stroblmeandClaude Fable 5
a38e2745eb
A data scientist keeps their code where it is and decorates it: `@node` declares a function's ports beside the function, `Flow(name, nodes=[...])` says which of them make a flow, and `use(fn, wire=..., **settings)` rebinds one for a single flow. `fluksio sync` uploads the document plus a generated import shim per node, so the store still holds a complete, runnable, git-versioned definition while the code it imports stays theirs. `fluksio login|run|runs` and `flow.submit().wait()` are the client half, over the run endpoints that already existed. Runs record the user repository's commit beside the store's, so "what code produced this number" is answerable on the side that now holds the code. - `fluksio/sdk/`: ports, decorators, the flow builder and its checks, the shim generator, an HTTP client and sync. Standard library only at import, so `from fluksio import node` in a training script pulls in no engine. - `FlowDef.origin` marks a flow code-defined; `Run.origin_commit` carries the repository's commit; `POST /modules/refresh` retires the workers without an install, which every sync calls — a worker holds the imported package in memory, so an edit to it is invisible until the process goes. - The canvas shows a generated body read-only and names the repository to edit instead; a body edited there stops the next sync rather than being discarded. - The worker's reporter carries inert `Port`, `node`, `use` and `Flow`, since the shim imports a module whose first line declares them. - `examples/myresearch` is the worked example, `make sync-example` uploads it. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012ue1tkFWB1bcGy3aWhCKpU
718 lines
28 KiB
Python
718 lines
28 KiB
Python
"""Runs: a flow taken from its inputs to its outputs, once.
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A cascade is what an always-on flow does when a value arrives — it has no
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beginning and no end worth recording. A *run* is the other shape the same
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engine can take: parameters go in, the graph executes until it drains, and
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what it produced is kept. That is what an ML experiment is, and what a
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CI-style job is, so both are this one entity.
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Three things make a run different from a cascade, and each is deliberate:
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* **Its own state.** Message names are global keys, so two runs of one flow
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sharing the engine's state would overwrite each other's values. A run gets a
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state backend namespaced to itself, which is a constructor argument rather
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than a change to the pipeline — every key the engine keeps, versions and
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node memory included, already goes through that backend.
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* **Its own record.** The event bus drops what it cannot keep up with, which
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is right for a live canvas and wrong for a result. The driver writes the
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run's rows itself, from the thread that is running it.
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* **Its own durability.** The queue wakes an engine up; from the moment a run
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starts, its database row is the truth. Redelivering hours of training
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because an acknowledgement was late is not recovery, so a run is
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acknowledged as soon as it is claimed and a stale lease — not an unacked
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stream entry — is what marks a run its engine died in the middle of.
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"""
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from __future__ import annotations
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import hashlib
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import json
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import logging
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import os
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import socket
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import threading
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import time
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import uuid
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from collections.abc import Callable
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from concurrent.futures import ThreadPoolExecutor
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from datetime import datetime, timedelta, timezone
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from typing import Any
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from sqlalchemy import update
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from sqlalchemy.dialects.sqlite import insert as upsert
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from sqlmodel import Session, col, select
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from fluksio.core.db import engine as db_engine
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from fluksio.flow.controller import FlowController, RunContext
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from fluksio.flow.messages import qualify
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from fluksio.flow.pipeline import NodeOutcome, Pipeline
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from fluksio.flow.queue import WorkItem, WorkQueue
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from fluksio.flow.schemas import FlowDef
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from fluksio.flow.state import MemoryState, StateBackend
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from fluksio.models import Run, RunArtifact, RunMetric, RunNode
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logger = logging.getLogger(__name__)
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#: How long a finished run's state is kept before Redis drops it. Long enough
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#: to look at what a failed run left behind, short enough not to accumulate.
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RUN_STATE_TTL = 24 * 3600
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#: How often a running run says it is still alive.
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LEASE_INTERVAL_S = 20.0
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#: A lease older than this belongs to an engine that is not coming back.
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LEASE_STALE_S = 90.0
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#: How often stale leases are looked for.
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SWEEP_INTERVAL_S = 30.0
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#: Runs driven at once. Node bodies are bounded by the worker pool anyway;
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#: this only bounds how many graphs are in flight.
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MAX_PARALLEL = 4
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CLAIM_COUNT = 4
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CLAIM_BLOCK_MS = 1000
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ERROR_CAP = 2000
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LOG_CAP = 8000
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#: Reported numbers held before they are written. A training loop reporting
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#: every step must not be a round trip every step.
