Provide a container image, a command, and a budget. Nodus finds GPU capacity and runs your workload. Use the same Python client for training, fine-tuning, or a batch of experiments.

1. Install and sign in

pip install nodus-compute
nodus login

Requires Python 3.10 or newer. Upgrading an existing installation? Use pip install --upgrade nodus-compute. Browser login without a URL requires 0.1.3 or newer.

Your browser opens Nodus sign-in. Sign in and approve the code matching your terminal. You can then close the tab. The terminal finishes automatically and saves your credentials. Python clients use that login without extra setup.

For a machine without a browser, use nodus login --no-browser. See authentication for API keys and custom deployments.

2. Run your first workload

This GPU smoke test prints the available GPU name. No local script is uploaded. It submits paid compute with a $5 workload budget. Available capacity and account limits still determine admission.

Save this as first_workload.py:

import nodus

with nodus.Client() as client:
    workload = client.run(
        image="pytorch/pytorch:2.6.0-cuda12.4-cudnn9-runtime",
        command=[
            "python", "-c",
            "import torch\n"
            "assert torch.cuda.is_available()\n"
            "print(torch.cuda.get_device_name(0))",
        ],
        compute_class="accelerator",
        model="GPU-smoke-test",
        budget=5,
    )
    print("Workload:", workload.id)
    done = workload.wait()
    print(done.status, done.cost_now_usd)
    if not done.succeeded:
        raise RuntimeError(f"Workload {done.id} ended: {done.status}")
    print(done.logs())

Run it with python first_workload.py. It prints the workload ID, waits for completion, then prints the status, current cost, and GPU name.

run() accepts the workload. wait() waits for a terminal status, so check succeeded before using results. Ctrl+C while waiting requests cancellation and remote resource cleanup.

The script prints the GPU name from the workload logs. For files produced by your own program, see logs and results.

Run your own code

Package your script and dependencies in a container image, then pass its image and command to client.run(). The SDK does not upload your local files.

• Run a Python script
• Train or fine-tune a model
• Read logs and download results

For individual options, use the Python reference and parameter reference. See troubleshooting if a run fails.

Contributing

See RELEASING.md for release steps. Licensed under Apache-2.0.