The store answers the new-node template when nothing was ever written for a
node, so such a node ran — returning {} on every call, reporting active and
ok, and saying nothing anywhere. Unreachable through `fluksio sync`, which
writes every body before it publishes; the editor end was open.
A run of a flow holding one is now refused, and the flow carries a
missing_source issue so it is visible before anybody runs it. A draft is
exempt: a node being written legitimately has no published body yet.
The generated client is regenerated for the new issue code, which also
catches up the drift left by earlier backend work (resources, code_digest,
idempotency_key).
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
A cache key held qualified input names, so the same node reading the same
values through two flows keyed differently and only a node with no inputs
could ever hit across one. The fingerprint beside the key already says what
the node is, and it has been flow-agnostic since it moved ahead of
assign_flow — the names were the last thing tying an entry to one flow.
Inputs now reduce by the node's own name for them; a name belonging to
another flow keeps its prefix, since reading it is part of what the
execution is. Every stored entry misses once and is re-run.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The worker held each yield one behind, because the last one is the node's
result when the generator returns nothing of its own. Only the engine knows
what ports a node declared, so the check happened when the *next* yield
arrived — a pass late, which for a training loop is however long one epoch
takes.
The worker now sends every yield as it happens and returns whatever its
generator returned; EmitSink holds the last one back and decides at the end
of the call what it was. Old "emit" frames are still handled, so a remote
agent that has not been restarted keeps working.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
A cache hit still replays no emissions — those values were the story of an
execution that is not happening — but the run they were recorded in is now
written on the row (`run_node.cached_from`), and the metrics endpoints read the
series back from there. So a reused run answers `run.metrics("train.loss")`
with the same points the run that trained did, rather than looking like a run
that produced no numbers at all. Pointed at rather than copied: a sweep of 500
reusing one frozen node would otherwise duplicate its curve 500 times.
That needed the cross-flow restore fixed first. The cache key has no flow in
it while the stored outputs are named for the flow that produced them, so
`quick.prepare` getting a hit from `train` wrote `train.dataset` into `quick`'s
state and the next node was called without its argument. One rule now covers
both halves: `requalify` reads a name owned by one flow as the same name in
another, applied to the restored outputs, to the node id behind the pointer,
and to the series names on the way out. Reuse across flows is kept.
Also: `@run:<id>.<output>` and a bare `sha256:` digest resolve on every input,
not only artifacts. Chaining a run's json config into the next one from a shell
meant pasting the whole object inline, and the CLI could not even send the
spelling — `_coerce` died in `json.loads` before the engine saw it. Both
spellings are reserved on every input now, `str` included, and `_from_run`
returns whatever the run's result holds rather than only a reference.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Dp9L6gakMVro1K2C5zdtBE
Two halves of the same gap: the CLI could start work but not show you any.
`fluksio status` draws the home screen's top half in a terminal — health and
what is wrong with it, every flow with its state and node count, and the
recent runs and failures under them. `--watch` keeps it there. Rich does the
drawing; it was already installed under fastapi's own CLI, and is named now
because a command depends on it.
`fluksio run` with no parameters at a terminal asks for them, one line per
declared input with its declared value in brackets — so Enter through the lot
is what running the defaults looks like, and an artifact input takes the
`@run:` spelling the engine now resolves. A scripted run is untouched: passing
any parameter, or piping the command, skips the questions, as does --defaults.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019V5bsYGNxcgPs4xXmTPx69
Four things the python SDK turned up, each fixed where every client sees it.
A key no port declares is now an error rather than a silent drop, on the
return, the yield and the emit alike — the contract the docs already stated.
The SDK reads literal yields at sync time, so a typo fails before anything
runs, and an emission of one fails the call rather than being logged where
nobody looks.
NaN and infinity are refused at the port. JSON cannot spell either, so one
that travelled came back as a 500, a socket frame that stopped the canvas, or
a metric batch the database dropped whole.
