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  stroblmeandClaude Opus 5
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4479eeb726 |
Follow a record into its fields, name the metrics, name the version
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Three things the first export pass got wrong for a real study. **Dotted paths.** A node returns a record, not a scalar — the numbers arrive inside `final_metrics` — so `--metrics final_metrics.train_loss` yielded an empty column and `--metrics final_metrics` yielded the whole record in one cell. Both sides of the wide table now take dotted paths, and the defaults reach the same depth: every number a result carries is a column named by its path, and inputs are compared leaf by leaf, so two configurations differing in one field give that field as the axis rather than two blobs that are merely not equal. Lists stay whole — a curve belongs in the long table. **`--list`.** Metric names are flow-qualified, so `--name train_loss` matched nothing and said only that. `fluksio export metrics --list` prints the names the selection carries, and an empty export made with `--name` points at it. **A version to compare.** The CLI ships ahead of the engine and a stale one answered a flat 404 with nothing anywhere in the API to tell how old it was. The engine reports `version` on `/observability/summary`, `fluksio status` prints it, and a 404 from export now names both versions — or says "older" when the field itself predates the engine. Bumped to 0.1.5, which is what makes the number worth reading. Also formats `flow/metrics.py`, which had been committed unformatted and was the last `ruff format --check` failure. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A9Hdrmf2cwNABCnE5x9UJa |
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stroblmeandClaude Opus 5
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37a7df9d24 |
Let a sweep run more than four at a time, and name the run a failure was in
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Concurrent runs sat at 4 whatever FLOW_MAX_CASCADES said: that setting bounds cascades, and the run drivers read a hardcoded MAX_PARALLEL nobody could reach. FLOW_MAX_RUNS is the knob they read now, --max-runs/--max-cascades/--max-workers are the same three as flags on serve, and the engine says which numbers it started with — which is the only way to tell that a settings file was read. Events keep the run they happened in. The payload always carried it and the persist path dropped it, so reading one run's failures meant filtering the engine-wide list; a batch run's id reaches those events now too, since a run has no journaled item to name itself by. Also: a provisioner's 0 means "no deadline" rather than "cancel on the next reconcile", and a command that reaches no engine says how to start one. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015sbYeYaVgYQqm1sbx7wPdL |
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stroblmeandClaude Opus 5
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40f8ad378d |
Ask a cluster for a machine when nothing here will do
Slurm is not a machine that attaches and stays; it is a queue somebody else owns. So nothing here submits a node to it. It submits a job whose payload is an ordinary worker dialling back in, and everything downstream — the protocol, the artifacts, cancellation, the books — already worked and did not have to learn what Slurm is. The alternative, which Covalent takes, is to stage a serialized call and a runner onto the login node, poll squeue and copy the result back: a second way of running a node beside the one that exists. The cost of not doing that is one assumption, that a compute node can open a connection outward. Where that is false, _payload is the single method a staged variant would replace. Clusters are configured in provisioners.json beside the alerts, since this is infrastructure an operator writes rather than anything a flow says. The script is generated with the system ssh and no new dependency, and prerun owns the environment — deliberately no pip install, because what is on a cluster is somebody's decision. One outstanding request per profile, cancelled if it never attaches and on the way out. Nothing autoscales. The run gate needed the same hook: a run held before it starts never reaches the placer's own wait, so it would have queued forever on a machine nothing had asked for. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6HeySA27EkGANZN95QySW |
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stroblmeandClaude Opus 5
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6ff56533f5 |
Schedule a node across every machine, not just this one
The engine answered "where does this node run" twice, in two ways that could not see each other: a device sent it to a worker carrying that label, and resources were counted against the engine's own cores. Declaring both meant the second answer won and nothing was counted at all — which the data-science getting-started page and the worked example both do. One question now, in flow/placement.py: of every machine attached, which could grant what this node asked for, and which of those has it free. The books move onto each machine — one accountant per worker, built from the inventory it reported — and the waiting moves above them, where one condition variable can be woken by a release anywhere or by a worker attaching. Locks go one way: placer, then a machine's books, never back. So a node asking for a card now finds the box that has one, rather than being clamped down to none and run here. When nothing can grant the ask at all it is still cut down and run — a flow written on a cluster has to work on a laptop — but the ceiling is one real machine now, since taking the largest of each dimension separately can describe a machine nobody has. Two things fixed on the way. A device on a connector node held every batch run of its flow forever, waiting for a worker that could never run an entry point. And `prefer` falling back to the engine skipped the books, so the fallback held nothing. The bench flow's node has taken a `params` argument that with_settings has not forwarded for some time, so the benchmark could not run at all: 62 ms median submit-to-result with this, against the 61 ms on record. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01A6HeySA27EkGANZN95QySW |
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stroblmeandClaude Opus 5
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0ffcabfdb9 |
Media dtypes: image, audio and video as narrowed artifact references
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A port may now declare `image`, `audio` or `video`. Each is the artifact
reference the engine already had, narrowed by the `media_type` on it, so a
speech recogniser declares what it eats rather than taking any bytes at all and
finding out. Bytes still never travel as a message and nothing on the wire
stops being JSON: a camera publishes one reference per frame, a microphone one
per chunk, and a reference may carry a `meta` dict nothing here interprets.
