Lakeshore

Lakeshore runs Python functions somewhere other than the machine in front of you. You decorate a function with @udf, call it, and the result comes back — Lakeshore packs the arguments, hands the work to a queue, and a worker somewhere else runs it and returns the value.

You'd reach for it when the work doesn't fit locally: a GPU job, a long sweep, a fan-out of identical calls. You write Python; Lakeshore owns the queueing, the transport, and the worker side.

Install

Pick the surface you are starting from — each page below covers the authentication and first-run check for that one.

The CLI is optional for the local tier and required once you manage providers, daemons, or a hosted control plane. See Release notes for the CLI changelog.

The CLI and the SDK each need a server URL, a namespace, and a token. Get them from whoever runs your control plane, or stand one up yourself with Admin setup.

Two tiers, one decorator

A bare @udf is a local call — an ordinary in-process function call with a run context wrapped around it. Nothing touches the network, and the HTTP/msgpack stack is never even imported.

import dreamlake.lakeshore as dls

@dls.udf
def stats(xs: list[float]) -> dict:
    return {"n": len(xs), "mean": sum(xs) / len(xs)}

print(stats([1.0, 2.0, 3.0]))     # {'n': 3, 'mean': 2.0} — runs here

Adding queue= makes the same function remote. A plain call now submits to that queue and blocks for the result; .submit() returns the invocation id instead, so you can walk away and reconnect later.

import dreamlake.lakeshore as dls

@dls.udf(queue="cpu")
def double(x: int) -> int:
    return x * 2

print(double(21))                 # 42 — submit, wait, unpack
inv_id = double.submit(21)        # a durable string id; no Future type

Point the SDK at a control plane with LAKESHORE_URL (plus LAKESHORE_CLIENT_TOKEN and LAKESHORE_NAMESPACE when the plane requires auth). With LAKESHORE_URL unset, submits go to a local SQLite plane under LAKESHORE_HOME (default ~/.lakeshore) — handy for trying the remote tier without any server at all.

Something has to drain the queue. The simplest drainer is a native Python worker on your own machine:

lakeshore worker start --url http://localhost:8080 --queue cpu

The three pieces

your process                 control plane                  worker host
────────────                 ─────────────                  ───────────

 @udf call    ──submit──▶   queue: "cpu"     ◀──poll──   nymph daemon
 result       ◀──result──   invocation done   ──ack───▶   nymph daemon

Every wire call is daemon-initiated and outbound. The control plane never opens a connection to a worker, so a worker needs no inbound port, no port forward, and no firewall change.

Where to go next