A worker reported its labels and nothing about the machine behind them, so the
engine could route a node to a GPU box but not tell whether that box had a GPU
free. Inventory — cores, GPUs, memory — now arrives with the hello frame, and
the run frame carries back what the engine allocated for that call.
Which is protocol 2 on both ends. GPUs are never probed: asking a vendor tool
would make the one dependency two, so a GPU is what the batch job says it was
given or what --gpus says. A worker that reports nothing still attaches and is
scheduled by its label alone.
Two things a job scheduler needs: --max-idle stops a worker started for one job
rather than letting it hold its allocation to the walltime, and a refusal is now
fatal instead of a reconnect loop that reads as a hang in a job's log.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01A6HeySA27EkGANZN95QySW
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>
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
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
The gates have never gone green on the new runners. Three separate reasons:
- backend/Dockerfile shipped Python 3.10 while the code imports typing.Self
and datetime.UTC, so the container exited on import and the suite could not
even load its conftest. The image moves to 3.13 and the packages declare
>=3.12, which is the floor the tests actually pass on; ruff's target follows
and rewrites timezone.utc and asyncio.TimeoutError accordingly. Relocking
drops the 3.10 branch, which bumps FastAPI and so regenerates the SDK.
- frontend/README.md had no trailing newline and two dashboard widgets used
arbitrary text-[…] sizes. Both are em-relative on purpose, so they move to
the inline style the neighbouring ramp already uses.
- Every commit left its own run queued: without a concurrency group a runner
that was offline for a while works through a backlog nobody reads. A stack
that fails to come up now prints its logs before the teardown removes it.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
`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>
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>
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>
The engine now speaks MCP at /mcp, with a built-in OAuth 2.1 authorization
server in front of it: an agent registers itself, sends a human to the browser
to approve it, and exchanges the resulting code for a token. PKCE is required,
codes are single-use and stored only as hashes, the browser is redirected to
the URI that was registered rather than the one asked for, and refresh tokens
rotate so that replaying a spent one revokes the whole line.
Twenty tools cover reading, building, publishing and running flows, and each
one calls the same REST endpoint the dashboard calls, in-process, carrying the
caller's own token. That keeps one description of what a flow is and what may
be done to it — validation, the draft/publish split, the version check — and
means an agent can do nothing a person could not do in the browser.
Agent tokens are RS256 with a keypair of their own rather than the secret that
signs browser sessions, so deleting the key withdraws every agent without
logging anyone out, and deps.decode_token grew the branch that trusting a
second issuer will need when the hosted login arrives.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Makes the flow engine reachable from the API, which is what M3 needs before
any of it can reach the browser.
- app/flow is a package now; the prototype's watch-dir scripts and the
matplotlib/networkx visualiser are gone with their dependencies.
- Messages carry a serializable dtype instead of a live Python type, and a
port name, so the graph can speak qualified names while node functions keep
local arguments. Redis state is JSON, not pickle.
- Message names are namespaced per flow ("heating.temp"); a bare name resolves
to its own flow, a dotted one crosses flows.
- Several nodes may provide the same message: producers are a list, so fan-in
is a real edge instead of a silently dropped one.
- Flows are stored as flow.json plus node sources in a git repository, one
commit per save, with identical saves skipped so autosave stays quiet.
- Node failures are isolated and reported per node; validate() returns cycles
and unconnected inputs instead of raising deep in a run.
- Credentials live in an encrypted store and are referenced as {"$secret": …}.
- Engine events reach websocket clients through a bus, so values, node status
and execution show up live.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016WzrvW7rjQbynnhF6pxh6i
stroblmeandClaude Opus 5