kubernetes PVC Reference

Next Steps

See coreweave-security-basics.