A wheel whose top-level module is `app` collides with anything else in a
user's venv, so the package that is about to be published takes the name
it is published under. Only the Python package moves; the repo, the
Docker WORKDIR and the compose project keep theirs.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
A python node's settings are constants of its own function, so they are passed
the way its ports are: by name. The controller binds them to the compiled
function, the `params` field is gone from the worker and remote protocols, and
a setting sharing a port's name is reported as a node error rather than
shadowing it. The panel's scaffold follows suit and keeps the header in step
with both ports and settings.
The demo's `pace` moves from a flow input to a setting of the training node,
which is what it always was.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NUb8YpL2s3gmN9WTACTt4q
The engine runs where the automations are and the GPU is somewhere else,
usually behind a different network — so the worker connects out and the engine
answers over the socket it was given. Nothing has to expose Redis, and the
same connection works through the tunnel the hosted access will use.
What travels is the protocol the local pool already speaks, so a node cannot
tell which kind of worker it is on. A node declares device: gpu and
device_policy, the label is resolved per call (a worker attaching later needs
no rebuild), and a run whose labels nothing carries waits in the queue saying
what it waits for rather than failing — submit from the couch, the GPU box
picks it up when it is switched on.
Two things had to move with it. Compiling now happens on the machine that will
run the node: a node importing torch is correct on the GPU box and a missing
module on the engine, so checking it here failed nodes that were fine. And the
artifact endpoint accepts a worker's own credential, because storing a
checkpoint is exactly what that credential is for — and only that.
Verified against the real split: the training ran on this host (its checkpoint
names the machine and a numpy the engine does not have), streamed 40 metric
points back mid-run, and the evaluate node read the checkpoint on the engine.
Cancel kills the remote training; pulling the worker fails the run in six
seconds instead of waiting out its ten-minute timeout.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
stroblmeandClaude Opus 5