Run from a workload file

Keep a reusable workload definition in nodus.toml. Start with:

nodus init

This creates a GPU smoke test with a $5 budget. It does not start paid work or overwrite an existing file. Review the file, then run:

nodus run

Nodus prints the workload ID, shows progress, and reports the final status and current cost. A failed or cancelled workload exits with a nonzero code.

Use your own image

Replace the starter configuration with your actual image and command:

image = "YOUR_REGISTRY/trainer:v1"
command = ["python", "/app/train.py"]
budget = 5

The image must contain your code and dependencies. The command is an argument list, not a shell command. A budget is a workload ceiling, not a quoted price.

To keep several configurations, save one as train.toml:

nodus run train.toml

For submission without waiting, use nodus submit train.toml. Keep the printed ID to check status, collect logs, or cancel later.

Use the same file in Python

import nodus

with nodus.Client() as client:
    workload = client.run_file("train.toml")
    print(workload.id)
    done = workload.wait()
    if not done.succeeded:
        raise RuntimeError(f"Workload ended: {done.status}")
    print(done.logs())

run_file() returns after acceptance. The CLI run also waits. Both use the same configuration and validation.

Add options as needed

Top-level keys use the same names as Python submission parameters. For example, add gpu = "H100" before any TOML table. Nested dictionaries use TOML tables:

image = "YOUR_REGISTRY/trainer:v1"
command = ["python", "/app/train.py"]
budget = 25
peak_memory_gb = 24

[requirements]
model = "LoRA-fine-tune"

Advanced files can use [[stages]] for stage definitions and nested tables for policy and continuity. Explicit stages supply their own sources, so omit top-level image and command. A workload file does not build an image or automatically upload files from your computer.