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METRIC_BATCH = 500
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METRIC_FLUSH_S = 2.0
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#: How often a run waiting for a worker looks again.
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WAIT_RETRY_S = 15.0
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#: Where a run's state lives, so it can never collide with the engine's own.
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RUN_NAMESPACE = "run"
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class RunRejected(ValueError):
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"""The run cannot be made: bad parameters, or a flow that cannot batch."""
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def new_run_id() -> str:
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"""Time-ordered, so the newest runs sort last without reading a column."""
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return f"{int(time.time() * 1000):013d}-{uuid.uuid4().hex[:8]}"
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def digest_of(params: dict[str, Any], seed: int | None) -> str:
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"""What identifies a run's inputs: same parameters, same digest."""
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canonical = json.dumps(
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{"params": params, "seed": seed}, sort_keys=True, separators=(",", ":")
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)
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return hashlib.sha256(canonical.encode()).hexdigest()
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def batch_issues(flow: FlowDef) -> list[str]:
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"""Why this flow cannot be run as a batch, if it cannot.
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One thing genuinely breaks: a port with a discretization interval holds
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values back for a timer to release, and a run has no timer — the engine
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would drop them instead of delaying them. On a *streaming* port that is
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exactly right and is how you keep a chart from being flooded: the run's
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history keeps every value, and the interval only thins what is published.
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Anywhere else it is a message quietly going missing.
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A delay node is fine; without a queue to defer into it simply sleeps,
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which in a run is what was asked for.
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"""
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issues: list[str] = []
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for node in flow.nodes:
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for spec in list(node.requires) + list(node.provides):
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if spec.interval > 0 and not spec.stream:
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issues.append(
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f"Node '{node.id}' rate-limits '{spec.port or spec.name}'. "
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"A run has no timer to release what that holds back, so "
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"the value would be dropped. Remove the interval, or mark "
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"the port as streaming if it is a curve being thinned out."
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)
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return issues
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def required_labels(flow: FlowDef) -> list[str]:
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"""Worker labels this flow cannot run without.
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Only the nodes that *require* their device. One that merely prefers it runs
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on the engine when no such worker is attached, so holding the whole run
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back to wait for one would be waiting for something it does not need.
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"""
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return sorted(
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{
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node.device
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for node in flow.nodes
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if node.device and node.device.strip() and node.device_policy == "require"
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}
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)
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def seed_values(
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flow: FlowDef, params: dict[str, Any], seed: int | None = None
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) -> dict[str, Any]:
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"""Turn a run's parameters into the messages the flow starts from.
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A run's ``seed`` fills an input of that name when the flow declares one, so
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the field that distinguishes two runs of one configuration is the same
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number the flow draws from — otherwise it would only look like it was, and
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a sweep over seeds would run the same experiment N times. An explicit
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parameter still wins, and a flow that declares no ``seed`` ignores it.
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"""
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specs = {declared.spec.name: declared.spec for declared in flow.inputs}
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if seed is not None and "seed" in specs and "seed" not in params:
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params = {**params, "seed": seed}
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values: dict[str, Any] = {}
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for key, value in params.items():
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spec = specs.get(key)
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if spec is None:
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known = ", ".join(sorted(specs)) or "none"
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raise RunRejected(
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f"'{key}' is not an input of flow '{flow.name}' (it declares: {known})"
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)
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try:
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spec.check(value)
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except TypeError as exc:
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raise RunRejected(f"Parameter '{key}': {exc}") from exc
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values[qualify(flow.name, spec.name)] = value
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return values
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def collect_result(flow: FlowDef, state: StateBackend) -> dict[str, Any]:
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"""What the run produced, keyed by message name without the flow prefix."""
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prefix = f"{flow.name}."
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if flow.outputs:
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names = [qualify(flow.name, name) for name in flow.outputs]
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else:
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# Everything the flow ended up holding. The engine's own bookkeeping is
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# keyed by `__thing__:message`, so it never starts with the flow name.
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names = sorted(key for key in state.keys() if key.startswith(prefix))
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result: dict[str, Any] = {}
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for name in names:
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if name in state:
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result[name[len(prefix) :] if name.startswith(prefix) else name] = state[
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name
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]
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return result
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class MetricSink:
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"""Keeps the series a run's streaming outputs traced out.
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A run's metrics are not logged; they are the numbers its nodes published on
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the way to finishing. This watches the emissions, keeps the numeric ones,
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and writes them in batches — synchronously rather than over the event bus,
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which drops what it cannot keep up with, and a training curve with holes in
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it is not a result.
