# Get started

> Install the Nodus CLI, sign in, run a command on a GPU, follow it, get its results and see exactly what it cost.

Source: https://www.nodus-compute.ai/docs/getting-started/
Build revision: 53bfc045e800bf7bb44fa18623013c7aa9c9971e

Run a small GPU check, inspect its status, then move on to a script that produces a downloadable result. You need a terminal, a browser, and an activated Nodus organization.

1. **Install the CLI.**

   **pip**

   Terminal window

   ```sh
   pip install nodus-compute
   ```

   The Python package (Python 3.10 or newer) includes the `nodus` CLI.

   **macOS and Linux**

   Terminal window

   ```sh
   curl -fsSL https://nodus-compute.ai/install | sh
   ```

   **Windows**

   Terminal window

   ```powershell
   irm https://nodus-compute.ai/install.ps1 | iex
   ```

2. **Sign in and finish account setup.**

   Terminal window

   ```sh
   nodus login
   ```

   Creating your first organization requires a verified email and a verified card. Complete onboarding in your browser, then return to the CLI sign-in. Card setup does not collect a payment or enable auto-recharge. Eligible accounts receive a **$30 starter grant**, valid for 30 days; verify your available balance before running work. If you join an existing organization, its administrator manages billing.

   Terminal window

   ```sh
   nodus whoami
   nodus get jobs
   nodus billing
   ```

   These commands check the account, connection, and balance without starting compute. An empty Job list is normal for a new project. On a machine without a browser, use `nodus login --device`.

3. **Review a bounded GPU check.**

   Terminal window

   ```sh
   nodus run --name first-gpu-check --gpu L4 --image nodus/pytorch \
     --no-source --max-cost 1 --timeout 10m --no-warm --dry-run \
     -- python -c "import torch; print(torch.cuda.get_device_name())"
   ```

   Review the returned estimate and any blockers. The $1 limit and 10-minute timeout are example limits, not a quoted price or a promise that capacity will be available. `--no-source` keeps local files on your machine. `--dry-run` does not create the Job.

4. **Run after accepting the estimate.**

   Remove only `--dry-run` from the command above. This submits paid compute. The CLI streams progress and logs; check that the Job succeeds and the logs include the GPU name. If you delegate this step to a coding agent, approve the request before it submits.

   Terminal window

   ```sh
   nodus describe job/first-gpu-check
   nodus logs job/first-gpu-check
   ```

   A created Job is not proof of completed work. If it fails, inspect its status and events before retrying. Use a new name for a new run; avoid resubmitting after an uncertain response without checking the original.

5. **Download a result from your own code.**

   The GPU check prints to its logs. Continue with [Run your own code](https://www.nodus-compute.ai/docs/getting-started/quickstart/) for a complete training script, a declared `metrics.json` output, and its download command.

   Terminal window

   ```sh
   nodus billing
   nodus delete job/first-gpu-check
   ```

   Review usage in Billing. Delete the completed check when you no longer need its status and logs. To stop it while it is running, use `nodus cancel job/first-gpu-check` and verify the final status.

Using a coding agent?

Connect Claude Code, Codex or Cursor to Nodus at [/connect](https://www.nodus-compute.ai/connect/), then ask it to run your command. The agent uses the same account, projects and spending limits as the CLI.

## Next steps

* [Concepts](https://www.nodus-compute.ai/docs/concepts/): resources, projects, and spending controls.
* [Guides](https://www.nodus-compute.ai/docs/guides/): one guide per feature.