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`--format jsonl` existed, but `_cell` ran json.dumps at row-build time, before a format was chosen — so a nested value was a string by then and jsonl only re-escaped it, leaving a consumer against csv's 128KB field limit either way. Stringifying moved to the csv writer, so csv is byte-identical and jsonl carries json. The runs TUI followed, or a record would draw as a Python repr. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01KYM38KSb4V4v2T71eifnZv
837 lines
31 KiB
Python
837 lines
31 KiB
Python
"""Runs over the API: submit one, watch it, read what it made.
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Submitting returns immediately with a queued run — a training run is measured
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in hours, so nothing here waits for one. The way to follow a run is to poll it
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or to listen on the flow socket, which carries its start and finish.
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"""
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import csv
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import io
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import json
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import time
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from collections.abc import Iterator
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from datetime import UTC, datetime
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from itertools import groupby
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from typing import Annotated, Any, Literal
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from fastapi import APIRouter, Depends, HTTPException, Query, Request, Response
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from fastapi.concurrency import run_in_threadpool
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel, Field, model_validator
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from sqlalchemy import delete, func
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from sqlalchemy import select as sa_select
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from sqlmodel import Session, col, select
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from fluksio.api.deps import CurrentUser, SessionDep, get_current_user
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from fluksio.core.db import writing
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from fluksio.flow.events import event_bus
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from fluksio.flow.messages import requalify
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from fluksio.flow.runs import RunRejected, RunService, new_run_id
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from fluksio.flow.store import FlowNotFound
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from fluksio.models import Run, RunArtifact, RunMetric, RunNode
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router = APIRouter(
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prefix="/runs", tags=["runs"], dependencies=[Depends(get_current_user)]
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)
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#: A sweep bigger than this is almost always a mistake in a loop.
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MAX_SWEEP = 1000
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def elapsed_ms(since: datetime) -> float:
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"""Milliseconds since an instant the columns stored as UTC."""
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start = since if since.tzinfo else since.replace(tzinfo=UTC)
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return round((datetime.now(UTC) - start).total_seconds() * 1000, 2)
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def _aware(when: datetime) -> datetime:
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"""A bound as the columns store it. A naive one is read as UTC."""
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return when if when.tzinfo else when.replace(tzinfo=UTC)
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#: Where a caller may say a run came from. "sweep" is not here because the
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#: sweep route writes it itself, and neither is a value a client made up: the
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#: column is only worth a table row if it means the same thing every time.
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RunCause = Literal["api", "cli", "sdk"]
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class RunCreate(BaseModel):
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params: dict[str, Any] = Field(default_factory=dict)
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seed: int | None = None
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#: Run the unpublished draft instead of what is published.
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draft: bool = False
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#: Execute every node, whatever an earlier run already worked out.
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no_cache: bool = False
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#: Who is asking. The dashboard leaves it, and is the "api" default.
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cause: RunCause = "api"
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#: A key the caller minted for this submission. Sending it again returns
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#: the run it already made, so a retry after a timeout cannot double-submit.
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idempotency_key: str | None = Field(default=None, max_length=64)
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class SweepEntry(BaseModel):
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params: dict[str, Any] = Field(default_factory=dict)
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seed: int | None = None
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#: One per entry, so retrying a half-created sweep recreates only the runs
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#: whose rows never landed.
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idempotency_key: str | None = Field(default=None, max_length=64)
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class SweepCreate(BaseModel):
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runs: list[SweepEntry] = Field(default_factory=list)
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draft: bool = False
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no_cache: bool = False
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class RunNodeRow(BaseModel):
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node: str
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status: str
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attempt: int
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duration_ms: float
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worker: str
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error: str
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logs: str
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#: What this node's result was looked up by. Empty when it may not be
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#: reused; `status` is "cached" when it was.
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cache_key: str = ""
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#: Which run it was restored from, when it was. That run is also where this
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#: node's series was recorded.
