Run training, fine-tuning, and batch experiments when your local machine lacks the GPU memory or capacity they need. Provide your container image and command, then use one Python client to submit work, follow progress, and retrieve results. Nodus matches the workload to available GPU capacity. Add a budget to set a workload spending limit.

1. Install and sign in

pip install nodus-compute
nodus login

Get nodus-compute on PyPI. Requires Python 3.10 or newer. Upgrading an existing installation? Use pip install --upgrade nodus-compute. These docs cover SDK 0.3.x.

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.

Running nodus login again reuses a valid login. Use nodus login --force for a fresh sign-in.

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

Before starting a workload, open Billing and add a payment method. New accounts start with $30 in credits, but a card is required to use them. Adding a card does not purchase credits. If you joined a shared workspace, its administrator manages the payment method.

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:2.8.0-cuda12.9-cudnn9-runtime",
        command=[
            "python", "-c",
            "import torch\n"
            "assert torch.cuda.is_available()\n"
            "print(torch.cuda.get_device_name(0))",
        ],
        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 and shows live logs, lifecycle events, and elapsed time while waiting. Training workloads also show reported steps or epochs. The final output includes 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.

Prefer the terminal?

nodus init
nodus run

init creates nodus.toml with the GPU smoke test and a $5 budget. Review the file, then run submits it and waits for completion. Edit the image, command, and budget to run your own workload. See workload files.

nodus status WORKLOAD_ID
nodus logs WORKLOAD_ID
nodus cancel WORKLOAD_ID

Choose an optional preference

Set optimization="lowest_cost", "lower_cost", "balanced", "faster", or "fastest". The default is balanced. Nodus balances expected completion cost and completion time when qualified estimates are available, using price and GPU performance signals otherwise. Preferences do not guarantee total cost or runtime. Older deployments may record the preference without applying it.

Set gpu="H100" to require a GPU model, or omit it to let Nodus choose. No runtime estimate is needed. See resource options.

GPU enforcement, live logs, login verification, and spending limits require a compatible Nodus backend. Installing the SDK alone does not enable these server features. See backend compatibility before relying on them with a custom or older deployment.

Run your own code

Upload your script with client.assets.upload() or package it in a container. Choose an image with your dependencies and pass its command to client.run().

• Run a Python script
• Attach code and datasets
• Train or fine-tune a model
• Read logs and download results
• Use Nodus with a coding agent

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.