An artifact input takes `@run:<id>.<output>` or a bare digest, resolved on the
engine — so the CLI, the run dialog and a python caller mean the same thing,
and a sweep can pass one at all.
Node timeouts are off by default. The clock measured silence, which a training
node is full of, and remote workers had already stopped enforcing it — their
heartbeat reset it. Now a heartbeat proves the agent rather than the node,
ninety seconds of nothing fails the call either way, and the engine touches
work it is still running so a long node is not redelivered at sixty seconds.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019V5bsYGNxcgPs4xXmTPx69
A code node in a batch run is now fingerprinted by its source, its raw
settings and the values it reads — an artifact input counting as its digest,
which is what the content addressing was always for. A run that finds the key
restores what the earlier one returned and skips the node, recorded as
`cached`. The run history is the cache: `run_node.outputs` beside the
`cache_key` the schema already had, no second store. On for code nodes, never
for the built-in and connector types that have side effects; off per node with
`@node(cache=False)` and per run with `--no-cache`.
Emissions are not replayed on a hit, so a cached training node returns its
result without redrawing its curve. Recorded in NOTEPAD.md with the two other
deliberate limits.
`fluksio run --local` boots the real app in the command's own process and
drives it through its ASGI interface behind the ordinary client, so a run no
longer needs a `serve` terminal beside it — same data directory, same history,
and the cache carries between the two. It always waits, because the engine it
starts lives exactly as long as the command.
Also: `fluksio sweep --param lr=0.1,0.01` for the product of the lists,
`run --follow` for a run's numbers as they arrive, Ctrl-C cancelling a waited
run rather than abandoning it, coloured statuses on a terminal, and `name`
made optional on the metrics endpoint so a follower can ask for every series.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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>
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
The first cut had node code call fluksio.log_metric, which was a second,
undeclared way for data to leave a node: invisible to validation, absent from
the canvas, and stored where the graph could not see it. That is precisely the
MLflow discrepancy this framework exists to avoid, so it is gone.
A node that produces values over time is a generator. Every yield is a dict
keyed by output port, published the instant it happens — same port, same type
check, same place on the canvas as any other value — and what it returns is
its result. A port doing this declares stream: true, and a run keeps every
number one takes, so experiment tracking is a consequence of the graph rather
than an API beside it: a chart binds to a training curve the way it binds to a
temperature. fluksio.emit writes the same ports imperatively, for where a
yield cannot reach — inside a training framework's callback.
In a live flow an emission also wakes what is downstream, as a subscriber
publishing does; in a run it does not, because a run's graph is scheduled once
and mid-node cascades would leave 'finished' with nothing to mean. The
enqueued item carries no payload: the value is already in state, and one
carrying it would re-apply an old emission after the node returned.
Verified on the stack: 30 loss values arrived live on the flow socket during a
run, attributed to the node that produced them, and the same node run on the
remote worker streamed its curve back across the socket.
Also caches remote compile results per worker, so attaching a GPU box does not
put a network round trip in every rebuild.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
A cascade has no end worth recording; a run does. Parameters go in, the graph
executes until it drains, and the result is kept — which is what an ML
experiment is and what a CI-style job is, so both are one entity.
Each run gets a state backend namespaced to itself, so two runs of one flow
cannot overwrite each other's messages; that is a constructor argument rather
than a change to the pipeline, because every key the engine keeps already goes
through the state backend. Its record is written by the driver thread rather
than folded off the event bus, which drops what it cannot keep up with. Its
own Redis stream wakes an engine up, and from the claim onwards the database
row is the truth: redelivering hours of training because an acknowledgement
was late is not recovery, so a stale lease is what marks a run whose engine
died.
Flows gain mode: batch, which are built and validated but never activated, and
nodes gain a device label for the worker that must run them.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AD8SfVhzXBG2nAfFcVh3iD
stroblmeandClaude Opus 5