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The step is the count of emissions on that message. A node that publishes
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every tenth training step therefore has steps 0, 1, 2 rather than 0, 10,
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20 — a faithful x-axis of its own emissions, not of the loop inside it.
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"""
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def __init__(
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self,
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run_id: str,
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batch: int = METRIC_BATCH,
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interval: float = METRIC_FLUSH_S,
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) -> None:
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self.run_id = run_id
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self._batch = batch
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self._interval = interval
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self._rows: dict[tuple[str, int], RunMetric] = {}
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self._steps: dict[str, int] = {}
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self._last_flush = time.monotonic()
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self._lock = threading.Lock()
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def handle(self, node_id: str, outputs: dict[str, Any]) -> None:
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"""One emission: every number in it belongs to this run's history."""
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now = time.time()
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rows: list[RunMetric] = []
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with self._lock:
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for name, value in outputs.items():
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if not isinstance(value, (int, float)) or isinstance(value, bool):
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# A checkpoint or a record is on the run some other way —
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# as an artifact, or as its result. Only numbers are series.
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continue
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step = self._steps.get(name, -1) + 1
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self._steps[name] = step
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row = RunMetric(
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run_id=self.run_id,
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name=name[:128],
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step=step,
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node=node_id[:255],
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ts=now,
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value=float(value),
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)
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self._rows[(row.name, row.step)] = row
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due = (
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len(self._rows) >= self._batch
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or time.monotonic() - self._last_flush >= self._interval
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)
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if due:
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rows = list(self._rows.values())
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self._rows.clear()
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self._last_flush = time.monotonic()
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if rows:
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self._write(rows)
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def flush(self) -> None:
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with self._lock:
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rows = list(self._rows.values())
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self._rows.clear()
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self._last_flush = time.monotonic()
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if rows:
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self._write(rows)
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def _write(self, rows: list[RunMetric]) -> None:
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try:
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with Session(db_engine) as session:
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statement = upsert(RunMetric).values([row.model_dump() for row in rows])
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session.exec(
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statement.on_conflict_do_update(
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index_elements=["run_id", "name", "step"],
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set_={
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"value": statement.excluded.value,
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"ts": statement.excluded.ts,
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"node": statement.excluded.node,
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},
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)
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)
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session.commit()
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except Exception:
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logger.exception(
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"Could not write %d metric(s) of %s", len(rows), self.run_id
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)
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def origin_commit(flow: FlowDef) -> str:
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"""The user repository's commit, for a flow declared in code elsewhere.
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Marked dirty when the tree had uncommitted changes at sync, because then
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the hash names something other than what actually ran.
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"""
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origin = flow.origin
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if origin is None or not origin.commit:
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return ""
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return f"{origin.commit}-dirty" if origin.dirty else origin.commit
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class RunService:
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"""Accepts runs, drives them, and writes down what they did."""
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def __init__(
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self,
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controller: FlowController,
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queue: WorkQueue,
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state_factory: Callable[[str], StateBackend] | None = None,
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parallel: int = MAX_PARALLEL,
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) -> None:
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self.controller = controller
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self.queue = queue
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# Without one, a run gets a private in-memory state — which is exactly
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# the isolation it wants, minus surviving the process.
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self._state_factory = state_factory or (lambda _ns: MemoryState())
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self.engine_name = f"{socket.gethostname()}-{os.getpid()}"[:64]
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self._pool = ThreadPoolExecutor(max_workers=parallel, thread_name_prefix="run")
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self._stop = threading.Event()
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self._consumer: threading.Thread | None = None
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self._keeper: threading.Thread | None = None
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# Runs this process is driving, and the pipeline each is running, so a
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# cancel has something to hold on to.
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self._active: dict[str, Pipeline] = {}
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self._cancelled: set[str] = set()
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self._lock = threading.Lock()
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# -------------------------------------------------------------------------
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# Lifecycle
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# -------------------------------------------------------------------------
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def start(self) -> None:
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if self._consumer is not None:
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return
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self._consumer = threading.Thread(
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target=self._consume, name="run-consumer", daemon=True
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)
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self._consumer.start()
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self._keeper = threading.Thread(
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target=self._keep_leases, name="run-leases", daemon=True
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)
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self._keeper.start()
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def stop(self) -> None:
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self._stop.set()
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for thread in (self._consumer, self._keeper):
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if thread is not None:
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thread.join(timeout=5)
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self._consumer = None
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self._keeper = None
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self._pool.shutdown(wait=False)
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self.queue.close()
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def alive(self) -> bool:
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return self._consumer is not None and self._consumer.is_alive()
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# -------------------------------------------------------------------------
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# Accepting work
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# -------------------------------------------------------------------------
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def submit(
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self,
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flow_name: str,
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params: dict[str, Any] | None = None,
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seed: int | None = None,
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group_id: str | None = None,
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cause: str = "api",
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actor: str = "",
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draft: bool = False,
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) -> Run:
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"""Journal a run and wake an engine up for it. Never blocks on it."""
