Run your own Python script

Upload your code, choose an image containing its dependencies, and run it on a GPU. The SDK does not install your script's dependencies automatically.

For example, save this as hello.py:

import torch

print(torch.cuda.get_device_name(0))

Then submit it from Python:

import nodus

with nodus.Client() as client:
    code = client.assets.upload("hello.py")
    workload = client.run(
        image="pytorch/pytorch:2.8.0-cuda12.8-cudnn9-runtime",
        source_asset_id=code.id,
        command=["python", "hello.py"],
        budget=5,
    )
    print(workload.id)
    done = workload.wait()
    if not done.succeeded:
        raise RuntimeError(f"Workload ended: {done.status}")
    print(done.logs())

assets.upload() transfers the file explicitly. run() uses the uploaded asset as the source working directory. A filename in command alone never uploads it. For several source files, upload an archive or import a GitHub repository. See code and datasets.

Use a custom container

When you need additional dependencies, package them with your code in an image. For example, put this Dockerfile beside hello.py:

FROM pytorch/pytorch:2.8.0-cuda12.8-cudnn9-runtime
WORKDIR /app
COPY hello.py /app/hello.py

Build and push to a registry Nodus can pull from. Replace the namespace below:

docker build -t YOUR_REGISTRY/hello:v1 .
docker push YOUR_REGISTRY/hello:v1

Submit the image without a source asset:

import nodus

with nodus.Client() as client:
    workload = client.run(
        image="YOUR_REGISTRY/hello:v1",
        command=["python", "/app/hello.py"],
        budget=5,
    )
    print(workload.id)

Use absolute paths for code baked into the image. Pin versions or digests for repeatability. The SDK has no registry-credential argument, so confirm access before using a private image. Do not bake credentials into images.