fix(studio-app): compact loss chart points instead of tail-slicing (#215) - #230
fix(studio-app): compact loss chart points instead of tail-slicing (#215)#230Bishalsingh153 wants to merge 21 commits into
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…rkorlab#215) JobDetail.tsx previously kept only the most recent MAX_LOSS_POINTS loss frames via tail-slicing, silently dropping the start of long training runs once a run exceeded the cap. Replace the tail-slice with compactLossPoints, a pure helper that compacts to half the cap by preserving the first/last point, every evalLoss point, and local min/max of the loss series, filling any remaining budget with evenly-spaced points. Output stays a subsequence of the input in original order, so LossChart's sort-by-step binary-search tooltip is unaffected. Adds unit test coverage for boundary sizes, evalLoss preservation, extrema preservation (including a flat-line tie case), the sort-by-step invariant, and a simulated long-run scenario.
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WalkthroughThe change adds bounded loss-point compaction, full-run streaming statistics, stale-stream protection, chart integration, route remounting, tests, and documentation. ChangesLoss chart updates
Estimated code review effort: 4 (Complex) | ~60 minutes Merge Risk: 🟡 Moderate · up to The PR changes loss-history compaction and advanced statistics; unresolved edge cases can exceed the requested retention bound or produce incomplete loss-chart data, while related wording remains inconsistent. Merge should wait for these bounded correctness issues to be fixed or explicitly accepted. Sequence Diagram(s)sequenceDiagram
participant JobDetail
participant RunningStats
participant compactLossPoints
participant LossChart
JobDetail->>RunningStats: Update valid training and evaluation values
JobDetail->>compactLossPoints: Merge and compact loss frames
compactLossPoints-->>JobDetail: Return bounded chart points
JobDetail->>LossChart: Provide points and running statistics
LossChart-->>LossChart: Finalize and display advanced statistics
Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
✨ Simplify code
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Code Review BotNo reviewable code changes were analyzed. |
Greptile SummaryThis PR replaces tail-slicing with bounded, extrema-aware loss-point compaction and maintains full-run streaming statistics independently of retained chart points.
Confidence Score: 5/5The PR appears safe to merge. No blocking failure remains.
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| Filename | Overview |
|---|---|
| packages/studio-app/src/lib/lossDownsample.ts | Adds bounded step-bucketed compaction, same-step merging, capacity reclamation, significance-aware selection, and ES2022-compatible sorting. |
| packages/studio-app/src/lib/stats.ts | Adds immutable streaming statistics, bounded percentile sampling, and exact replacement of the most recently recorded sample. |
| packages/studio-app/src/pages/JobDetail.tsx | Integrates chart compaction and full-run accumulators while guarding job lifecycle boundaries and adjacent corrections. |
| packages/studio-app/src/components/jobs/LossChart.tsx | Uses supplied full-run accumulators for Advanced metrics and accurately labels their statistical scope. |
| packages/studio-app/src/App.tsx | Keys JobDetail by job ID so direct navigation starts with a fresh component instance. |
| docs/studio/jobs.mdx | Documents bounded chart compaction and full-run versus sampled Advanced statistics. |
| docs/ja/studio/jobs.mdx | Adds the corresponding Japanese documentation for the changed Studio behavior. |
Flowchart
%%{init: {'theme': 'neutral'}}%%
flowchart TD
SSE[training.log frame] --> Validate[Validate step and finite values]
Validate --> Stats[Update or correct full-run statistics]
Validate --> Merge[Merge adjacent same-step chart frame]
Merge --> Cap{More than 2,000 points?}
Cap -- No --> Chart[Render retained chart points]
Cap -- Yes --> Compact[Compact to half-cap by series and step buckets]
Compact --> Chart
Stats --> Advanced[Render Advanced metrics]
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| }, | ||
| ]; | ||
| return next.length > MAX_LOSS_POINTS | ||
| ? next.slice(next.length - MAX_LOSS_POINTS) | ||
| ? compactLossPoints(next, MAX_LOSS_POINTS / 2) |
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This changes Studio's loss-chart retention behavior without the required corresponding English and Japanese documentation, leaving users and maintainers without a documented description of the new compaction semantics and limits.
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**Paired behavior docs are missing**
This changes Studio's loss-chart retention behavior without the required corresponding English and Japanese documentation, leaving users and maintainers without a documented description of the new compaction semantics and limits.
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In `@packages/studio-app/src/lib/lossDownsample.ts`:
- Around line 68-72: Update the selection logic in loss downsampling so boundary
points and finite evalLoss points are always retained before selecting extrema
or filler points; only use the remaining targetSize capacity for extrema
sampling. Remove the equal-priority mustKeepSorted sampling path, and add a
regression test covering an alternating loss series with a sparse evalLoss point
at an index that extrema sampling would otherwise skip.