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flow = self.controller.store.read_flow(flow_name, draft=draft)
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issues = batch_issues(flow)
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if issues:
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raise RunRejected(" ".join(issues))
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params = params or {}
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# Checked here rather than in the driver: a caller who mistyped a
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# parameter should be told now, not by a run that fails in a minute.
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seed_values(flow, params, seed)
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run = Run(
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id=new_run_id(),
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flow=flow.name,
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flow_version=flow.version,
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commit=self.controller.store.head(),
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origin_commit=origin_commit(flow),
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params=params,
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params_digest=digest_of(params, seed),
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seed=seed,
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group_id=group_id,
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cause=cause,
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status="queued",
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labels=required_labels(flow),
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created_at=datetime.now(timezone.utc),
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actor=actor,
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)
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with Session(db_engine) as session:
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session.add(run)
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session.commit()
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session.refresh(run)
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self.queue.add(WorkItem(kind="run", node="", flow=flow.name, run_id=run.id))
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return run
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def cancel(self, run_id: str) -> bool:
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"""Stop a run: kill what it is executing, schedule nothing further."""
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with self._lock:
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pipeline = self._active.get(run_id)
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if pipeline is None:
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# Not running here — if it is still queued, refusing to start
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# is all the cancelling it needs.
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cancelled = self._finish_queued(run_id)
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if cancelled:
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self._cancelled.add(run_id)
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return cancelled
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self._cancelled.add(run_id)
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# The gate first, so nothing new is submitted while the running nodes
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# are being killed; a gated node is never handed to the executor, so
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# the graph drains instead of going further.
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pipeline.pause(self._flow_of(run_id) or "")
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workers = self.controller.workers
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if workers is not None:
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# Keyed by run, so a sweep cancelling one config leaves the others
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# training.
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workers.cancel_run(run_id)
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if self.controller.remote is not None:
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self.controller.remote.cancel_run(run_id)
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return True
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def _flow_of(self, run_id: str) -> str | None:
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with Session(db_engine) as session:
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run = session.get(Run, run_id)
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return run.flow if run else None
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def _finish_queued(self, run_id: str) -> bool:
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with Session(db_engine) as session:
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result = session.exec(
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update(Run)
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.where(col(Run.id) == run_id, col(Run.status) == "queued")
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.values(
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status="cancelled",
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finished_at=datetime.now(timezone.utc),
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status_reason="Cancelled before it started",
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)
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)
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session.commit()
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return bool(result.rowcount)
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# -------------------------------------------------------------------------
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# Threads
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# -------------------------------------------------------------------------
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def _consume(self) -> None:
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failures = 0
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while not self._stop.is_set():
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try:
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# Runs put back to wait for a worker come due here. The claim
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# below blocks for a second, so this is about once a second.
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self.queue.move_due(time.time())
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items = self.queue.claim(CLAIM_COUNT, CLAIM_BLOCK_MS)
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failures = 0
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except Exception as exc:
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failures += 1
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logger.error("Could not claim runs: %s", exc)
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self._stop.wait(min(30.0, 2.0**failures))
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continue
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for item in items:
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if not item.run_id:
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self.queue.ack(item)
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continue
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missing = self._missing_labels(item.run_id)
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if missing:
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# Left in the queue rather than failed: submitting a run
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# before turning the GPU box on is a normal way to work, and
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# the run says what it is waiting for while it waits.
