5 · Install the SDK & run your first job

Python 3.12 required — your functions are shipped to the workers with cloudpickle, which won't unpickle across versions.

Install

python3.12 -m venv .venv && source .venv/bin/activate
pip install gridweave-sdk jupyterlab

# The onboarding notebooks:
curl -sL https://pub-c48a651bbb2f42988602aa11bb9d9267.r2.dev/tarball/gridweave-sdk.tar.gz | tar xz
jupyter lab gridweave-sdk/onboarding.ipynb

# Colab / an existing 3.12 env, and upgrades:
pip install -U gridweave-sdk

Authenticate and run

Get an access token, and your platform URL (https://platform.… after HTTPS, else http://<MASTER_IP>:8100):

• Onboarded by an admin? Use the token returned at onboarding — the Create Token form (and the /v1/auth/onboard call) hand it back once.
• Signed up yourself? Signup gives a browser session, not an SDK token — mint one under Settings → Access Tokens (a long-lived gw_… token) and use that.

import gridweave
gridweave.auth("YOUR_TOKEN", platform_url="https://platform.example.com")
gridweave.resources()              # nodes, vendors, and free VRAM

@gridweave.remote(vram="4GB")      # no vram → CPU; vram → a GPU with ≥4 GB free
def matmul():
    import torch
    x = torch.randn(4096, 4096, device="cuda")
    return (x @ x).mean().item()

matmul.run()

Going further

• Full SDK reference — client_sdk/README.md: fractional/multi-GPU, train(), serve() (vLLM + custom images), gather(), storage.
• onboarding.ipynb (in the tarball) — walks every capability end to end.
• LLM-ready docs — your deployment serves its own docs at https://app.…/docs, with a downloadable llms.txt: paste it into an LLM and ask it to build a notebook for your workload.

---

Next: 6 · Update the cluster — ship new versions without losing state.