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20
Commits
| Author | SHA1 | Message | Date | |
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  stroblmeandClaude Opus 5
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058f16ec1d |
Close eight open SDK tasks: the pidfile, the log, cards, names and a live curve
Each was a loose end recorded under `### SDK` in the notepad. `serve` takes its own pidfile down on SIGTERM. uvicorn restores the handler it found and re-raises the signal it stopped on, so the default handler ended the process without unwinding and the `finally` never ran — which is what a stop sends, and what left `serve.pid` behind. `serve.log` is cut back past 5 MB by the engine rather than by the screen that started it, so an adopted engine is bounded too. Gated on its own stdout being an appended regular file, which is what makes the cut safe: the kernel then puts the next write at the new end. Cards are counted from `/dev/nvidia[0-9]*`, so `FLOW_GPUS`/`--gpus` of 0 means "work it out" the way `FLOW_CPUS` always has. The engine counts, not the accountant — a remote worker builds one of those from its own inventory, and detecting there would hand it the engine host's cards. The worker counts last: what a batch job says it was granted still wins. `GET /runs/metrics/names` is the distinct over a selection that `--list` and the terminal's metric picker were approximating by reading the newest run that had measured anything, which missed a name only an older run ever wrote. `MetricSink` announces each batch it has written (`run_metric`, carrying the names). Not a per-point event: one covers up to 500 points or two seconds of them, and the rows stay the record. The terminal comparison fills in as the first readings land instead of staying blank until reopened, and the browser refetches the run and any comparison rather than the list behind them. `retry --group` pages the list route by `before` instead of stopping at 500. The terminal dashboard takes the terminal's colours (`ansi-dark`), and the web UI can re-pair from Settings without disconnecting first. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PRQ9bmTvCbqCwXo9mxZzzV |
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stroblmeandClaude Opus 5
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d471614e6a |
Push a frame instead of storing and fetching it
The rate the media dtypes could carry was one frame every second or two: each
was a file on the data volume, an event on the socket, and a request back for
the bytes. This closes both halves of that, and they are one feature.
`save_artifact(..., volatile=True)` writes to a `VolatileStore` — the same
content-addressed store, in `/dev/shm`, bounded by size with the oldest falling
out (`ARTIFACT_VOLATILE_BYTES`, 48 MB under the container's raised `shm_size`).
Nothing sweeps it: a frame nobody kept is not worth walking the store to find.
`ArtifactStore.path` falls through to it, which is what lets a volatile frame be
an ordinary reference everywhere else — the dtype check, a panel's digest scope,
`load_artifact` in a node, and the widget's own fetch all work on one unchanged.
`adopt` copies one into the store when a run records it, so "returned media is
kept, emitted media is not" stays true.
The bytes then go down the flows websocket as a length-prefixed binary frame,
sent just ahead of the `message_value` naming them, so a tile has the frame when
it hears the value moved. Nothing is pushed unasked: a client names the messages
it is drawing (`{"type":"media","names":[…]}`), a panel's list is intersected
with the scope it already had, and only the newest frame per name in a batch is
sent — a client that fell behind is not handed frames it would draw over. The
tunnel relays text only, so a screen reached through a portal falls back to
fetching, which is why the rate table now has two rows.
Around the edges: the remote worker's fetch cache is bounded at last
(`FLUKSIO_ARTIFACT_CACHE_BYTES`), since content addressing means nothing in it
ever expires and a media stream fills it with chunks nothing asks for twice; a
port carrying an image draws the frame in the node panel rather than only
saying `image/png · frame.png · 1.79kB`; and an edge chip says that much instead
of a line of hash. The media screenshot stops waiting for `networkidle` — a
camera is a socket that never goes quiet, which is the point of it.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01YC4u66vjzW54fnHu5Juhh9
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stroblmeandClaude Opus 5
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d01a8dad37 |
Rename Installation to Instance
Follows the portal: the noun is "instance" everywhere the app says it — UI strings, CLI output, error details, docs and comments. The wire keys (`instance_id`, `instance_token`) and the hub route this calls move with it. An existing cloud.json is adopted rather than refused: without the key alias the dataclass fails to parse, which the caller swallows and reads as "never enrolled" instead of "reconnect". `instance_key` on a node type becomes `target_key`. It means the outside thing a node points at, which is a different sense of the word, and keeping both would put two meanings of "instance" in one codebase. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015YrQnKV3bnQd4K342y8tKj |
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stroblmeandClaude Opus 5
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57eace2226 |
Bound what the API accepts, and close the holes the audit found
**SQLite is the database, and now says so.** `metric_minute` and every run
table are written with `sqlalchemy.dialects.sqlite.insert(...)
