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Run your own code

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This page runs a script from your own directory on a GPU, then downloads the file it wrote. It assumes you have installed the CLI and signed in. The source and command sequence live in the repository’s examples/cli/quickstart example. Creating an organization requires email and card verification.

  1. Put your code in a directory. Any directory works; this one holds a small PyTorch training loop that saves its final metrics to /nodus/outputs/metrics.json.

    train.py
    """Fit a small linear model, print progress and save metrics.json as the declared output "metrics"."""
    

    import json
    import pathlib
    

    import torch
    

    device = "cuda" if torch.cuda.is_available() else "cpu"
    print("device:", torch.cuda.get_device_name() if device == "cuda" else "cpu")
    

    torch.manual_seed(0)
    x = torch.randn(1024, 16, device=device)
    y = x @ torch.randn(16, 1, device=device)
    w = torch.zeros(16, 1, device=device, requires_grad=True)
    opt = torch.optim.SGD([w], lr=0.1)
    

    for step in range(200):
        loss = ((x @ w - y) ** 2).mean()
        opt.zero_grad()
        loss.backward()
        opt.step()
        if step % 50 == 0:
            print(f"step {step} loss {loss.item():.4f}")
    

    print(f"final loss {loss.item():.6f}")
    out = pathlib.Path("/nodus/outputs")
    out.mkdir(parents=True, exist_ok=True)
    (out / "metrics.json").write_text(json.dumps({"final_loss": loss.item(), "steps": 200}))

  2. Review the estimate, then run. From the directory containing train.py:

    Terminal window
    nodus run --name cli-quickstart --gpu L4 --image nodus/pytorch \
      --max-cost 1 --timeout 10m --no-warm --dry-run \
      --output metrics=/nodus/outputs/metrics.json -- python train.py

    This dry run uploads the directory for inspection but does not create the Job. Inspect the estimate and any blockers. The limits are choices for this example, not a price quote or a guarantee that it will complete. After you accept the estimate, submit the same request without --dry-run:

    Terminal window
    nodus run --name cli-quickstart --gpu L4 --image nodus/pytorch \
      --max-cost 1 --timeout 10m --no-warm \
      --output metrics=/nodus/outputs/metrics.json -- python train.py

    This starts paid compute. The CLI uploads this directory, streams the logs and returns your command’s exit code. Check for a successful final status and training progress in the logs. The script runs 200 steps and writes metrics.json; its exact loss and runtime depend on the environment. Files excluded by .gitignore or .nodusignore stay local. Inspect the directory before uploading it.

  3. Get the output. The --output flag declared metrics; copy it back:

    Terminal window
    nodus cp job/cli-quickstart:outputs/metrics metrics.json

    The file is checked against its SHA-256 digest. Inspect final_loss and steps in the downloaded JSON. If the Job fails or no output is listed, inspect its status and logs instead of reporting a saved result.

Leave it running

Section titled “Leave it running”

Press Ctrl+C during a run and the CLI asks whether to cancel the Job; answer n to detach and leave it running. Or start with -d to return as soon as the Job exists. A detached Job keeps running:

Terminal window
nodus get jobs                        # every Job in the project, with phase and cost so far
nodus get jobs --mine -w              # only yours, updating live
nodus logs -f job/cli-quickstart      # stream the output again
nodus describe job/cli-quickstart     # attempts, placement, cost, conditions and events
nodus cancel job/cli-quickstart       # request cancellation, then verify status

Control the cost

Section titled “Control the cost”
Flag Effect
--max-cost 5 Set a $5 Job spending limit; choose a limit that fits your Job
--timeout 2h The Job is stopped after two hours of wall-clock time
--dry-run Review the estimate without creating the Job

nodus billing shows your balance and this month’s spend; nodus billing usage --group-by day breaks it down.

Clean up

Section titled “Clean up”

Finished Jobs are kept for 30 days so you can read their logs and outputs, then deleted. To delete one now:

Terminal window
nodus delete job/cli-quickstart

Next steps

Section titled “Next steps”