Skip to main content
GhostNexus
Integrations

GPU compute wherever
you already work

Run GPU jobs directly from your CI pipeline or Jupyter notebook — no infrastructure changes required.

GitHub Actions

CI/CD GPU jobs

Available

Dispatch GPU training, inference, or benchmark jobs directly from your CI workflow with one step.

Jupyter Magic

Notebook GPU execution

Available

Run any notebook cell on a remote GPU with a single %%ghostnexus magic command.

GitHub Actions

The ghostnexus/ghostnexus-run action submits your script to a GhostNexus GPU, streams logs back to your CI run, and fails the workflow if the job fails.

1Add your API key as a repository secret

Go to Settings → Secrets → Actions and add:

GHOSTNEXUS_API_KEY = your key from the dashboard

2Reference a script file in your repo

yaml — .github/workflows/train.yml
# .github/workflows/train.yml
name: GPU Training

on: [push]

jobs:
  train:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Run on GPU
        uses: ghostnexus/ghostnexus-run@v1
        with:
          api-key: ${{ secrets.GHOSTNEXUS_API_KEY }}
          task-name: train-${{ github.sha }}
          script-path: scripts/train.py
          timeout-minutes: 60

3Or write an inline script

yaml — inline script
- name: GPU benchmark
  uses: ghostnexus/ghostnexus-run@v1
  with:
    api-key: ${{ secrets.GHOSTNEXUS_API_KEY }}
    task-name: benchmark
    script: |
      import torch
      t = torch.randn(4096, 4096).cuda()
      result = torch.mm(t, t)
      print(f"GPU: {torch.cuda.get_device_name(0)}")
      print(f"VRAM: {torch.cuda.memory_allocated()/1e9:.1f} GB")

Available inputs

InputRequiredDescription
api-keyYesYour GhostNexus API key (use secrets.GHOSTNEXUS_API_KEY)
task-nameNoJob name shown in dashboard (default: ci-job)
script-pathEither/orPath to a .py file in your repository
scriptEither/orInline Python script to run
timeout-minutesNoMax wait time in minutes (default: 30)

Available outputs

job-idGhostNexus job identifier
statuscompleted | failed | timeout
cost-creditsCredits consumed by the job
duration-secondsActual GPU runtime in seconds

Jupyter Magic Commands

Install the ghostnexus-magic package to add a %%ghostnexus cell magic to any JupyterLab or classic Jupyter notebook. The cell runs on a remote GPU; output streams back inline.

1Install and configure

bash
# Install
pip install ghostnexus-magic

# In your notebook
%load_ext ghostnexus_magic
%ghostnexus_config --api-key YOUR_API_KEY

2Use the %%ghostnexus magic in any cell

python — notebook cell
%%ghostnexus --task resnet-training
import torch
import torchvision

model = torchvision.models.resnet50(pretrained=False).cuda()
x = torch.randn(32, 3, 224, 224).cuda()

with torch.no_grad():
    out = model(x)

print(f"GPU: {torch.cuda.get_device_name(0)}")
print(f"Output shape: {out.shape}")

Magic command options

--task NAMEJob name in the dashboard (default: notebook-job)
--timeout MINUTESMax wait time before timeout (default: 30)
--no-logsHide output logs, only show status badge

Note on environment

The cell runs in a fresh Python environment on the GPU node. To install packages, add import subprocess; subprocess.run(['pip', 'install', 'torch']) at the top of your cell or pre-install them on a custom base image.

Ready to integrate?

Get your API key from the dashboard, add it to your secrets, and start dispatching GPU jobs in minutes.

Get your API keyREST API reference