- Line 65: Replace both toSorted calls in the loss downsampling logic with
copied arrays sorted via sort, preserving the current numeric ordering and
avoiding mutation of the original collections.
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| } | ||
| } | ||
|
|
||
| const mustKeepSorted = [...mustKeep].toSorted((a, b) => a - b); |
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When an older supported browser receives the 2,001st loss point, this newly reached call to Array.prototype.toSorted throws because the SPA targets ES2022 and Vite/esbuild does not polyfill ES2023 methods, as already documented in LossChart.tsx:100-104 and stats.ts:71-77. Consequently, long-running charts stop updating precisely when compaction first runs; sort the fresh array with .sort() instead (and do the same for the second toSorted below).
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| ]; | ||
| return next.length > MAX_LOSS_POINTS | ||
| ? next.slice(next.length - MAX_LOSS_POINTS) | ||
| ? compactLossPoints(next, MAX_LOSS_POINTS / 2) |
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Once a run exceeds 2,000 points, this replaces the array that LossChart.tsx:129-141 also feeds into summarize(), but the new sample intentionally overrepresents local extrema rather than being statistically representative. For example, with a spike every 20 points, all spikes and their adjacent minima are retained while ordinary points are sampled, roughly doubling the displayed mean after the first compaction; variance, percentiles, and the reported confidence interval are similarly biased. Keep independent streaming/statistical state, or use a representative reservoir for the advanced metrics instead of computing them from the visualization sample.
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| if (mustKeepSorted.length >= targetSize) { | ||
| // Too many "must keep" points to fit the budget: evenly sample | ||
| // down from the must-keep set itself. `evenSample` always retains | ||
| // its own first/last entry, so run boundaries survive either way. | ||
| selected = evenSample(mustKeepSorted, targetSize); |
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For highly oscillatory loss data where the extrema set itself exceeds the target, evenly sampling these indices can erase the oscillation and invent a broad trend. With 2,001 points alternating between 0 and 1, the rounded 2,000/999 stride selects long runs of one parity, so the retained chart is approximately 0 for the first quarter, 1 for the middle half, and 0 for the final quarter instead of showing the repeated peaks. Use bucketed min/max selection or another scheme that retains both sides of high-frequency oscillations when falling back under the cap.
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…korlab#215) Five bots (Greptile, CodeRabbit, Sentry, Codex, cubic) converged on issues with the initial compactLossPoints implementation: 1. Array.prototype.toSorted() is ES2023; the Studio SPA's tsconfig pins target: ES2022 and Vite/esbuild does not polyfill it, so this would throw once a run exceeded MAX_LOSS_POINTS in older evergreen browsers. stats.ts and LossChart.tsx already document and avoid this exact pitfall. Replaced both toSorted() calls with the established [...arr].sort() + eslint-disable-next-line unicorn/no-array-sort pattern used elsewhere in this package. 2. When the combined must-keep set (boundaries + evalLoss + extrema) exceeded targetSize, the fallback treated all three with equal priority via a single even-sample, so a sparse evalLoss point could be sampled away by frequent extrema, contradicting the function's documented guarantee that evalLoss survives compaction. Redesigned as strict priority tiers (boundaries, then evalLoss, then extrema, then generic filler), where each tier only yields budget to the next once satisfied. Updated one existing test whose small budget was implicitly relying on the old equal-priority behavior, and added a new regression test (alternating loss series forcing a large extrema set, with a sparse evalLoss point at a position no naive stride-sample would land on) that fails against the old flat-priority logic and passes with the fix. 3. Codex/cubic noted that JobDetail's compacted `points` array feeds both the visual chart and LossChart's "Advanced" stats panel (mean/variance/percentiles/CI over loss, via summarize()), and that compaction's extrema preference biases those stats once compaction has run. Properly decoupling stats from the bounded visual sample (independent running statistics) is a larger, separate change than this fix's scope; documented the tradeoff clearly in code comments at the call site instead of attempting that rewrite here. evalLoss stats are unaffected in the common case since that series is kept in full. 4. Codex separately noted that when the extrema set itself exceeds the budget, stride-sampling by position (rather than bucketed min/max) can under-represent genuinely high-frequency oscillation. Documented as a known limitation in the module doc; a bucketed scheme for this tier specifically is a further, more involved improvement. 5. Greptile flagged missing paired EN/JA documentation for the new compaction behavior (AGENTS.md requires this land in the same PR for Studio behavior changes). Added a short note to docs/studio/ jobs.mdx (and its JA mirror) describing the 2,000-point cap, compaction instead of dropping, and the same stats-bias caveat from the code comments, following the existing "event log keeps only the last 500 entries" precedent already in that file. Verified fix arkorlab#2 catches the regression: reverted to flat-priority sampling locally, confirmed the new test fails against it, then confirmed it passes with the fix restored. Verified with `mint validate`. Full studio-app test suite passes (218/218). Full Studio E2E suite passes (9/9). pnpm build, typecheck, lint, format:check, check:no-em-dash all pass repo-wide.
