Get started
Getting started
Install an SDK, create a key, and make your first API call in under five minutes.
Coming soon
1. Install an SDK
Node Data ships first-party SDKs for the four languages most common in robotics and applied ML. The Python SDK is the reference implementation; every endpoint surfaces there first.
# Python — on PyPI
pip install nodedata
# TypeScript / JavaScript — not on npm yet, install from the tarball
npm install https://www.nodedata.dev/node-data-sdk-latest.tgz2. Create an API key
Sign in to Dashboard → API keys and generate a key. Keys are scoped to either test (sandbox marketplace, no real funds) or live (production). Export the key as an environment variable:
export NODE_DATA_API_KEY="nd_live_•••••••••••••••••••••"Treat keys like passwords
3. Make your first call
Search the marketplace and print the first five hits. No payment is required to call the search API.
from nodedata import NodeData
nd = NodeData() # reads NODE_DATA_API_KEY from env
# Browse the marketplace
page = nd.models.list(q="manipulation policy", limit=5)
for asset in page["items"]:
print(asset["slug"], asset["price"]["amount"] / 100, asset["license"])4. Download a model
Downloads stream from a region-aware CDN with resumable byte ranges. Models you have not licensed can still be inspected via their model card, but the weight files require a successful checkout.
asset = nd.models.retrieve("bin-picking-policy-v2")
signed = nd.models.download(asset["id"])
print(signed["url"], "expires in", signed["expires_in"], "seconds")5. Publish a model
Uploading is symmetric: hand the SDK a list of file paths, a license, and pricing. The SDK chunks large files via the resumable upload protocol and writes a content-addressed manifest.
nd.models.create(
title="Lift and Place",
description="Pick-and-place policy trained on 12k episodes.",
type="rl-policy",
category="manipulation",
license="MIT",
price_cents=4900, # integer cents, not dollars
frameworks=["pytorch"],
storage_path=uploaded["storage_path"],
)Next steps
- Uploading models — packaging, formats, and validation.
- API reference — every endpoint documented.
- Deploying models — Jetson, ROS 2, and cloud inference.