An example to evaluate: a training run, its dashboard, and two bugs it found

make seed-demo builds demo_training — prepare on the engine, a GPU-bound
train, evaluate back here — and a panel that draws the loss curve while the
training is still going. It is the session's whole argument in one flow: batch
runs with parameters and a result, a generator yielding on a declared port
rather than logging, fluksio.emit from inside a callback, artifacts carrying
the dataset and the weights between machines, and a sweep whose configs are
isolated from each other. The train node prefers its label rather than
requiring it, so it runs before a GPU box exists and says which machine and
which numeric backend it actually used.

Building it turned up two real bugs. A run waited for a worker its flow only
*preferred*, because required_labels ignored device_policy — so the example
hung on a label it did not need. And a run's seed never reached the flow, so
sweeping over seeds ran the same experiment N times; it now fills an input of
that name when the flow declares one, which is what the field looked like it
did all along.

Pressing Run on a batch flow now submits a run rather than taking the old
non-durable path — that button is the first thing anyone evaluating will press,
and it was quietly doing something else.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD

Makefile

@@ -3,7 +3,7 @@
 # The workspace root delegates to these (see ../Makefile).
 
 .PHONY: dev-utils dev dev-local up down update install dev-backend dev-frontend \
-        generate-client seed-example test test-backend test-frontend soak bench-startup lint lint-backend \
+        generate-client seed-example seed-demo test test-backend test-frontend soak bench-startup lint lint-backend \
         lint-frontend umami clean help
 
 COMPOSE_ROOT := $(CURDIR)
@@ -94,6 +94,9 @@ generate-client:  ## Regenerate the frontend SDK from the backend's OpenAPI sche
 seed-example:  ## Seed the querying-chart example (needs a running stack + InfluxDB)
 	cd backend && uv run python ../scripts/seed_example_chart.py
 
+seed-demo:  ## Seed the training-run example: a batch flow and its dashboard
+	cd backend && uv run python ../scripts/seed_demo_training.py
+
 # ── Testing ───────────────────────────────────────────────────────
 
 test: test-backend test-frontend  ## Run all tests (backend + frontend)