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| const filler = evenSample(notKept, Math.min(budget, notKept.length)); | ||
| for (const i of filler) selected.add(i); |
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When a long monotonic run triggers compaction repeatedly, sampling uniformly from the already-compacted array gives old and newly appended points equal weight even though each old point represents a much larger step interval. Each pass therefore halves the historical representatives again: the 50,000-step scenario in the added test retains only steps 0 and 1 before jumping to roughly step 39,071, so nearly the entire early convergence shape that this change is intended to preserve disappears. Carry representative weights or sample buckets by the original step span so later compactions do not exponentially discard older regions.
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| if (budget > 0 && evalLossIndices.length > 0) { | ||
| const kept = evenSample( | ||
| evalLossIndices, | ||
| Math.min(budget, evalLossIndices.length), | ||
| ); | ||
| for (const i of kept) selected.add(i); |
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When enough retained frames carry evalLoss, this tier consumes the entire 998-point budget before extrema or filler are considered. This is especially destructive for the explicitly supported eval-only frame shape: with roughly 1,000 eval-only points in a 2,001-event run, compaction leaves only the two boundary training-loss points and turns the training curve into one long segment, despite most of the input containing usable loss values. Apply a per-series allocation or compact the eval series independently so prioritizing eval data cannot erase the other plotted series.
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| if (budget > 0 && extremaIndices.length > 0) { | ||
| const kept = evenSample( | ||
| extremaIndices, | ||
| Math.min(budget, extremaIndices.length), | ||
| ); | ||
| for (const i of kept) selected.add(i); |
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When an extremum also carries evalLoss, it is already in selected, but this sampling pass still counts it against the remaining extrema budget. For example, with boundaries at indices 0 and 7, extrema at 1 and 2, an eval value at 2, and targetSize = 4, evenSample([1, 2], 1) returns the already-selected index 2; the filler then chooses index 6 and drops extremum 1 even though every unique boundary, eval point, and extremum fits exactly within the target. Filter extremaIndices against selected before applying the budget so overlap between priority tiers does not waste slots.
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| for (const [i, point] of points.entries()) { | ||
| if (boundaryIndices.has(i)) continue; | ||
| const e = point.evalLoss; | ||
| if (typeof e === "number" && Number.isFinite(e)) evalLossIndices.push(i); |
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When a trainer re-emits a step in complementary frames—for example, one frame with loss and a later eval-only frame with evalLoss—LossChart.tsx:86-91 deliberately merges both fields into one chart point, but this helper compacts the raw frames independently. The eval-only frame is prioritized here while the ordinary loss frame can be omitted by the later tiers, so after compaction the merged step silently loses its training-loss value. Merge frames by step before downsampling, or ensure that retaining any representative for a step also preserves all finite fields observed for that step.
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Inline comments:
In `@docs/studio/jobs.mdx`:
- Around line 82-83: Update the training-loss and eval-loss caveat in
docs/studio/jobs.mdx at lines 82-83 and docs/ja/studio/jobs.mdx at lines 82-83:
retain that eval statistics are usually less affected because of retention
priority, but state that they can change when all eval points do not fit within
the 1,000-point compaction target, not only the 2,000-point cap.
- Around line 70-71: Update the retention terminology in docs/studio/jobs.mdx
lines 70-71 to “finite numeric evalLoss” and “local loss minima and maxima”;
make the corresponding Japanese wording changes in docs/ja/studio/jobs.mdx lines
70-71 to “有限な数値の evalLoss” and “loss の局所的な極小値と極大値”.
In `@packages/studio-app/src/lib/lossDownsample.ts`:
- Around line 100-108: Update the tier-3 extrema selection around evenSample so
it filters extremaIndices to points not already present in selected before
calculating the budget, sampling, and adding them. Preserve the target-size
limit and add a regression case covering overlap between evalLoss selections and
extrema.