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cached_from: str = ""
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class ArtifactRow(BaseModel):
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name: str
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node: str
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digest: str
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size: int
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media_type: str
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#: What it was called where it was written, so something downloading it can
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#: give it that name back rather than the message's. Absent when the node
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#: never said one.
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filename: str | None = None
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class RunRow(BaseModel):
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"""A run without its result, which is the part that can be large."""
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id: str
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flow: str
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status: str
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status_reason: str
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cause: str
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params: dict[str, Any]
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params_digest: str
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#: The user repository's commit, for a flow declared in code with the
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#: decorators. Empty for one drawn on the canvas, where `commit` is the
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#: whole answer to what produced the number.
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origin_commit: str = ""
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#: What that repository's python files hashed to when the run started.
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#: Two runs of one dirty tree share a commit and differ here, which is the
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#: only way to tell apart what they actually executed.
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code_digest: str = ""
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#: The flow store's own commit. Short, unlike `result`, so the list
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#: carries it: "what code produced this" is a question asked of a table.
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commit: str = ""
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seed: int | None
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group_id: str | None
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labels: list[str]
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created_at: Any
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started_at: Any = None
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finished_at: Any = None
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#: How long it took, or — while it is still going — how long it has been
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#: going: a duration of its own is only written once a run finishes.
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duration_ms: float
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actor: str
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@model_validator(mode="after")
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def _running_duration(self) -> "RunRow":
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if self.status == "running" and not self.duration_ms and self.started_at:
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self.duration_ms = elapsed_ms(self.started_at)
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return self
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class RunDetail(RunRow):
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result: dict[str, Any] = Field(default_factory=dict)
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flow_version: int = 1
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nodes: list[RunNodeRow] = Field(default_factory=list)
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artifacts: list[ArtifactRow] = Field(default_factory=list)
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class FlowRunsRow(BaseModel):
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"""How much a flow has been run, for the screen's list of flows."""
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flow: str
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runs: int
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running: int
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queued: int
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last_created_at: Any = None
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class MetricPoint(BaseModel):
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step: int
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ts: float
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value: float
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name: str = ""
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class MetricSeries(BaseModel):
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"""The shape a chart widget already draws, so comparing runs is a binding."""
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label: str
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points: list[list[float]] = Field(default_factory=list)
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class SeriesAnswer(BaseModel):
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metric: str
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#: What the x values are: "step", "time" (seconds since this run's first
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#: reading), or the name of another metric this one was plotted against.
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x: str = "step"
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lines: list[MetricSeries] = Field(default_factory=list)
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def _service(request: Request) -> RunService:
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service: RunService | None = getattr(request.app.state, "run_service", None)
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if service is None:
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raise HTTPException(status_code=503, detail="Runs are not available")
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return service
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@router.post("/flows/{name}", response_model=RunRow, status_code=202)
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async def create_run(
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name: str, body: RunCreate, request: Request, user: CurrentUser
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) -> Any:
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"""Queue one run of a flow."""
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service = _service(request)
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try:
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return await run_in_threadpool(
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service.submit,
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name,
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params=body.params,
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seed=body.seed,
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cause=body.cause,
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actor=user.email,
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draft=body.draft,
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no_cache=body.no_cache,
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idempotency_key=body.idempotency_key,
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)
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except FlowNotFound as exc:
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raise HTTPException(status_code=404, detail=str(exc)) from exc
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except RunRejected as exc:
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raise HTTPException(status_code=422, detail=str(exc)) from exc
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@router.post("/flows/{name}/sweep", response_model=list[RunRow], status_code=202)
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async def create_sweep(
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name: str, body: SweepCreate, request: Request, user: CurrentUser
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) -> Any:
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"""Queue many runs of one flow under a shared group.
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An ensemble is this with the same parameters and different seeds; a grid
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search is this with the parameters spread out. Either way the caller
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builds the list — the engine does not own a sweep grammar.