Streaming media is therefore an ordinary streaming port — with one change to
what that means. An emission used to journal an item with no payload, so
downstream read whatever was current when the item was claimed; a consumer
slower than its producer saw only the newest chunk and the ones between were
lost. That is right for a training curve and wrong for a second of speech, so
an emission now journals a `kind="emission"` item carrying its values, and the
executor hands them to the nodes reading that message instead of writing them
to state again. The value in state stays the latest, which is what everything
else reads, and the wave is filtered by what actually changed rather than
walking everything reachable. No queue serialization change — the existing
`outputs` field carries it.
Continuous media makes the store's missing GC a real problem, so this closes
it: `sweep_artifacts` runs hourly, keeps every digest a `run_artifact` row
records or a live message holds, spares anything written in the last hour, and
stands aside entirely while a run is in flight, since a node may store a
checkpoint long before it returns the reference to it. That also collects the
orphans a deleted flow has always left behind. `ARTIFACT_GC_INTERVAL_S=0` turns
it off.
Around the edges: `GET /artifacts/{digest}` serves the media type the caller
passes and answers ranged requests, so a browser plays a clip rather than
downloading it; `PUT` spools to disk instead of holding the whole body in
memory, as does `save_artifact` given a path; a Media widget draws whatever its
message points at, and a wall panel may fetch the bytes its own tiles are
showing and nothing else; and a connector gets `save_artifact`, for a device
whose readings are bytes.
What this cannot do is live video: a frame every second or two is a glance, and
the honest answer above that is the camera's own stream, which the widget takes
as a URL and the browser plays from source.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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stroblmeandClaude Opus 5
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608d30d884 |
Let a node say how much of the machine it takes
Five concurrent training nodes, each sizing its thread pool to every core,
left the engine's own event loop unscheduled: the API stopped answering
within 10 s and every client died. The same shape on a GPU deadlocked a run
for 21 minutes at 0% utilisation with nothing failing and nothing to read --
it just sat in `running`.
@node(resources={"cpus": 2}) is the declaration. The engine holds that much
for the length of the execution, so more of them than the machine has room
for wait their turn rather than oversubscribing it, and a `gpus` node holds
its card exclusively. FLOW_CPUS defaults to every core but two, and those two
are what keeps the engine answering.
Because a thread cap is read when the process imports the library, a warm
worker cannot be told a different one -- so an environment gets a pool of its
own and nodes deriving the same one share it, rather than paying a cold start
per call on exactly the nodes whose imports are slowest. XLA_FLAGS is never
derived: it is a composed, version-dependent string, so it travels in
resources.env where it is visible.
A node that declares nothing is not accounted for and behaves as it always
did -- it just gets FLOW_CPUS/FLOW_MAX_WORKERS as a thread cap, which is the
half of this that fixes the reported incident without anybody declaring
anything. An operator who set OMP_NUM_THREADS themselves still wins.
Resources are claimed strictly before a worker slot, so the two blocking
waits cannot deadlock. A node queued for them publishes node_queued and shows
on GET /workers/resources, because waiting and hanging looked identical.
Accounted, not enforced: no cgroups, no rlimits. Scheduling across machines,
flavours and enforcement are the next steps.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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stroblmeandClaude Opus 5
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180da3d640 |
Stop paying five Redis round trips and a global lock per message
The engine was I/O-bound on its own state backend. `RedisState.lock()` is one key — `pipeline:_lock` — for the whole process, taken five times a message at two round trips each, and every cascade and every node read queued behind it. Inside it, reading a node's inputs was three round trips per input (an EXISTS for `in`, then EXISTS and GET for the value), writing was two updates that a single transaction already gives, and the version counters went one INCR at a time. Replaced with the atomic command that was always available: `get_present` is one MGET and tells a missing key from one holding null, so the lock it used to be read under bought nothing; value and timestamp land in one `update`, which is a MULTI/EXEC; `increment_multi` pipelines the counters. `values()` — what every websocket snapshot calls — is two reads whatever the message count instead of two per message. Beside that: every webhook did its blocking XADD on the asyncio event loop (MQTT already used `to_thread`); the per-execution `NodeOutcome` was built and validated even with no run watching; `_minute` built a tz-aware datetime per event on the loop thread to key a dict, and now keys on an int; `move_due` promoted delayed items one round trip each, every second; `FLOW_MAX_CASCADES` makes the in-flight ceiling a setting rather than a constant. `orjson` replaces stdlib json where a message pays for it — state, the journal, the engine side of the worker pipe. `fluksio-worker` stays dependency-free, and the run-cache digest stays on stdlib so no stored key is invalidated. A non-finite number now stores as `null` rather than the bare `NaN` that was never JSON. Measured with `scripts/bench_engine.py` against a real Redis, 200 messages: a five-node chain went from 43.9 to 103.1 msg/s with p50 latency 2110ms → 782ms and p95 3913ms → 1439ms; one source into twenty consumers went from 5.4 to 33.7 msg/s. In memory, twenty consumers went from 187 to 448 msg/s. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BpfSinyCBfjuieikyfMPbf |
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stroblmeandClaude Opus 5
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60757fa7fa |
Style the engine's own logs, notice enrolment while serving, say more in status