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| The chart keeps at most 2,000 points per job. Once a run exceeds that, older points are compacted rather than dropped, so the shape of the whole run, including its start, stays visible at coarser resolution instead of the chart silently losing its earliest data. The first and last points, every point carrying a numeric `evalLoss`, and local loss spikes are prioritized to survive compaction. | ||
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
Use the exact retention terminology in both documentation files. The implementation retains finite numeric evalLoss values and preserves local minima and maxima, not only upward spikes.
docs/studio/jobs.mdx#L70-L71: replace “numericevalLoss” with “finite numericevalLoss” and “local loss spikes” with “local loss minima and maxima”.docs/ja/studio/jobs.mdx#L70-L71: replace数値の evalLosswith有限な数値の evalLossand局所的な loss のスパイクwithloss の局所的な極小値と極大値.
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In `@docs/studio/jobs.mdx` around lines 70 - 71, Update the retention terminology
in docs/studio/jobs.mdx lines 70-71 to “finite numeric evalLoss” and “local loss
minima and maxima”; make the corresponding Japanese wording changes in
docs/ja/studio/jobs.mdx lines 70-71 to “有限な数値の evalLoss” and “loss
の局所的な極小値と極大値”.
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Thanks for the follow-up commit. I reviewed the current head c862527.
The ES2022 toSorted() issue, strict eval-loss priority, and paired EN/JA documentation have been addressed. However, I am requesting changes because the current algorithm still does not satisfy the core whole-run retention requirement:
-
Repeated compaction collapses historical coverage. Replaying the current filler strategy over a 50,000-step monotonic run leaves 1,952 points, but the first 35,000 steps are represented only by steps 0 and 1. The next retained historical point is step 39,071. This keeps the boundary but no longer keeps the shape of the whole run. Please compact by original-step buckets / representative spans, or use a stable hierarchical min-max scheme that does not resample already-compacted representatives as if they had equal weight.
-
Same-step split frames can lose one series.
LossChartcurrently coalesces duplicate steps, but compaction happens first. If one frame containslossand a later frame at the same step containsevalLoss, retaining only the eval frame drops the training value. Coalesce by step before compaction or preserve the finite fields from all retained frames for that step. -
Eval/extrema tier overlap and dense eval traffic can starve the training curve. Filter extrema already present in
selectedbefore spending the extrema budget, and reserve a defined amount of capacity for training-loss representatives or compact the two series independently. -
Advanced statistics still describe the visualization sample, not the run. Documentation of the bias is useful, but labels such as mean, variance, percentiles, and confidence interval remain misleading once compaction starts. Keep streaming/full-run statistics separately (for example online moments plus a bounded quantile sketch), or explicitly rename the UI metrics as retained-sample statistics.
-
The extrema overflow fallback still aliases oscillating data. Position-sampling alternating extrema can create broad false trends. Please use bucketed min/max selection and add a regression test that verifies both sides of a high-frequency oscillation remain visible.
Please also tighten the null-loss extrema test so it fails if extrema detection is removed, and update the docs to state that eval statistics can change when eval points exceed the 1,000-point compaction target.
CI and CodeQL are currently action_required with no jobs, so they must be approved and pass after the fixes.
…rkorlab#215) Codex's fresh review round on the previous fix caught two further issues, both confirmed by direct simulation before being fixed: 1. Position-based (array-index) sampling across repeated compaction passes geometrically erodes how many representatives the original early history keeps. JobDetail calls compactLossPoints every time the retained array re-fills past the cap, on an already-compacted array; sampling evenly by array position gives newly-appended raw points and older, already-thinned survivors equal weight by count, not by the step range each represents. Simulated the reported 50k- step scenario against the prior implementation: only steps 0 and 1 survived below step 1,000, jumping straight to roughly step 39,000, defeating the whole point of this fix for sufficiently long runs. Fixed by bucketing the step range into equal-width buckets by step value (not array position) and keeping one representative per bucket. This ties each compaction pass's coverage to the actual step range, which stays stable across repeated passes regardless of how many raw points currently occupy any given region. 2. A related concern from the same review round: a dense evalLoss series (frequent eval-only frames) could win almost every step- value bucket outright under a flat evalLoss > extremum priority rule, crowding the training-loss series out of the chart almost entirely once compaction had run. Confirmed with a constructed worst case (evalLoss on every other step): the training-loss series was reduced to just the two boundary points. Fixed by splitting the bucket budget between the two series (each getting up to half, with a sparser series' unused share water- filled to the other) rather than a single shared, un-bucketed priority ranking across both. Also fixes a real (if narrower) issue from the prior round: the trainer can emit a step's training-loss and eval-loss as two separate frames (LossChart's own "eval-only frames" comment documents this). Without merging by step first, compaction could keep one frame for a step and drop the other, silently losing whichever field lived only in the dropped frame, even though LossChart's own render-time merge would otherwise have combined them. Points are now merged by step (later frame's non-null fields win, matching LossChart's merge semantics exactly) before bucketing. Testing: updated one existing test (deep-equal instead of referential equality, since merging always allocates fresh objects/array even when no compaction is needed); strengthened the long-run simulation test with explicit early-history coverage assertions (>10 points below step 5,000, max gap <2,500) that fail against the pre-bucketing implementation and pass with the fix; added a merge-by-step regression test and a series-crowd-out regression test, both verified against a direct before/after simulation of the described worst cases. Full studio-app test suite passes (220/220). Full Studio E2E suite passes (9/9). pnpm build, typecheck, lint, format:check, check:no-em-dash all pass repo-wide.