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"""
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if not body.runs:
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raise HTTPException(status_code=422, detail="A sweep needs at least one run")
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if len(body.runs) > MAX_SWEEP:
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raise HTTPException(
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status_code=422, detail=f"A sweep is capped at {MAX_SWEEP} runs"
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)
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service = _service(request)
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group = new_run_id()
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def submit_all() -> list[Run]:
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return [
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service.submit(
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name,
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params=entry.params,
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seed=entry.seed,
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group_id=group,
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cause="sweep",
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actor=user.email,
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draft=body.draft,
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no_cache=body.no_cache,
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idempotency_key=entry.idempotency_key,
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)
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for entry in body.runs
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]
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try:
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return await run_in_threadpool(submit_all)
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except FlowNotFound as exc:
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raise HTTPException(status_code=404, detail=str(exc)) from exc
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except RunRejected as exc:
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raise HTTPException(status_code=422, detail=str(exc)) from exc
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@router.get("", response_model=list[RunRow])
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def read_runs(
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session: SessionDep,
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flow: str | None = None,
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status: str | None = None,
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group: str | None = None,
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digest: str | None = None,
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since: datetime | None = None,
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before: datetime | None = None,
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limit: Annotated[int, Query(ge=1, le=500)] = 50,
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offset: Annotated[int, Query(ge=0)] = 0,
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) -> Any:
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"""Runs, newest first. The queryable table an experiment log needs.
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``before`` is the cursor a long history is paged by: rows are newest
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first, so handing back the last row's ``created_at`` reads the next page
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whatever landed meanwhile — which ``offset`` cannot, since a run submitted
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between two pages shifts every row down one. ``since`` bounds the other
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end and is inclusive.
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"""
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statement = select(Run).order_by(col(Run.created_at).desc())
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if flow:
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statement = statement.where(col(Run.flow) == flow)
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if status:
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statement = statement.where(col(Run.status) == status)
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if group:
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statement = statement.where(col(Run.group_id) == group)
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if digest:
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statement = statement.where(col(Run.params_digest) == digest)
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if since:
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statement = statement.where(col(Run.created_at) >= _aware(since))
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if before:
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statement = statement.where(col(Run.created_at) < _aware(before))
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statement = statement.offset(max(0, offset)).limit(min(limit, 500))
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return list(session.exec(statement))
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@router.get("/overview", response_model=list[FlowRunsRow])
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def read_overview(session: SessionDep) -> Any:
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"""One row per flow that has ever run, busiest-recent first.
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The list caps at 500 newest runs, so counting flows on the client goes
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wrong the moment a history outgrows one page. The database counts instead.
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"""
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# Deliberately unwindowed. It is a group-by over the whole `run` table,
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# which is never pruned — but a run kept for years is the point of the
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# table, and a window here would drop a finished experiment off the rail
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# rather than making it cheaper to find. A rollup is the answer if the
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# scan ever measures.
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statement = sa_select(
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col(Run.flow),
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col(Run.status),
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func.count(col(Run.id)),
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func.max(col(Run.created_at)),
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).group_by(col(Run.flow), col(Run.status))
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rows: dict[str, FlowRunsRow] = {}
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for flow, status, count, latest in session.execute(statement):
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row = rows.setdefault(flow, FlowRunsRow(flow=flow, runs=0, running=0, queued=0))
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row.runs += count
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if status == "running":
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row.running += count
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elif status == "queued":
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row.queued += count
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if row.last_created_at is None or latest > row.last_created_at:
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row.last_created_at = latest
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return sorted(rows.values(), key=lambda row: row.last_created_at, reverse=True)
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# ---------------------------------------------------------------------------
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# Export
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#
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# Both routes are declared above `/{run_id}`, or "export" is read as the id of
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# a run nobody has. What they are for: an analysis wants a dataframe, and the
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# alternatives are a call per run or somebody reading our schema out of
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# `fluksio.db`. The two shapes below are what an analysis actually asks for.
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# ---------------------------------------------------------------------------
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#: What an export is written as. Parquet is a conversion the client does over
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#: jsonl, because keeping dtypes is worth a dependency only to whoever wants it.