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self._waiting(item.run_id, missing)
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self._defer(item)
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continue
|
|
# Acknowledged before it runs: from here on the row is the
|
|
# record, and a lease that stops moving is what says otherwise.
|
|
self.queue.ack(item)
|
|
try:
|
|
self._pool.submit(self._drive, item.run_id)
|
|
except RuntimeError:
|
|
logger.warning("Run %s not started: shutting down", item.run_id)
|
|
|
|
def _missing_labels(self, run_id: str) -> list[str]:
|
|
"""Worker labels this run needs that nothing attached carries."""
|
|
with Session(db_engine) as session:
|
|
run = session.get(Run, run_id)
|
|
needed = list(run.labels) if run else []
|
|
if not needed:
|
|
return []
|
|
hub = self.controller.remote
|
|
available = hub.labels() | {w.name for w in hub.workers()} if hub else set()
|
|
# A node that only prefers its label runs locally instead, so it is not
|
|
# a reason to hold the run back; that is decided per node at call time.
|
|
return sorted(set(needed) - available)
|
|
|
|
def _waiting(self, run_id: str, missing: list[str]) -> None:
|
|
reason = f"Waiting for a worker labelled {', '.join(missing)}"
|
|
try:
|
|
with Session(db_engine) as session:
|
|
session.exec(
|
|
update(Run)
|
|
.where(col(Run.id) == run_id, col(Run.status) == "queued")
|
|
.values(status_reason=reason)
|
|
)
|
|
session.commit()
|
|
except Exception:
|
|
logger.exception("Could not record what run %s is waiting for", run_id)
|
|
|
|
def _defer(self, item: WorkItem) -> None:
|
|
"""Put an item back for later, and let go of this delivery."""
|
|
try:
|
|
self.queue.add_delayed(item, time.time() + WAIT_RETRY_S)
|
|
self.queue.ack(item)
|
|
except Exception:
|
|
logger.exception("Could not defer run %s", item.run_id)
|
|
|
|
def _keep_leases(self) -> None:
|
|
"""Say the local runs are alive, and clean up after engines that died."""
|
|
last_sweep = 0.0
|
|
while not self._stop.is_set():
|
|
self._stop.wait(LEASE_INTERVAL_S)
|
|
if self._stop.is_set():
|
|
break
|
|
with self._lock:
|
|
mine = list(self._active)
|
|
now = datetime.now(timezone.utc)
|
|
try:
|
|
if mine:
|
|
with Session(db_engine) as session:
|
|
session.exec(
|
|
update(Run)
|
|
.where(col(Run.id).in_(mine))
|
|
.values(lease_at=now)
|
|
)
|
|
session.commit()
|
|
if time.monotonic() - last_sweep >= SWEEP_INTERVAL_S:
|
|
last_sweep = time.monotonic()
|
|
self._sweep_abandoned(now)
|
|
except Exception:
|
|
logger.exception("Could not refresh run leases")
|
|
|
|
def _sweep_abandoned(self, now: datetime) -> None:
|
|
cutoff = now - timedelta(seconds=LEASE_STALE_S)
|
|
with Session(db_engine) as session:
|
|
result = session.exec(
|
|
update(Run)
|
|
.where(
|
|
col(Run.status) == "running",
|
|
col(Run.lease_at) < cutoff,
|
|
)
|
|
.values(
|
|
status="abandoned",
|
|
finished_at=now,
|
|
status_reason="The engine running it stopped reporting",
|
|
)
|
|
)
|
|
session.commit()
|
|
if result.rowcount:
|
|
logger.warning("Marked %d run(s) abandoned", result.rowcount)
|
|
|
|
# -------------------------------------------------------------------------
|
|
# Driving one run
|
|
# -------------------------------------------------------------------------
|
|
|
|
def _claim(self, run_id: str) -> Run | None:
|
|
"""Take the run, or leave it: whoever moves it out of `queued` owns it.
|
|
|
|
The compare-and-swap is what makes a redelivered item harmless — the
|
|
second engine to arrive updates nothing and walks away.
