Code and datasets

Assets let you upload code and attach datasets without rebuilding a container. The image still supplies Python, libraries, and system dependencies.

Upload or import

Inside a with nodus.Client() as client: block:

Python

code = client.assets.upload("train.py")

Uploads accept a file or archive. Imports provide alternatives:

Call	Source
`client.assets.import_github("owner/repo", ref="main")`	Source repository
`client.assets.import_huggingface("owner/dataset")`	Hugging Face dataset, not model weights
`client.assets.import_url("https://example.com/data.csv")`	Direct HTTPS download

These calls return an Asset after the transfer completes. Keep its id to reuse it in later workloads. GitHub and Hugging Face imports accept token= for private access. Supply tokens through your secret manager. Do not put them in workload files. A signed HTTPS link can authorize a private URL download.

Attach code and data

Python

import nodus

with nodus.Client() as client:
    code = client.assets.upload("train.py")
    dataset = client.assets.upload("data.csv")
    workload = client.run(
        image="YOUR_REGISTRY/trainer:v1",
        source_asset_id=code.id,
        command=["python", "train.py"],
        inputs=[{"name": "training", "asset_id": dataset.id}],
        outputs={"model": "model.bin"},
        budget=25,
    )
    print(workload.id)

The source asset is extracted into the working directory. Each input is extracted into a directory exposed to your program as NODUS_INPUT_<name>. In this example, data.csv is inside the directory named by NODUS_INPUT_training.

Pass the returned asset IDs through source_asset_id and named inputs as shown above. client.run() does not accept assets or a top-level asset_id. Putting these fields, or source_asset_id, inside extra does not attach files and is rejected before submission.

Your program must write model.bin in its working directory for the declared model output to be available. After successful completion, call workload.download() to retrieve it. See logs and results.

Use at most eight named inputs. Direct input URIs and arbitrary environment variable injection are not supported. For dependencies between workload stages, use stage input references.

From the terminal

Bash

nodus upload train.py
nodus assets

upload prints the asset ID to reuse as source_asset_id in a workload file. assets lists your uploads and imports.

Manage stored assets

client.assets.list() returns up to the 500 most recent assets. client.assets.delete(asset_id) removes an asset when you no longer need it. Keep assets required by pending workloads. AsyncClient.assets exposes the same methods with await.