Imported from jeremylongshore/tons-of-skills-marketplace (plugins/saas-packs/coreweave-pack/skills/coreweave-ci-integration/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill coreweave-ci-integration. Copyright stays with the author (MIT).

CoreWeave CI Integration

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

Overview

Set up CI/CD for CoreWeave GPU cloud workloads: run unit tests with mocked Kubernetes clients on every PR, deploy inference containers to CoreWeave namespaces on merge to main, and validate GPU resource requests against quota. CoreWeave uses standard Kubernetes APIs with GPU-specific scheduling, so CI pipelines authenticate via kubeconfig and manage deployments through kubectl.

GitHub Actions Workflow

Prerequisites

• A repository with protected branches and a trusted runner policy.
• Mocked tests that need no cluster credential, plus a dedicated low-privilege staging identity for optional integration tests.
• Secret-manager-backed CI secrets available only to trusted, non-fork workflows.

Instructions

1. Run deterministic unit and manifest validation on every pull request without credentials.
2. Run live integration checks only after merge or from an approved protected workflow.
3. Bound test resources, collect redacted pod events on failure, and revoke the staging identity if exposure is suspected.
4. Keep deploy approval and production rollout separate from the test job.

# .github/workflows/coreweave-ci.yml
name: CoreWeave CI
on:
  pull_request:
    paths: ['src/**', 'k8s/**', 'Dockerfile']
  push:
    branches: [main]

jobs:
  unit-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '20' }
      - run: npm ci
      - run: npm test -- --reporter=verbose

  deploy:
    if: github.ref == 'refs/heads/main'
    needs: unit-tests
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build and push container
        run: |
          echo "${{ secrets.GHCR_TOKEN }}" | docker login ghcr.io -u ${{ github.actor }} --password-stdin
          docker build -t ghcr.io/${{ github.repository }}/inference:${{ github.sha }} .
          docker push ghcr.io/${{ github.repository }}/inference:${{ github.sha }}
      - name: Deploy to CoreWeave
        env:
          KUBECONFIG_DATA: ${{ secrets.COREWEAVE_KUBECONFIG }}
        run: |
          echo "$KUBECONFIG_DATA" | base64 -d > /tmp/kubeconfig
          export KUBECONFIG=/tmp/kubeconfig
          kubectl set image deployment/inference \
            inference=ghcr.io/${{ github.repository }}/inference:${{ github.sha }}
          kubectl rollout status deployment/inference --timeout=300s

Mock-Based Unit Tests

// tests/coreweave-service.test.ts
import { describe, it, expect, vi } from 'vitest';
import { deployInferenceModel } from '../src/coreweave-service';

vi.mock('@kubernetes/client-node', () => ({
  KubeConfig: vi.fn().mockImplementation(() => ({
    loadFromDefault: vi.fn(),
    makeApiClient: vi.fn().mockReturnValue({
      patchNamespacedDeployment: vi.fn().mockResolvedValue({ body: { status: { readyReplicas: 1 } } }),
      listNamespacedPod: vi.fn().mockResolvedValue({
        body: { items: [{ metadata: { name: 'inference-abc' }, status: { phase: 'Running' } }] },
      }),
    }),
  })),
  AppsV1Api: vi.fn(),
}));

describe('CoreWeave Service', () => {
  it('deploys inference model with GPU requests', async () => {
    const result = await deployInferenceModel('llama-70b', { gpu: 'A100', count: 4 });
    expect(result.status).toBe('deployed');
    expect(result.gpuType).toBe('A100');
  });
});

Integration Tests

// tests/integration/coreweave.integration.test.ts
import { describe, it, expect } from 'vitest';
import { KubeConfig, CoreV1Api } from '@kubernetes/client-node';

const hasKubeconfig = !!process.env.COREWEAVE_KUBECONFIG;

describe.skipIf(!hasKubeconfig)('CoreWeave Live API', () => {
  it('lists GPU nodes in namespace', async () => {
    const kc = new KubeConfig();
    kc.loadFromString(Buffer.from(process.env.COREWEAVE_KUBECONFIG!, 'base64').toString());
    const k8sApi = kc.makeApiClient(CoreV1Api);
    const { body } = await k8sApi.listNamespacedPod('default');
    expect(Array.isArray(body.items)).toBe(true);
  });
});

Error Handling

Output

• A credential-free pull-request test lane and a trusted, scoped integration lane.
• A reproducible CI receipt with test result, manifest validation, and redacted failure evidence.
• Clear rate, quota, and rollout failure handling without weakening branch protection.

Examples

Run the unit suite locally, then trigger the trusted integration job only from the protected branch:

npm test
gh workflow run coreweave-integration.yml --ref main

If the live job cannot schedule a GPU, preserve the namespace events and retry after capacity is confirmed. Do not make the kubeconfig available to pull-request or forked code.

Resources

• CoreWeave Kubernetes Docs
• GitHub Actions Secrets

Next Steps

For deployment patterns, see coreweave-deploy-integration.