Zyeon UICharts
Training Runs
A four-state training-runs card: loss against optimiser step for several concurrent experiments, each a debiased EMA over its own faint raw trace, with divergence and restart markers, an optional learning-rate schedule on a secondary axis, and a scan that reads every run at the same step and says whether the lead survives the noise.
Preview in your theme
Loading preview…
Installation
npx shadcn@latest add https://ui.zyeon.ai/r/chart-training-runs.jsonPrompt
Build a React + TypeScript + Tailwind "ChartTrainingRuns" experiment-comparison
card in plain SVG with zod. Not recharts: the drawn line is derived from the
payload rather than sent (an EMA the reader re-weights live), each run needs a
second faint copy of itself underneath, the event markers have to sit on one
named run's raw value, and the learning-rate schedule is a second unit that must
NOT share the loss gridlines — none of which is a chart-library prop.
Contract
- One zod schema is the source of truth:
{ status: "loading" | "empty" | "error" | "ready"; title: string;
metric: string; // y-axis title and the readout unit
runs: { id: string; label: string;
points: { step >= 0; loss: number }[];
lrPoints?: { step >= 0; lr >= 0 }[];
events?: { step >= 0; kind: "divergence" | "restart";
note?: string }[] }[];
smoothing: number in [0, 0.99] }.
- points are the RAW logger output, never pre-smoothed: the card smooths them,
and a pre-smoothed series cannot say how much scatter it is hiding — which is
the yardstick every comparison on this card is judged against.
- Nothing assumes a loss range and nothing is clamped: a spike to 6 nats is a
real reading, and it is the reading a divergence marker exists to explain.
- superRefine: ready needs one run with >= 2 readings ("one reading is a number,
not a curve"); run ids unique; every event step inside its run's logged range,
because an event outside it has no line to sit on. Guard each access so a
ragged payload yields an issue, not a TypeError.
- Props = z.infer plus height (default 240, clamped 160..480), onRetry,
className and the div's native props; forwardRef to the card.
- Export the maths beside the component so a test can print the numbers the
picture is made of: smoothSeries(), stepScatter(), emaStandardError(),
largestRemainder(), seriesValueAt(), buildTrainingRunsModel(), readRunsAt(),
compareRuns(), axisStep().
Maths (state the formulas in comments; they are the component, not decoration)
- Smoothing is TensorBoard's DEBIASED EMA:
acc_n = w·acc_{n-1} + (1-w)·x_n
bias_n = w·bias_{n-1} + (1-w) ( = 1 - w^n )
smooth_n = acc_n / bias_n
The divisor is what stops the opening points being dragged toward the zero the
accumulator starts at. w = 0 returns the raw series untouched. One-sided, so a
spike never bends the line before the step it happened at.
- Step-to-step scatter from successive differences, independent of w:
sigma = sqrt( SUM (x_i - x_{i-1})^2 / (2 (n-1)) )
because E[(x_i - x_{i-1})^2] = 2·sigma^2 around a slowly moving trend — the
noise is measured without having to know the trend.
- Standard error of the smoothed line: se = sigma·sqrt((1-w)/(1+w)), from
Var = sigma^2 (1-w)^2 SUM w^{2k}. Heavier smoothing therefore buys a tighter
reading, and the card prints the bar so the slider's effect is visible.
- A lead counts only when gap > 2·sqrt(se_leader^2 + se_runner^2) — the standard
error of the DIFFERENCE, never one line's own bar.
- A divergence is paired with the first restart after it; the steps between are
what the rollback cost. A divergence with no restart means the run was
abandoned there.
- Step shares are apportioned by LARGEST REMAINDER over each run's last logged
step, so the printed integers add to exactly 100 (30.77/30.77/24.62/13.85
rounds to 101 if each share is rounded alone). Ties break on the larger value,
then on payload order.
Behavior
- Four first-class branches in one bg-card panel: loading (three descending
skeleton traces + tick column, aria-hidden, pulse disabled under
motion-reduce), empty (needs at least one run with two readings, worded so it
cannot be read as a failed fetch, plus the count of unusable readings), error
(message + "Try again" only when onRetry exists), ready. A ready payload with
no run carrying two readings — the schema's own test, not "are there two
distinct steps" — falls through to empty rather than rendering an axis around
two bare dots.
- Per run: sort a COPY by step, drop repeats (two losses at one step are a
contradiction, and their mean is a number nobody logged) and non-finite pairs,
place events by interpolating the raw series, count what could not be placed
and say so on the card. A run that logged NOTHING is counted the same way: it
is in no figure on the card, and every headline count reads off the drawable
runs, so the card would otherwise print four runs for a payload of five.
- TWO controls, and they do different jobs: a native <input type="range"> for
the smoothing weight (0..0.95 in 0.05 steps, seeded from the payload's
`smoothing`, taking over once dragged) which RE-DERIVES every line and shrinks
the noise bar; and a scan (pointer move + a role="slider" group with
arrows/Page/Home/End) that only moves the readout. Keyboard moves announce
through a live region; pointer moves do not, or the region becomes a queue.
- The scan rests at the last step EVERY run reached — the only step where the
sweep compares without silently dropping a run that stopped early. Readout
rows stay in payload order so they do not leap about while scanning; the
leader is marked instead.
