The workflow this serves: make a venv, install what you work with, then `pip
install fluksio` into the same one. Building a second environment beside it
was exactly wrong — the packages the nodes need are already here, and the
Modules screen was asking for them a second time.
`NODE_VENV=auto` (the default) adopts that venv. It declines in the three
cases where adopting would be wrong: `managed` says otherwise, a managed venv
already exists and may hold packages somebody installed on purpose, or the
engine is not running from a venv at all. The images set `managed`, since the
venv in them holds the app and nothing of anybody else's.
An adopted venv is never written to. `uv pip sync` makes a venv hold exactly
the manifest, so pointed at somebody's own environment it uninstalls their
work and the engine with it — `sync()` refuses outright and `reconcile()`
returns before it can be called at startup, which is where that would have
happened first. The Modules screen lists what is installed and drops its
editor; `pip` is how that environment changes.
`fluksio serve` now names the interpreter node code runs on, which is the
thing a data scientist most needs to know at that moment. `fluksio-worker`
already defaulted `--python` to its own interpreter, so a GPU box works the
same way — that was only ever undocumented.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012ue1tkFWB1bcGy3aWhCKpU
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>
The worker script is handed to the interpreter by path, so app/flow was
sys.path[0] for every node: `import queue` got the engine's. It now drops
its own directory before anything else imports, and runs with the
deployment's credentials scrubbed out of its environment.
Also: reload builds off the event loop, the pool wakes what is blocked on
it when it stops, a refused metrics flush is kept for the next one rather
than dropped, and the cascade events are paired through failures.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017MeiWk3Yq12n2pTvnQWYvt
User code no longer execs in the engine. A pool of persistent worker
subprocesses speaks one JSON object per line; the controller installs a
proxy as the node's function, so every execution path funnels through it
and the pipeline is untouched. A crash costs one subprocess, a per-node
timeout is a kill, and cancelling from the canvas is that same kill.
The workers run a venv of the user's own on the data volume, filled from
a pip manifest versioned beside the flows. Applying it retires the
workers and rebuilds, so a package lands without restarting the engine.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017MeiWk3Yq12n2pTvnQWYvt
stroblmeandClaude Fable 5