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ExportFormat = Literal["csv", "jsonl"]
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#: The columns of the long table, in order. The run is on every row: it is the
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#: join back to the run page and to what the run made, and it is what makes an
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#: exported file auditable rather than loose.
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METRIC_COLUMNS = ("run", "name", "step", "ts", "value")
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#: A run's own columns in the wide table. Its inputs and its final numbers
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#: follow, prefixed, so an input named "status" cannot collide with the run's.
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#: The most runs one export covers. High enough that a real sweep fits and low
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#: enough that an unfiltered request cannot read the whole table into memory —
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#: which it did, before a byte was streamed, while holding one of the fifteen
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#: pooled database connections for the length of the download.
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EXPORT_CAP = 10_000
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RUN_COLUMNS = (
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"id",
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"flow",
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"status",
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"created_at",
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"started_at",
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"finished_at",
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"duration_ms",
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"seed",
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"group_id",
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"code_digest",
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"origin_commit",
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)
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def _selected(
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session: Session,
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flow: str | None,
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status: str | None,
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group: str | None,
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ids: str,
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since: datetime | None,
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until: datetime | None,
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) -> list[Run]:
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"""The runs an export covers, newest first — the filters the list takes.
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Capped: the filters bound a *sensible* request and nothing bounded an
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unfiltered one, so asking for everything read every row of the table into
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memory before a byte was sent. A truncated export says so in a header.
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"""
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statement = select(Run).order_by(col(Run.created_at).desc())
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if flow:
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statement = statement.where(col(Run.flow) == flow)
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if status:
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statement = statement.where(col(Run.status) == status)
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if group:
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statement = statement.where(col(Run.group_id) == group)
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named = [part for part in ids.split(",") if part]
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if named:
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statement = statement.where(col(Run.id).in_(named))
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if since:
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statement = statement.where(col(Run.created_at) >= _aware(since))
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if until:
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statement = statement.where(col(Run.created_at) < _aware(until))
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return list(session.exec(statement.limit(EXPORT_CAP + 1)))
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def _cell(value: Any) -> Any:
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"""A value as a table holds it: a scalar, a date as its ISO string, or a
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record kept as one — csv stringifies it at write time; jsonl wants it a
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value, not a string full of it."""
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return value.isoformat() if hasattr(value, "isoformat") else value
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def _leaves(value: Any, prefix: str = "") -> Iterator[tuple[str, Any]]:
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"""Everything a record holds, by its dotted path.
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A node returning a record rather than a scalar is the ordinary shape —
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the numbers arrive inside `final_metrics` — and a record in one cell is
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not a column anybody can compare. Lists are left whole: a curve belongs in
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the long table, not in a cell of this one.
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"""
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if isinstance(value, dict):
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for key, inner in value.items():
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yield from _leaves(inner, f"{prefix}.{key}" if prefix else str(key))
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else:
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yield prefix, value
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def _dig(record: dict[str, Any], path: str) -> Any:
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"""A dotted path into a record: `final_metrics.train_loss`.
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A key with a dot in its own name is not reachable this way, which is the
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price of the spelling.
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"""
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value: Any = record
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for part in path.split("."):
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if not isinstance(value, dict) or part not in value:
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return None
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value = value[part]
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return value
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def _scored(runs: list[Run]) -> list[str]:
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"""A run's final numbers: every number its declared outputs carry, however
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deep it sits. A flag is not a number, and neither is a label."""
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return sorted(
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{
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path
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for run in runs
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for path, value in _leaves(run.result)
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if isinstance(value, (int, float)) and not isinstance(value, bool)
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}
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)
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def _drain(buffer: io.StringIO) -> str:
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text = buffer.getvalue()
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buffer.seek(0)
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buffer.truncate(0)
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return text
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def _csv(columns: list[str], chunks: Iterator[list[dict[str, Any]]]) -> Iterator[str]:
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"""Header first, then a chunk at a time through one reused buffer.
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A cell that is still a record here — `_cell` leaves one alone for jsonl's
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sake — becomes the JSON string a csv cell can hold.
