Run node code on the venv Fluksio was installed into

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

worker/fluksio_worker/agent.py

@@ -4,7 +4,11 @@ Point it at the engine on the box with the GPU and it dials in::
 
     pip install fluksio-worker
     fluksio-worker --url wss://api.example.com/api/v1/workers/attach \\
-        --token "$FLUKSIO_WORKER_TOKEN" --labels gpu --python /opt/venv/bin/python
+        --token "$FLUKSIO_WORKER_TOKEN" --labels gpu
+
+Install it into the environment the training code already runs in and node
+code runs on that: ``--python`` defaults to the interpreter this was started
+with. Point it elsewhere only when the two are meant to differ.
 
 It connects *out*, so the engine needs no route back and nothing has to expose
 Redis. What it then does is what the engine's own worker pool does: hold a few
@@ -274,7 +278,10 @@ def main(argv: list[str] | None = None) -> int:
     parser.add_argument(
         "--python",
         default=sys.executable,
-        help="the interpreter node code runs on; point it at the venv with torch",
+        help=(
+            "the interpreter node code runs on (default: the one running this, "
+            "so installing into the venv with torch in it is enough)"
+        ),
     )
     parser.add_argument("--parallel", type=int, default=1)
     parser.add_argument("--artifact-url", default="")