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Four things from a testing pass. `fluksio serve` printed its own lines through the root logger, which has no handler and falls back to `INFO:fluksio.cloud.connector:...` — beside uvicorn's aligned output it reads like something went wrong. The engine's loggers and alembic's now use uvicorn's own handler. Named rather than configuring the root: httpx logs every portal call at INFO and none of that is printed today. `fluksio enroll` writes its config from another process, so an engine already serving never learned it had been paired. It now looks for one every few seconds and dials when it appears. `load()` rather than `exists()`, or a file that does not parse would be restarted forever. `fluksio status` says where the installation stands with its portal — never paired, linked, or paired and unreachable, which is the one worth acting on. `--seed` and `--timeout` had no help text at all. Both say what they are for now, and the docs say what a seed is actually for: recorded on the run, part of its input digest, and passed to an input named `seed` when the flow declares one, so the number a run is labelled with is the one the code drew from. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_019V5bsYGNxcgPs4xXmTPx69 |
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stroblmeandClaude Opus 5
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400d7d9c5c |
Stage caching for batch runs, and an engine that lives in the command
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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> |
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stroblmeandClaude Opus 5
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c81d6cb21a |
Bound the pipeline teardown so a stuck node cannot wedge the controller
A node's stop() and a supervised task's cancellation are both waited on inside the rebuild lock, and neither had a deadline: an MQTT client whose broker never acknowledges the disconnect leaves aiomqtt's __aexit__ waiting forever, so reload() never returned and every start, stop or publish behind it hung until the container was restarted. Each node now gets five seconds to close and is abandoned after that, and cancel_all reports what is still running rather than waiting on it — it also no longer swallows a cancellation aimed at the caller, which used to make the lock holder unkillable. A rebuild asked for by a request gives up on the lock after fifteen seconds with RebuildBusy, answered as a 503. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01StpRc2C6au1WJ1EUU7fsfu |
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stroblmeandClaude Opus 5
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10b0ba9e49 |
Add the fluksio CLI: serve, enroll, worker
`pip install fluksio && fluksio serve` on a machine with no Docker, no database and no configuration — which is the case this is for: a node on a cluster where ports cannot be opened. It makes its data directory, its key and an admin account, prints the password once, and serves. Pairing is `fluksio enroll <code> --portal …`, doing what the Settings screen does through the same function, before the engine starts and without one running — a machine nobody can route to has no browser pointed at it either. The portal serves the dashboard, so nothing is served here. Two things had to give way. `fastapi[standard]` pulls a cloud CLI that wants sentry-sdk 2.x while we pinned below it — no pip resolution existed, so the pin is lifted, which the comment beside it had been waiting for and which also lets the Python cap go. And `uv` is now a dependency rather than something to find on PATH: the Modules screen is how a data scientist installs torch, and it was quietly falling back to the engine's own interpreter. The CLI imports nothing from the engine before it has set DATA_DIR — the settings are built on the first import of core.config, and reaching it early put the database in the working directory. There is a test for that now, because the failure is silent. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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stroblmeandClaude Opus 5
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961a8f881d |
Keep the engine's state in SQLite, not Postgres
One process owns this database — the image has run a single uvicorn worker for that reason since the four-engines bug — so a file beside the flows is the honest shape for it, and it is what lets `fluksio serve` need no infrastructure at all. Live values, node execution and the work queue never came here anyway; what does is a rollup a minute at a time, a row per cascade and the run history, and WAL keeps the readers going while that one writer works. DATA_DIR is now the one setting that moves everything an installation keeps; the rest derive from it and the images still spell theirs out. The schema is prepared in-process at startup, so the prestart service is gone, and the ten Postgres-only revisions collapse into one portable baseline. Three things only worked because psycopg was casting for us: a token's subject arriving as a string where the column is a UUID, `greatest`, and `date_bin`. The timestamps needed a column type of their own — SQLite stores no offset, and a naive datetime read back either raises against an aware `now` or serialises as local time. Postgres stays in the stack only for Umami, behind the analytics profile. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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stroblmeandClaude Opus 5
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2c369ac75f |
Split the worker into a distribution of its own
A cluster or GPU host installs `pip install fluksio-worker` and gets the agent and the runner, not psycopg, numpy and the MCP SDK. The engine depends on it as a workspace member, so the file it launches node code with is the same file a remote worker runs — which is what keeps a node unable to tell the difference. Copying the two files by hand still works. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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stroblmeandClaude Opus 5
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60d7ec81c0 |
Rename the import package app to fluksio
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> |