- Each row answers one of three things: where the run stopped ("ended 96k"), how
far it is off the leader ("+0.084"), or — with nobody to compare against — the
raw log. Outside a run's logged range the value is null, never extrapolated.
- The learning-rate overlay is a toggle (aria-pressed), and it appears only when
some run shipped lrPoints. Its column says "no lr" only for a run that shipped
no schedule at all; a run that did log one but is outside its range here gets
a dash, the same as any other unread value.
Rendering & styling
- Colour identifies the RUN and nothing else: var(--chart-{(i % 5) + 1}) for its
smoothed line, its raw trace, its schedule, its event glyphs, its legend
swatch and its budget segment. Line style identifies the QUANTITY: solid
2px = smoothed, 1px at opacity-40 dark:opacity-60 = raw, "1.5 3" dots at
1.25px = learning rate. Shape identifies the EVENT: filled triangle =
divergence, hollow diamond = restart. Every run also takes a dash pattern, so
the card survives greyscale.
- The lost-step band is fill-destructive/10 dark:fill-destructive/20 — the dark
wash is twice the light one, because a tenth of a bright ink lands within a
few sRGB steps of a near-black card. Divergence rules stroke-destructive,
restart rules stroke-muted-foreground, both dashed, painted under the data.
- The loss axis owns the gridlines. The learning-rate axis starts at zero, gets
tick marks at the right edge and never a gridline: a second set would claim
the two units share a scale. Both axes guard their degenerate extent — a flat
loss and an all-zero schedule each fall back rather than divide by nothing.
- The y window grows from the RAW readings (the EMA is a convex combination of
them, so it can never leave their range). Both x-domain ends are always
labelled; an interior tick survives only if its own label clears them.
- Precision follows the axis step, never a constant: tick digits from the step,
readouts two digits finer. tabular-nums everywhere numbers change.
- Accessibility: the SVG is role="group" (not role="img", which would silence
the slider inside it), labelled by the heading and described by the summary
line; the whole table of smoothed values is repeated sr-only, rank-sampled to
~14 rows; focus-visible rings on both controls; the ResizeObserver is
disconnected on unmount and on node change.
Customization levers
- Plot density: `height` (160..480) and the tick pitches (80px x, 46px y) —
raise them for a wall dashboard, lower for a card in a grid.
- Smoothing range: SMOOTHING_MAX / SMOOTHING_STEP set the slider grid; make it
logarithmic (0.9/0.99/0.999) for runs logged every few steps, or drop the
slider entirely and take the weight from the payload alone.
- Which sub-blocks ship: the schedule overlay, the event note list, the verdict
box and the step-budget strip are each independent — delete any of them
without touching the plot.
- The comparison rule: SIGMA = 2 is the only tuning constant. Raise it for a
stricter claim, or swap the whole test for "best final loss" if your team
reads runs that way — the card prints whichever rule it uses.
- Palette: the colour formula cycles five chart tokens; map a fixed token per
run id (baseline always chart-1) by replacing inkFor.
- Units: `metric` names the y axis, so perplexity, bits-per-byte or grad-norm
drop in unchanged; formatLr controls how the schedule is written ("3.0e-4"
vs "0.0003").
- Density of the readout: the grid floor (19rem) decides when rows wrap to one
column; drop the lr column, or add a raw column beside the smoothed one, by
editing one row template.Concepts
- Raw under smoothed — the EMA is drawn over a faint copy of the readings it came from, because a smoothed line alone cannot show how much it is hiding; the weight is a live slider, so the reader can watch a "difference" dissolve into scatter instead of taking the default weight's word for it.
- Debiased EMA — dividing by
1 − wⁿis what keeps the first few points from being dragged toward the accumulator's zero. Without it the opening of every run reads lower than it was, which is precisely the stretch where a divergence gets argued about. - Noise bar, not a vibe — step-to-step scatter is estimated from successive differences (so the trend never inflates it into the estimate), converted to the smoothed line's own standard error by
√((1 − w)/(1 + w)), and a lead is reported only when it clears twice the standard error of the difference between two runs. Smoothing tightens the bar; it cannot tighten the data. - Same step, every run — the scan rests at the last step every run reached, so nothing that stopped early is quietly dropped from a ranking; rows keep payload order while scanning and the leader is badged, because rows that re-sort under the pointer are unreadable.
- Incidents cost steps — a divergence is paired with the restart that answered it and the gap between them is shaded and counted as steps burnt; a divergence with no restart is a run that ended there, and the card says so rather than letting the line just stop.
- Two units, one frame — the learning-rate schedule shares the run's colour but takes a dotted line and the right axis, which gets tick marks and never gridlines: two sets of gridlines would claim the units share a scale.
- Largest remainder — step shares are floored and the leftovers handed to the biggest fractional parts, so 30.77 / 30.77 / 24.62 / 13.85 prints 31 / 31 / 24 / 14 and the strip's widths are the same integers the legend prints.
Eval Leaderboard
A model × benchmark leaderboard: every column normalised in its own direction, cells shaded with the raw score printed, per-column bests marked, rank with movement arrows against the previous snapshot, and a weighted index whose whole-percent weights always total 100.
Token Latency
A four-state LLM serving-latency scatter: time to first token against time per output token for every request, nearest-rank p50/p95 crosshairs, an inclusive budget crosshair that cuts the plane into four filterable zones, and queue/prefill/decode phase bars for the median and p95 requests.