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"""
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buffer = io.StringIO()
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writer = csv.DictWriter(buffer, fieldnames=columns)
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writer.writeheader()
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yield _drain(buffer)
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for chunk in chunks:
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writer.writerows(
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{
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key: value
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if value is None or isinstance(value, (str, int, float, bool))
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else json.dumps(value)
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for key, value in row.items()
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}
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for row in chunk
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)
|
|
yield _drain(buffer)
|
|
|
|
|
|
def _stream(
|
|
fmt: str,
|
|
name: str,
|
|
columns: list[str],
|
|
chunks: Iterator[list[dict[str, Any]]],
|
|
truncated: bool = False,
|
|
) -> StreamingResponse:
|
|
"""The rows out, a chunk at a time rather than a list.
|
|
|
|
Streaming because the point of an export is that it is bigger than what a
|
|
screen reads: a sweep's curves are millions of rows.
|
|
"""
|
|
if fmt == "jsonl":
|
|
body: Iterator[str] = (
|
|
"".join(json.dumps(row) + "\n" for row in chunk) for chunk in chunks
|
|
)
|
|
media = "application/x-ndjson"
|
|
else:
|
|
body = _csv(columns, chunks)
|
|
media = "text/csv; charset=utf-8"
|
|
headers = {"Content-Disposition": f'attachment; filename="{name}.{fmt}"'}
|
|
if truncated:
|
|
# The body is otherwise indistinguishable from a complete one, and an
|
|
# export quietly missing its oldest runs is the wrong thing to hand
|
|
# somebody who is about to plot it.
|
|
headers["X-Truncated"] = str(EXPORT_CAP)
|
|
return StreamingResponse(body, media_type=media, headers=headers)
|
|
|
|
|
|
@router.get("/export/metrics")
|
|
def export_metrics(
|
|
session: SessionDep,
|
|
flow: str | None = None,
|
|
status: str | None = None,
|
|
group: str | None = None,
|
|
ids: str = "",
|
|
since: datetime | None = None,
|
|
until: datetime | None = None,
|
|
name: str = "",
|
|
stride: Annotated[int, Query(ge=1, le=100_000)] = 1,
|
|
format: ExportFormat = "csv",
|
|
) -> Any:
|
|
"""Every selected run's series as one long table: run, name, step, ts, value.
|
|
|
|
The tidy shape a plotting library takes without reshaping. ``name`` keeps
|
|
the metrics it lists; ``stride`` thins each curve — per series, so asking
|
|
for every tenth point of two metrics gives every tenth point of both.
|
|
"""
|
|
runs = _selected(session, flow, status, group, ids, since, until)
|
|
truncated = len(runs) > EXPORT_CAP
|
|
runs = runs[:EXPORT_CAP]
|
|
wanted = [part for part in name.split(",") if part]
|
|
step = max(1, stride)
|
|
|
|
def chunks() -> Iterator[list[dict[str, Any]]]:
|
|
for run in runs:
|
|
rows: list[dict[str, Any]] = []
|
|
for series, points in groupby(
|
|
_series(session, run.id), key=lambda row: row.name
|
|
):
|
|
if wanted and series not in wanted:
|
|
continue
|
|
rows.extend(
|
|
{
|
|
"run": run.id,
|
|
"name": row.name,
|
|
"step": row.step,
|
|
"ts": row.ts,
|
|
"value": row.value,
|
|
}
|
|
for row in list(points)[::step]
|
|
)
|
|
yield rows
|
|
|
|
return _stream(format, "metrics", list(METRIC_COLUMNS), chunks(), truncated)
|
|
|
|
|
|
@router.get("/export/runs")
|
|
def export_runs(
|
|
session: SessionDep,
|
|
flow: str | None = None,
|
|
status: str | None = None,
|
|
group: str | None = None,
|
|
ids: str = "",
|
|
since: datetime | None = None,
|
|
until: datetime | None = None,
|
|
params: str = "",
|
|
metrics: str = "",
|
|
format: ExportFormat = "csv",
|
|
) -> Any:
|
|
"""One row per run: what it was given, what it scored, what code it ran.