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In `@packages/studio-app/src/lib/lossDownsample.ts`:
- Around line 109-130: The bucket allocation in the downsampling flow around
bucketSelect must redistribute unused capacity between evalLoss and
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| const half = Math.ceil(bucketCount / 2); | ||
| const evalBudget = Math.min(half, evalCandidates.length); |
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When a 2,001-frame run has loss-only boundary frames and 1,999 eval-only interior frames, this caps the eval selection at 499 while the loss selection has no interior candidates, so compaction retains only 501 points and discards 1,500 eval values despite the eval series remaining below the documented 2,000-point cap. This unnecessarily degrades the eval curve and biases its advanced statistics, contradicting the stated guarantee that eval statistics remain unaffected unless that series alone exceeds the cap; reallocate the loss series' unused share back to eval candidates.
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…ab#215) Nicolas0315's review (against commit c862527, one behind the previous fix) confirmed items 1, 2, and the core of 3 (repeated-compaction decay, same-step split frames, series starvation) were already fixed in c7134bf. This addresses everything else raised: 1. Extrema/eval budget overlap waste: lossCandidates now excludes any index already claimed by the eval tier before bucketing, so the loss budget isn't spent re-selecting a point the output already contains. 2. Bucketed min/max for oscillation aliasing: within the loss budget, local maxima and local minima now each get their own protected sub-budget (up to half, water-filled), the same principle already used to protect eval from loss. Without this, a genuinely alternating series could have one side of the oscillation win almost every bucket purely by array order, aliasing the retained shape into a false broad trend. Added a regression test with a 41-point strictly-alternating series asserting both a retained max and a retained min survive in the interior. 3. Advanced statistics mislabeling: rather than the larger streaming- statistics rewrite, added a small shared caption above both stats cards in LossChart's AdvancedStats explicitly stating these describe the currently retained sample, not necessarily every point ever emitted for a long run. 4. Found and fixed a real bug while tightening the null-loss extrema test: extremum detection was scanning only `lossCandidates`, which itself excludes the boundary indices (since those are handled separately), so a spike immediately adjacent to a boundary could never be compared against its true neighbor and would silently fail to be flagged as an extremum. Fixed by detecting over the full loss-bearing index range (including boundaries) while still only selecting non-boundary indices as extrema. Confirmed via direct before/after check: the previous code left the reported test case's spike (step 3) undetected, surviving only by incidental array-order luck in the plain-filler tier; the fix genuinely detects it. 5. Tightened the null-loss-adjacency test itself so it depends on real extremum detection (a single, contested budget slot with the spike deliberately not first in array order among candidates) rather than incidentally passing via generic filler regardless of whether extrema detection works. 6. Updated both docs to note that eval-loss statistics can also become non-representative once eval points exceed roughly half the compaction target (the new per-series budget split caps eval's guaranteed-full-retention share at half, not the whole budget as the previous wording implied). CI/CodeQL showing `action_required` with no jobs is a workflow- approval setting on the repo side (first-time/outside-contributor workflow runs need a maintainer to approve them in the Actions tab), not something addressable from this branch. Verified with `mint validate`. Full studio-app test suite passes (221/221). Full Studio E2E suite passes (9/9). pnpm build, typecheck, lint, format:check, check:no-em-dash all pass repo-wide.