.on_conflict_do_update` and with `max(a, b)`, neither of which another
dialect has — so pointing `DATABASE_URL` at Postgres migrated cleanly,
served, logged in, and then lost every observability flush into the
collector's hold buffer and failed every run. It refuses at startup
instead. (The Postgres in the compose stack is Umami's; the engine's own
database has been a file beside the flows since 2026-08-21.)
**Every integer query parameter is bounded.** The caps were written as
`min(limit, 500)`, which a negative walks straight through — `?limit=-1`
compiles to `LIMIT -1` and SQLite returns the whole table. Ten signatures,
now `Query(ge=…, le=…)`. `hours=0` still means an hour, which
`_window_hours` was already deliberate about.
**Exports are capped at 10 000 runs** and say so with `X-Truncated`. The
filters bounded a sensible request and nothing bounded an unfiltered one,
which read every row into memory before a byte was streamed. `_series`
resolves cached curves in two queries rather than a `Run` lookup and a
`RunMetric` query per restored node — a comparison of twenty runs was
calling that twenty times over.
**`PUT /artifacts` has a size limit** (`MAX_ARTIFACT_BYTES`, 2 GiB, 0 to
disable), checked against `Content-Length` and again against the stream for
a chunked body, and its writes moved off the event loop.
**`/observability/timeseries` takes `since`/`until`**, the same window
`/runs` and `/events` take, capped at 2000 points — `hours=720&bucket_s=60`
was 43 200 of them in one array. It is also what a dragged chart needs to
re-fetch at its own resolution rather than magnifying buckets it has.
**Composite indexes** for the three list screens: `run(flow, created_at)`
and `(status, created_at)`, `flow_run(flow, started_at)`,
`engine_event(type, ts)`. Every index was single-column, so SQLite picked
one and sorted the rest by hand. Verified against a copy of a live database
(250k `flow_run` rows): the planner takes all four.
**Redis clients have socket timeouts.** A Redis that stops answering
without closing the connection hung the caller until the kernel gave up —
including `/utils/health/`, whose job is to notice.
**The panels file is written under one lock.** `save_panels` and
`unpair_panel` are both read-modify-write, and a save that read before an
unpair wrote put the old nonce back — silently un-revoking a screen that
had just been unpaired. The nonce carry-forward was written to make that
impossible; the gap between its read and its write is where it happened.
**Startup releases what it acquired.** Everything past `event_bus.bind`
registers how to close itself and the `finally` walks that list backwards;
a failure part-way through used to reach none of the shutdown steps and
leave the worker pool's subprocesses and every background task behind —
under `--reload`, once per bad edit. `modules.reconcile` moved into the
background: `uv` gets five minutes twice over, the healthcheck allows
eighty seconds, and the autoheal restarted the container before it could
finish installing.
`delete_run` takes SQLite's write lock up front (`core.db.writing`) rather
than upgrading a deferred transaction and losing to whichever flush
committed in between. `modules.sync` is serialised — two applies mutated
one venv at once. The proxied-call and stream dicts are bounded, and a
reused id cancels its predecessor instead of dropping the reference.
Security, found in passing and small enough to fix here:
- **`/secrets/` required only a signed-in user.** The names alone say what
this installation talks to, and `PUT /{name}` takes any name, so any
account could overwrite the credential a flow authenticates with.
Superuser now — which `/search` already assumed and said so.
- **`POST /login/access-token` had no rate limit.** Argon2 is deliberately
expensive and the route is unauthenticated and runs in the shared
threadpool. Ten *failed* attempts per address per five minutes; a
successful sign-in spends nothing.
- **a password reset link worked repeatedly for 48 hours.** The token now
carries a digest of the password hash it was minted against, so it stops
verifying once it has set one. No table of spent tokens needed.
- **enrolment accepted `http://`**, sending the claim code and then this
installation's credential in clear. https, or a local address.
- the rate limiter read `request.client.host`, which behind Traefik is the
proxy — so every per-address limit was one global bucket and one caller
could lock out everyone. It reads the forwarded address, and its
bucket table is capped rather than growing one key per address forever.