|
|
|
|
The arm-comparison table. Inputs are columns rather than one JSON blob —
|
|
every input the selection recorded, so the schema is the same whichever
|
|
runs are asked for; ``params`` narrows it. ``metrics`` narrows the final
|
|
numbers to a few of a run's declared outputs.
|
|
|
|
Both take dotted paths into a record a node returned:
|
|
``metrics=final_metrics.train_loss,test_metrics.known.perfect`` selects
|
|
three fields rather than two blobs, and the defaults reach the same
|
|
depth.
|
|
"""
|
|
runs = _selected(session, flow, status, group, ids, since, until)
|
|
truncated = len(runs) > EXPORT_CAP
|
|
runs = runs[:EXPORT_CAP]
|
|
inputs = [part for part in params.split(",") if part] or sorted(
|
|
{path for run in runs for path, _ in _leaves(run.params)}
|
|
)
|
|
scores = [part for part in metrics.split(",") if part] or _scored(runs)
|
|
columns = [
|
|
*RUN_COLUMNS,
|
|
*(f"param.{key}" for key in inputs),
|
|
*(f"metric.{key}" for key in scores),
|
|
]
|
|
|
|
def chunks() -> Iterator[list[dict[str, Any]]]:
|
|
for run in runs:
|
|
row = {column: _cell(getattr(run, column)) for column in RUN_COLUMNS}
|
|
row.update((f"param.{k}", _cell(_dig(run.params, k))) for k in inputs)
|
|
row.update((f"metric.{k}", _cell(_dig(run.result, k))) for k in scores)
|
|
yield [row]
|
|
|
|
return _stream(format, "runs", columns, chunks(), truncated)
|
|
|
|
|
|
@router.get("/{run_id}", response_model=RunDetail)
|
|
def read_run(run_id: str, session: SessionDep) -> Any:
|
|
"""One run in full: what it was asked, what each node did, what it made."""
|
|
run = session.get(Run, run_id)
|
|
if run is None:
|
|
raise HTTPException(status_code=404, detail="No such run")
|
|
nodes = session.exec(select(RunNode).where(col(RunNode.run_id) == run_id)).all()
|
|
artifacts = session.exec(
|
|
select(RunArtifact).where(col(RunArtifact.run_id) == run_id)
|
|
).all()
|
|
detail = RunDetail.model_validate(run, from_attributes=True)
|
|
detail.nodes = [RunNodeRow.model_validate(n, from_attributes=True) for n in nodes]
|
|
detail.artifacts = [
|
|
ArtifactRow.model_validate(a, from_attributes=True) for a in artifacts
|
|
]
|
|
return detail
|
|
|
|
|
|
@router.post("/{run_id}/cancel", response_model=RunRow)
|
|
async def cancel_run(run_id: str, request: Request, session: SessionDep) -> Any:
|
|
"""Stop a run. One already past its last node is left as it finished."""
|
|
run = session.get(Run, run_id)
|
|
if run is None:
|
|
raise HTTPException(status_code=404, detail="No such run")
|
|
service = _service(request)
|
|
await run_in_threadpool(service.cancel, run_id)
|
|
session.refresh(run)
|
|
return run
|
|
|
|
|
|
@router.delete("/{run_id}", status_code=204)
|
|
def delete_run(run_id: str, session: SessionDep, user: CurrentUser) -> Response:
|
|
"""Forget a run and everything hanging off it.
|
|
|
|
The same four statements ``_forget_runs`` uses when a flow goes: the run
|
|
tables carry a plain string ``run_id`` and no foreign key, so nothing
|
|
cascades on its own. ``flow_run``, ``metric_minute`` and ``engine_event``
|
|
stay — they are the observability record and are pruned on their own window.
|
|
|
|
A live run is refused rather than raced: the driver writes its nodes back
|
|
when it finishes, and those rows would arrive for a run that no longer
|
|
exists. Cancel it first.