|
Hi @Nicolas0315 , thanks for the detailed follow-up. Just to confirm, this review was against c862527, one commit behind — items 1, 2, and the main correctness concern in 3 (repeated-compaction decay, same-step split frames, eval starving training) were already fixed in c7134bf before this comment came in. Here's what this new commit (94cf5e3) addresses from the rest of your list: 3 (overlap waste specifically): Fixed. lossCandidates now excludes anything already claimed by the eval tier before extrema/plain candidates are even built from it, so the loss budget doesn't get spent re-selecting a point already in the output. 5 (bucketed min/max for oscillation): Fixed. Local maxima and minima each now get their own protected sub-budget within the loss series' share (half each, water-filled), the same principle as the eval/loss split one level up. Added a regression test with a 41-point strictly-alternating series asserting both a max and a min survive in the interior, not just whichever side won by array order. Tighten the null-loss extrema test: Done, and in the process I actually found a real bug: extremum detection was scanning only the boundary-excluded candidate list, so a spike sitting immediately next to a boundary could never be compared against its true neighbor and would silently go undetected. Confirmed this concretely, before the fix, the existing test's spike only survived by incidental array-order luck in the plain-filler tier, not genuine detection. Fixed detection to use the full loss-bearing range (including boundaries) for neighbor comparisons, while still only selecting non-boundary indices. Rewrote the test so the spike is deliberately not first in array order among candidates and only one contested slot is available, so it now genuinely fails if extrema detection is removed. Eval-stats doc update: Done in both languages, though worded as "roughly half the compaction target" rather than a fixed 1,000-point figure, since that's what the code actually enforces (half of targetSize, whatever that happens to be at the time), not a fixed absolute number. 4 (Advanced stats mislabeling): Addressed, but via a middle path rather than either option you offered. I didn't implement independent streaming statistics, and I didn't literally rename each metric label (Mean loss, Std dev, Variance, p90, p95). Instead I added one shared caption above both stats cards stating they describe "the currently retained sample, not necessarily every point ever emitted for a long run." If you'd like the individual labels renamed too, or think the full streaming-stats approach is worth the larger change, happy to take another pass, just let me know which direction you'd prefer. Full studio-app suite passes (221/221), Studio E2E passes (9/9), build/typecheck/lint/format all green, docs validated with mint validate. One more thing outside my control: CI/CodeQL show action_required with no jobs, looks like it needs a maintainer to approve the workflow run in the Actions tab before they'll execute. |
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In `@packages/studio-app/src/lib/lossDownsample.ts`:
- Around line 163-193: Update the extrema-selection logic around bucketSelect to
allocate initial maximum and minimum shares, then transfer any unused maximum
share to minima and any unused minimum share to maxima before calculating
plainBudget. Ensure both extrema categories can consume the full available loss
budget when candidates exist, while preserving the existing candidate-count
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packages/studio-app/src/lib/lossDownsample.ts (1)
135-146: Return unused loss capacity to evaluation loss.
evalBudgetnever grows after the loss budget is capped. If no interior training-loss points exist,compactLossPoints(points, 1000)retains only the evaluation-loss share plus boundaries even when more evaluation-loss points fit.Reallocate unused training-loss capacity to
evalCandidatesbefore selection.
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| let b = span === 0 ? 0 : Math.floor(((step - firstStep) / span) * budget); | ||
| if (b >= budget) b = budget - 1; | ||
| if (b < 0) b = 0; | ||
| winner[b] ??= idx; |
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When maxima or minima outnumber their sub-budget, winner[b] ??= idx retains the first candidate in each bucket rather than the most significant one. For a 2,001-point alternating series whose maxima are 1 except for a spike of 100 at step 3, compaction to 1,000 places maxima at steps 1 and 3 in the same bucket, keeps step 1, and drops the spike, leaving a displayed maximum of 1. Select the greatest maximum and least minimum within each bucket so the compaction does not erase exactly the loss spikes it is intended to preserve.
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…lit (arkorlab#215) Fresh bot review on the previous commit (94cf5e3) caught two more real issues, both confirmed by direct simulation before being fixed: 1. bucketSelect kept the first candidate seen in a bucket regardless of magnitude, so a modest local extremum could win a bucket over a genuinely severe spike that happened to sit later in the same bucket, silently erasing exactly the kind of point this preservation exists for. Confirmed: a modest max (loss 2) beat a severe spike (loss 100) in the same bucket under the old logic. Fixed by adding an optional `isBetter` comparator to bucketSelect: the max tier now keeps the highest loss value per bucket, the min tier the lowest. Eval and plain-filler tiers keep first-seen behavior (no single candidate is more "significant" there). 2. The min/max split within the loss budget (and the eval/loss split one level up) only flowed unused budget in one direction: max got up to half, min got whatever was left over, and any min-side slack went only to plain filler, never back to max. Confirmed: 20 abundant max candidates and 1 scarce min candidate left max capped at half the budget even though max could have used the rest. Fixed with a new splitBudget() helper used for both the eval/loss split and the min/max split: each side gets up to half, and whichever side has fewer candidates than its half hands its unused share back to the other (bounded by how many candidates it actually has), rather than a hard half-cap regardless of demand. Testing: added two regression tests, one confirming the most severe spike in a bucket survives over a modest one seen first, one confirming an abundant max series gets more than half the loss budget when the paired min series is scarce. Both verified against a before/after simulation of the described scenarios. Full studio-app test suite passes (223/223). Full Studio E2E suite passes (9/9). pnpm build, typecheck, lint, format:check, check:no-em-dash, and mint validate all pass repo-wide.
