Nodus Python SDK

Run a training job on rented GPUs from Python. Nodus places the work on the cheapest capacity that fits, checkpoints it, and resumes it if the machine is taken back — so a fine-tune that would have died at hour six finishes.

import nodus

with nodus.Client() as client:
    wl = client.run(command=["python", "train.py"], budget=20)
    print(client.wait(wl.id).status)

Install

pip install nodus_compute

The distribution is nodus_compute; the import is nodus — the same split as pip install scikit-learn and import sklearn.

Point it at your account

Two settings. Sign in at https://nodus.run/console/, create an API key — it is shown once — and the console's quickstart comes with both already filled in.

export NODUS_API_KEY=nk_live_…
export NODUS_BASE_URL=https://…

Or pass them directly: nodus.Client(api_key=…, base_url=…).

There is no built-in address. With neither setting the client raises ConfigurationError before it opens a socket, naming what is missing — it does not dial a guess and hand you a name-lookup error.

Running work

run() takes a flat brief and returns a Workload:

import nodus

with nodus.Client() as client:
    wl = client.run(
        image="python:3.11-slim",
        command=["python", "train.py", "--epochs", "3"],
        peak_memory_gb=24,          # picks capacity that fits
        expected_runtime_hours=6,   # informs the estimate
        budget=40,                  # a ceiling, not a reservation
    )

    done = client.wait(wl.id)       # polls until the run is over
    print(done.status, done.cost_now_usd)

Only command is required. Everything else narrows the search or bounds the cost; omit budget and the run is uncapped.

Your image must be able to fetch a small binary — it needs curl, wget, or python3 on the PATH. Most ML images have one. A bare ubuntu image has none of them, and a machine that cannot fetch the runner is a machine you are billed for while it does nothing.

Waiting

wait() polls until the workload is terminal and has no deadline of its own. An 18-hour run is normal, and a client that gave up on one would not stop it — the work would carry on and keep billing while your program believed it had failed. A transient network failure does not end the wait either; only a permanent one (a revoked key, an unknown workload) is raised.

Pass timeout_seconds= if you want a bound. It ends the waiting, not the run: APITimeoutError is raised, the workload continues, and .cancel() is what stops it.

Watching it

for event in client.stream_events(wl.id):
    print(event.type, event.message)

print(client.logs(wl.id))           # the job's own stdout and stderr
print(wl.refresh().cost_now_usd)    # charged plus what is accruing right now

Async

AsyncClient mirrors Client method for method:

async with nodus.AsyncClient() as client:
    wl = await client.run(command=["python", "train.py"])
    done = await client.wait(wl.id)

Errors

Every failure is a subclass of NodusError, so one except catches the lot:

Command line

nodus run --command "python train.py" --budget 20
nodus status wl_…
nodus logs wl_…

Licence

Apache-2.0. See LICENSE.