|
|
|
|
Two things this costs, both deliberate. ``RunNode.outputs`` *is* the stage
|
|
cache, so a later run loses hits this one would have served. And a node
|
|
restored from this run points here through ``cached_from`` — ``_series``
|
|
already reads a missing source as an empty curve, which is what ``NO_CURVE``
|
|
explains on the screen. The artifact bytes need no help: ``sweep_artifacts``
|
|
keeps whatever a ``run_artifact`` row or a live message still names, so
|
|
dropping the rows is enough and the hourly sweep reclaims the blobs.
|
|
"""
|
|
# Reads the run, then decides whether to delete it — so it takes the write
|
|
# lock up front rather than upgrading and losing to whichever flush
|
|
# committed in between.
|
|
with writing(session):
|
|
run = session.get(Run, run_id)
|
|
if run is None:
|
|
raise HTTPException(status_code=404, detail="No such run")
|
|
if run.status in ("running", "queued"):
|
|
raise HTTPException(
|
|
status_code=409,
|
|
detail=(
|
|
f"Run {run_id} is {run.status}. Cancel it, or wait for it to "
|
|
"finish, before deleting it."
|
|
),
|
|
)
|
|
flow = run.flow
|
|
session.execute(delete(RunNode).where(col(RunNode.run_id) == run_id))
|
|
session.execute(delete(RunMetric).where(col(RunMetric.run_id) == run_id))
|
|
session.execute(delete(RunArtifact).where(col(RunArtifact.run_id) == run_id))
|
|
session.execute(delete(Run).where(col(Run.id) == run_id))
|
|
session.commit()
|
|
event_bus.publish(
|
|
{
|
|
"type": "audit",
|
|
"action": f"deleted run {run_id}",
|
|
"flow": flow,
|
|
"user": user.email,
|
|
"ts": time.time(),
|
|
}
|
|
)
|
|
return Response(status_code=204)
|
|
|
|
|
|
def _series(session: Session, run_id: str, name: str = "") -> list[RunMetric]:
|
|
"""A run's numbers, including the ones a cached node points at.
|
|
|
|
A cache hit replays no emissions, so a node restored from an earlier run has
|
|
no rows of its own — it carries that run's id instead, and its series is read
|
|
from there. Names are re-qualified on the way out, because the same node
|
|
reached through two flows publishes under two names and the caller asked for
|
|
this run's.
|
|
"""
|
|
statement = select(RunMetric).where(col(RunMetric.run_id) == run_id)
|
|
if name:
|
|
statement = statement.where(col(RunMetric.name) == name)
|
|
rows = list(session.exec(statement))
|
|
|
|
restored = session.exec(
|
|
select(RunNode).where(
|
|
col(RunNode.run_id) == run_id, col(RunNode.cached_from) != ""
|
|
)
|
|
).all()
|
|
if restored:
|
|
run = session.get(Run, run_id)
|
|
flow = run.flow if run is not None else ""
|
|
# Two queries for the lot, rather than a `Run` lookup and a `RunMetric`
|
|
# query per restored node. A comparison of twenty runs calls this
|
|
# twenty times, so the per-node round trips multiplied.
|
|
sources = {
|
|
row.id: row.flow
|
|
for row in session.exec(
|
|
select(Run).where(col(Run.id).in_({n.cached_from for n in restored}))
|
|
)
|
|
}
|
|
wanted: dict[tuple[str, str], list[RunNode]] = {}
|
|
for node_row in restored:
|
|
source_flow = sources.get(node_row.cached_from)
|
|
if source_flow is None:
|
|
# The run it came from is gone — deleted with its flow. The
|
|
# outputs are still on this run; the curve is not recoverable.
|
|
continue
|
|
key = (
|
|
node_row.cached_from,
|
|
requalify(node_row.node, flow, source_flow),
|
|
)
|
|
wanted.setdefault(key, []).append(node_row)
|
|
if wanted:
|
|
cached = session.exec(
|
|
select(RunMetric).where(
|
|
col(RunMetric.run_id).in_({run_id for run_id, _ in wanted}),
|
|
col(RunMetric.node).in_({node for _, node in wanted}),
|
|
)
|
|
)
|
|
for row in cached:
|
|
for node_row in wanted.get((row.run_id, row.node), ()):
|
|
source_flow = sources[node_row.cached_from]
|
|
renamed = requalify(row.name, source_flow, flow)
|
|
if name and renamed != name:
|
|
continue
|
|
rows.append(
|
|
RunMetric(
|
|
run_id=run_id,
|
|
name=renamed,
|
|
step=row.step,
|
|
node=node_row.node,
|
|
ts=row.ts,
|
|
value=row.value,
|
|
)
|
|
)
|
|
|
|
rows.sort(key=lambda row: (row.name, row.step))
|
|
return rows
|
|
|
|
|
|
@router.get("/{run_id}/metrics", response_model=list[MetricPoint])
|
|
def read_metrics(
|
|
run_id: str,
|
|
session: SessionDep,
|
|
name: str = "",
|
|
stride: Annotated[int, Query(ge=1, le=100_000)] = 1,
|
|
) -> Any:
|
|
"""One metric's series, in step order — or every one of them, unnamed.
|
|
|
|
``stride`` thins a long curve down: 3000 steps drawn on a 400-pixel chart
|
|
is 3000 points nobody can see.
|
|
"""
|
|
rows = _series(session, run_id, name)
|
|
if stride > 1:
|
|
rows = rows[:: max(1, stride)]
|
|
return rows
|
|
|
|
|
|
def _points(session: Session, run_id: str, metric: str, x: str) -> list[list[float]]:
|
|
"""One run's readings of ``metric``, against whichever x was asked for.
|
|
|
|
The step is the default because it is what every run has. Time answers
|
|
"which one got there sooner", and is measured from this run's own first
|
|
reading so that runs started hours apart still lie on top of each other.
|
|
Another metric answers "against what the loop was actually counting" — an
|
|
epoch, or samples seen — and is joined on the step the two share, which is
|
|
the only thing they have in common.
|
|
"""
|
|
rows = _series(session, run_id, metric)
|
|
if x == "time":
|
|
if not rows:
|
|
return []
|
|
start = min(row.ts for row in rows)
|
|
return [[row.ts - start, row.value] for row in rows]
|
|
if x and x != "step":
|
|
against = {row.step: row.value for row in _series(session, run_id, x)}
|
|
return [[against[row.step], row.value] for row in rows if row.step in against]
|
|
return [[float(row.step), row.value] for row in rows]
|
|
|
|
|
|
@router.get("/series/compare", response_model=SeriesAnswer)
|
|
def compare_metric(session: SessionDep, ids: str, metric: str, x: str = "") -> Any:
|
|
"""One metric across several runs, as the chart widget's series shape.
|
|
|
|
This is the comparison view: it answers in the same shape a flow answers a
|
|
chart's query with, so putting three training curves beside each other is
|
|
a widget binding rather than a screen of its own.
|
|
|
|
``x`` names what to plot against — nothing or "step", "time", or another
|
|
metric of the same runs.
|
|
"""
|
|
run_ids = [part for part in ids.split(",") if part]
|
|
if not run_ids:
|
|
raise HTTPException(status_code=422, detail="Name at least one run")
|
|
runs = {
|
|
run.id: run
|
|
for run in session.exec(select(Run).where(col(Run.id).in_(run_ids))).all()
|
|
}
|
|
lines: list[MetricSeries] = []
|
|
for run_id in run_ids:
|
|
run = runs.get(run_id)
|
|
if run is None:
|
|
continue
|
|
label = run_id
|
|
if run.seed is not None:
|
|
label = f"{run_id} (seed {run.seed})"
|
|
lines.append(
|
|
MetricSeries(label=label, points=_points(session, run_id, metric, x))
|
|
)
|
|
return SeriesAnswer(metric=metric, x=x or "step", lines=lines)
|