- SMTP has a timeout and sends after the response, so an unreachable mail
host cannot pin a threadpool worker, and a reply's timing no longer says
whether the address exists.
Test suite: engine-written rows are cleared between modules. A `FlowRun`
left `running` by one module turned up in another's query. Per-test
rollback is not available here — the module-scoped `client` runs the real
lifespan and its collector and run service write through sessions of their
own — so this bounds it where the writes come from. Three consecutive
green runs, orders randomised.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M6hPWS6YEbT1P8LxhhFb2T
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stroblmeandClaude Opus 5
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da528340a9 |
Cut the round trips a message costs the engine
Measured with `make bench-engine` against a real Redis: 103.6 -> 164.4 messages a second on a five-node chain (p50 latency 2125 -> 1171 ms) and 34.8 -> 63.2 on a fan-out of twenty. Against the memory backend, which is what a pip install runs on, 262 -> 626. The two that bought most of it: - `StateBackend.record` puts a published value, its timestamp, its series and its version counter in one round trip. They were four calls building four pipelines, and a value crossing an edge pays them twice. A released rate-limit hold rides along instead of a DEL per port. - the readiness check reads a node's inputs and hands them to the node, rather than reading the triggering ones to count them and having the node read the same keys again a moment later. `apply_outputs` was a second copy of `_record_outputs` and is now the same code plus the event that distinguishes it. The rest, each small: - `_derive` builds a node-by-id map and a `consumes` index, so dispatching an item and publishing a value stop scanning every node in the installation. - `read_all` is memoised against the store revision — it sits on the publish path, so a dashboard slider was reading and validating every flow file per value. Same mechanism `_wiring` already uses. - the `message_value` source block is built once per node instead of per emission. - both timer threads ask the queue to promote only when something is actually due, which takes an idle engine from ~4 Redis round trips a second to one. - the shared httpx client is bounded (32 connections, one retry); its default pool is 100 with no per-host cap, so one slow endpoint could take it and every other sender node with it. - the MQTT and delay nodes no longer log a line per message at INFO. Robustness, in the same pass: - `MemoryWorkQueue._done` was a set nothing ever removed from — one entry per non-idempotent node per item, for the life of the process, in the default configuration. Capped, the way the Redis side expires its markers. - a saturated engine can claim from the due lane past the cascade limit. The capacity gate sits in front of the claim, so the due lane's priority — decided inside it — did not apply while every slot was held: a motor's stop was not behind the long nodes, it was unread. Only after a slot has genuinely failed to free for half a second, and briefly, so the backlog is not starved in turn. - `reclaim_stale` dispatches through that same gate. It could return sixty entries and push in-flight far past the limit the gate exists to hold. - a flow's nodes are stopped together rather than one after another. Each gets `NODE_STOP_TIMEOUT`, so a flow whose broker was unreachable took five seconds per node — long enough to outlast `REBUILD_WAIT` and 503 the deploy. - the worker pool and the HTTP client are closed on a thread, not on the event loop, and a run closes the state backend it built (on Redis, a client and a connection pool per run). - the five background tasks say something when they die. Each catches exceptions inside its loop, so one raised anywhere else left the engine serving with no metrics, no alerts or no artifact sweep, silently. `tests/flow/test_round_trips.py` counts the state operations one message costs — four, where it was about eleven — because none of the above would fail a behavioural test if it were undone. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01M6hPWS6YEbT1P8LxhhFb2T |
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stroblmeandClaude Opus 5
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22c682e505 |
Put the deployment-only dependencies behind a server extra
A data-science environment installing fluksio waited for lxml, aiohttp and the rest of a connector stack it has nothing to talk to. Outbound mail, error reporting and the MQTT and InfluxDB clients moved to `fluksio[server]`, which the image installs; each import is guarded and names the extra. `tenacity` had no import site at all and is gone. 23 fewer packages and the compiled ones among them — a bare `pip install fluksio` still serves, runs every python node, and registers the mqtt and influxdb node types, which only need the library when one is actually built. sentry-sdk arrives anyway underneath `fastapi[standard]`; what changed there is that nothing of ours requires it. The dev environment keeps every extra: the suite exercises the connectors and strict mypy checks their call sites. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_019Hra4ndWMCLU5F3KjUuVAc |
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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> |