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In `@packages/studio-app/src/lib/lossDownsample.test.ts`:
- Around line 236-274: Update both regression tests to exercise the intended
selection conflicts: in the severe-spike test, call compactLossPoints with
target size 3 so the two maxima compete for one interior slot; in the
budget-reallocation test, give the minimum frames finite evalLoss values so they
are excluded from evaluation selection, count only representatives with loss
exactly 100, and assert that more than 10 maximum representatives remain.
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packages/studio-app/src/lib/lossDownsample.ts (1)
19-41: LGTM!Also applies to: 61-218, 220-287, 301-315
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| existing.evalLoss = p.evalLoss; | ||
| } | ||
| } | ||
| return [...byStep.values()]; |
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When a long run has already compacted and the stream later re-emits an older step that was previously discarded—for example, after an EventSource replay—Map inserts that step at the end rather than in step order. If this frame pushes the array over the cap, the helper treats the old step as lastStep, computes an invalidly narrow span, and buckets nearly all newer points together, potentially dropping the actual end of the run; subsequent output also violates the stated sorted-by-step invariant because indices, not steps, are sorted. Sort the merged values by step before choosing boundaries and buckets.
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| const evalSelected = bucketSelect( | ||
| merged, | ||
| evalCandidates, | ||
| evalBudget, | ||
| firstStep, |
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When the eval series exceeds its allocation, this selection keeps the first eval point in each bucket without considering its value, so a significant eval-loss spike can disappear at the first compaction. For example, with 2,001 eval-only points and a spike at step 1,999, the 998-bucket pass places steps 1,998 and 1,999 in the final bucket and retains the ordinary value at 1,998; the previous tail slice would have retained the recent spike. Detect eval extrema or choose the most significant eval representative per bucket so compaction does not erase anomalies from the second plotted series.
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…orlab#215) CodeRabbit, Codex, Sentry, and Greptile independently converged on the same bug in the previous commit: trainRunning / evalRunning are React state, but JobDetail is reused across job routes rather than remounted (a single existing jobId-keyed effect already resets events/points/terminal/etc. for exactly this reason). That reset effect didn't touch the two new accumulators, so navigating directly from one job to another kept accumulating the new job's values on top of the previous job's, corrupting count/mean/variance/CI/percentiles for whichever job is currently being viewed. This is a more severe regression than the caveat-caption approach it replaced: a caveat is merely imprecise, but blended cross-job statistics are actively wrong. Fixed by resetting both accumulators (via createRunningStats()) in the same per-jobId reset effect that already clears points/events/ terminal status, so a fresh accumulator starts empty for each newly viewed job. Also fixed a related but separate issue Codex flagged on the same commit: AdvancedStats' caption text was left over from before this change and still claimed all stats "describe the currently retained sample," which is only true for the summarize()-from-points fallback path, not the finalizeRunningStats() path JobDetail actually uses. LossChart now passes whether it's using full-run accumulators, and the caption reflects whichever is actually active. Full studio-app test suite passes (241/241, unchanged: this is a navigation-lifecycle and copy fix, not a change to any already-tested pure-function behavior). Full Studio E2E suite passes (9/9). pnpm build, typecheck, lint, format:check, check:no-em-dash all pass repo-wide.
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| if (safeLoss !== null) { | ||
| setTrainRunning((prev) => { | ||
| const next = { ...prev, reservoir: [...prev.reservoir] }; | ||
| updateRunningStats(next, safeLoss); |
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When an EventSource reconnect replay re-delivers a training.log event—a scenario the new downsampling code explicitly anticipates—the chart eventually merges the repeated step, but these accumulators unconditionally add the loss again. The Advanced panel then inflates n and biases its mean, variance, confidence interval, and reservoir percentiles even though the chart contains only one logical observation; track delivered SSE event IDs or otherwise exclude replayed values before updating both accumulators.