|
|
"""
|
|
now = datetime.now(timezone.utc)
|
|
with Session(db_engine) as session:
|
|
result = session.exec(
|
|
update(Run)
|
|
.where(col(Run.id) == run_id, col(Run.status) == "queued")
|
|
.values(
|
|
status="running",
|
|
started_at=now,
|
|
lease_at=now,
|
|
engine=self.engine_name,
|
|
)
|
|
)
|
|
session.commit()
|
|
if not result.rowcount:
|
|
return None
|
|
return session.exec(select(Run).where(col(Run.id) == run_id)).first()
|
|
|
|
def _drive(self, run_id: str) -> None:
|
|
run = self._claim(run_id)
|
|
if run is None:
|
|
return
|
|
started = time.perf_counter()
|
|
status = "ok"
|
|
reason = ""
|
|
result: dict[str, Any] = {}
|
|
state: StateBackend | None = None
|
|
errors = 0
|
|
|
|
def observe(outcome: NodeOutcome) -> None:
|
|
nonlocal errors
|
|
if not outcome.ok:
|
|
errors += 1
|
|
self._record_node(run_id, outcome)
|
|
|
|
sink = MetricSink(run_id)
|
|
try:
|
|
flow = self.controller.store.read_flow(run.flow)
|
|
state = self._state_factory(f"{RUN_NAMESPACE}:{run_id}")
|
|
pipeline = self.controller.build_run_pipeline(
|
|
flow,
|
|
state=state,
|
|
observer=observe,
|
|
emission_observer=sink.handle,
|
|
run=RunContext(run_id=run_id),
|
|
)
|
|
with self._lock:
|
|
self._active[run_id] = pipeline
|
|
if run_id in self._cancelled:
|
|
pipeline.pause(flow.name)
|
|
self._publish(run, "run_started")
|
|
pipeline.run(seed_values(flow, run.params, run.seed))
|
|
result = collect_result(flow, state)
|
|
with self._lock:
|
|
cancelled = run_id in self._cancelled
|
|
if cancelled:
|
|
status = "cancelled"
|
|
reason = "Cancelled while running"
|
|
elif errors:
|
|
status = "error"
|
|
reason = f"{errors} node(s) failed"
|
|
except Exception as exc:
|
|
logger.exception("Run %s failed", run_id)
|
|
status = "error"
|
|
reason = f"{type(exc).__name__}: {exc}"[:1024]
|
|
finally:
|
|
with self._lock:
|
|
self._active.pop(run_id, None)
|
|
self._cancelled.discard(run_id)
|
|
# Whatever the last batch was holding belongs to this run's record.
|
|
sink.flush()
|
|
duration = round((time.perf_counter() - started) * 1000, 2)
|
|
self._finish(run_id, status, reason, result, duration)
|
|
run.status = status
|
|
self._publish(run, "run_finished")
|
|
# Its values were only ever this run's; nothing reads them once it
|
|
# has a result. On Redis the namespace would expire anyway.
|
|
if state is not None and status != "error":
|
|
try:
|
|
state.clear()
|
|
except Exception:
|
|
logger.warning("Could not clear state of run %s", run_id)
|
|
|
|
def _record_node(self, run_id: str, outcome: NodeOutcome) -> None:
|
|
row = RunNode(
|
|
run_id=run_id,
|
|
node=outcome.node[:255],
|
|
status="ok" if outcome.ok else "error",
|
|
started_at=datetime.now(timezone.utc),
|
|
duration_ms=outcome.duration_ms,
|
|
error=outcome.error[:ERROR_CAP],
|
|
logs=outcome.logs[:LOG_CAP],
|
|
)
|
|
try:
|
|
with Session(db_engine) as session:
|
|
session.merge(row)
|
|
for message, ref in outcome.artifacts.items():
|
|
session.merge(
|
|
RunArtifact(
|
|
run_id=run_id,
|
|
name=message[:255],
|
|
node=outcome.node[:255],
|
|
digest=str(ref.get("digest") or "")[:71],
|
|
size=int(ref.get("size") or 0),
|
|
media_type=str(
|
|
ref.get("media_type") or "application/octet-stream"
|
|
)[:128],
|
|
)
|
|
)
|
|
session.commit()
|
|
except Exception:
|
|
logger.exception(
|
|
"Could not record node '%s' of run %s", outcome.node, run_id
|
|
)
|
|
|
|
def _finish(
|
|
self,
|
|
run_id: str,
|
|
status: str,
|
|
reason: str,
|
|
result: dict[str, Any],
|
|
duration_ms: float,
|
|
) -> None:
|
|
try:
|
|
with Session(db_engine) as session:
|
|
session.exec(
|
|
update(Run)
|
|
.where(col(Run.id) == run_id)
|
|
.values(
|
|
status=status,
|
|
status_reason=reason,
|
|
result=result,
|
|
duration_ms=duration_ms,
|
|
finished_at=datetime.now(timezone.utc),
|
|
)
|
|
)
|
|
session.commit()
|
|
except Exception:
|
|
logger.exception("Could not close run %s", run_id)
|
|
|
|
def _publish(self, run: Run, kind: str) -> None:
|
|
self._publish_event(
|
|
{
|
|
"type": kind,
|
|
"flow": run.flow,
|
|
"run": run.id,
|
|
"status": run.status,
|
|
"group": run.group_id or "",
|
|
"ts": time.time(),
|
|
}
|
|
)
|
|
|
|
def _publish_event(self, event: dict[str, Any]) -> None:
|
|
if self.controller.events is not None:
|
|
self.controller.events.publish(event)
|