Run your Nu program on a distributed cluster.
Wrap any Nu tree in Teleport. It runs on a remote worker, same code, cluster-scale compute.
See it.
Provision workers by tag. Send any subtree to a tagged worker. Same shape as any other Nu tree.
Your program, running on the cluster.
nu.cluster lets you provision Ray workers as fabric services and teleport any part of your Nu tree onto them. The body does not know or care where it runs.
Wrap it with the cluster provider, point at a worker, done. No task decorators, no result futures to juggle.
What you can do with it.
One import turns a laptop-shaped program into a cluster-shaped one.
push heavy work off
Send the slow parts to workers.
Model training, big data crunching, long-running jobs. Keep the driver light, let the cluster do the heavy lifting.
scale up
Same code, one worker or a hundred.
Add another entry to the worker list and target it. No rewrite, no framework switch. Prototype on one box, ship on the cluster.
named workers
Pin work to a role.
Ledger writer, indexer, ui host, anything. Provision each once, address by tag, send the right work to the right place.
Combines well with.
Cluster runs the work. Other fabrics hold the state and wire the processes together.
nu.kv
Durable state fabric.
Persist results, weights, metrics, job state. Workers share the same tree of Refs across the cluster.
nu.proxy
Fabrics on the wire.
Host a fabric on one worker, reach it from everywhere else. Perfect pair when driver, workers, and store all live apart.
nu.mp
Local process fabric.
Same shape as nu.cluster, no Ray needed. Prototype your topology on one box before you spin the cluster.
Try Nu.
One command gets you the wheel with every fabric. Then follow the movies tutorial to build a real app in an afternoon.
01 Install
pip install "nustack-py[all]"02 Run the demo
nu demo movies03 Build your app
Browse examplesLike what you see?
The project is young. Star it, join the room, watch what we ship next.