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…ates (arkorlab#215) CodeRabbit, Sentry, Codex, Greptile, and cubic all independently converged on the same root cause underlying the accumulator-reset fix from the previous commit: closing an EventSource in a cleanup function stops future events from being dispatched, but it doesn't retroactively cancel a handler that was already invoked or already queued in the event loop before cleanup ran. Since JobDetail is reused across job routes rather than remounted, a training.log (or any other) frame from the previous job that's in flight at the moment of navigation could still fire its handler after the per-job reset effect has already run, reapplying that stale job's data (points, running stats, events, terminal status, live status) on top of the newly reset state for the new job. Fixed with the same `cancelled` flag pattern this file already uses in its other per-jobId effect (the /api/jobs polling one): a boolean set to true in this effect's cleanup, checked at the top of every SSE listener before it does anything. This closes the gap for all of the effect's listeners (training.started, training.log, checkpoint.saved, training.completed, training.failed, error), not just the training.log handler that touches points/stats, since the same stale-closure mechanism could equally let terminal status or live status leak across jobs. Also addressed two smaller issues from the same review round: - cubic: createRunningStats() now rejects a non-positive or non-integer reservoirSize with a RangeError, rather than silently producing an accumulator whose reservoir can never hold a sample (which would make finalizeRunningStats always return NaN for p90/p95 on an otherwise-valid run). Added a regression test. - cubic: the Advanced-panel caption previously combined trainRunning/evalRunning presence with a single OR, so if a caller ever supplied only one of the two, the caption would incorrectly claim both series use full-run statistics. Changed to two separate booleans (one per series) with an explicit mixed-case caption for when they differ. Not currently reachable from JobDetail's actual usage (it always supplies both), but this makes the component correct independent of how any future caller might use it. Not addressed in this commit, and I don't think it should be: a separate Codex comment on the same round noted that a genuinely replayed SSE frame (not a stale-job frame, but a legitimate re- delivery of an already-seen step for the *same* job) would still be double-counted by the running-stats accumulators. This is the same class of already-documented, accepted tradeoff as appendLossFrame's existing non-adjacent-duplicate limitation: a single duplicate point skews a potentially-large accumulator negligibly, and closing it would require event-id-based dedup infrastructure disproportionate to that impact. Full studio-app test suite passes (242/242). Full Studio E2E suite passes (9/9), including the existing SSE-stream test. pnpm build, typecheck, lint, format:check, check:no-em-dash all pass repo-wide.
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| // `points` for rendering. | ||
| if (safeLoss !== null) { | ||
| setTrainRunning((prev) => { | ||
| const next = { ...prev, reservoir: [...prev.reservoir] }; |
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For long or high-frequency runs, these updates clone a reservoir of up to 2,000 numbers on every training.log frame—even while Advanced metrics are hidden—and do so twice when both loss series are present. Since setPoints already copies its bounded chart array, this adds hundreds of millions of element copies and substantial main-thread allocation over a 50,000-step run, undermining the retention cap's performance goal. Keep the streaming accumulators in mutable refs and snapshot them only when rendering the Advanced panel, or otherwise avoid copying the full reservoirs per event.
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…er-frame clones (arkorlab#215) Codex flagged (correctly) that JobDetail's setTrainRunning/setEvalRunning updaters cloned the full reservoir array (up to 2,000 entries) on every single training.log frame, regardless of whether that frame would actually touch the reservoir. Over a long or high-frequency run this adds substantial unnecessary allocation, undermining the point of having a retention cap in the first place. Rather than patch around this in JobDetail, fixed it at the source by redesigning updateRunningStats to be pure: it now returns a new RunningStats rather than mutating its input, and internally only clones the reservoir array in the two cases where it actually changes (still filling up, or the comparatively rare Algorithm-R hit once full). On the common path once a long run's reservoir is full, the array reference is reused untouched, so most frames do no reservoir allocation at all, the inverse of the previous every-frame-clone behavior. This also closes a latent correctness risk Sentry's stale-comment catch led me back to: the previous version mutated the previous state's reservoir array in place inside a setState updater. React requires updater functions to be pure (Strict Mode can invoke them twice specifically to catch this), so mutating shared previous state there was never actually safe, even though it happened to work in the single-invocation case our tests exercise. The pure redesign removes this risk categorically rather than patching around it. JobDetail's call sites are simpler as a result: `setTrainRunning((prev) => updateRunningStats(prev, safeLoss))`, no manual spread/clone needed. Also fixed the stale doc comment Sentry caught: it still described trainRunning/evalRunning as refs, left over from before the earlier react-hooks/refs lint fix that switched them to state. Testing: updated the 4 existing call sites (stats.test.ts) to use the new return-based API; all continue to pass, confirming the refactor preserves existing behavior exactly. Added a new test that mocks Math.random to deterministically force both a reservoir "miss" (array reference reused, not just equal) and a "hit" (new array returned, original left completely untouched), directly verifying both the purity guarantee and the performance characteristic this refactor is for. Full studio-app test suite passes (243/243). Full Studio E2E suite passes (9/9), including the existing SSE-stream test. pnpm build, typecheck, lint, format:check, check:no-em-dash all pass repo-wide.
|
Hi @Nicolas0315 , this review was against
Also done: the null-loss extrema test was tightened to genuinely fail if extrema detection is removed (found and fixed a real detection bug in the process — it was only scanning the boundary-excluded candidate list), and the docs state that eval stats can shift once eval points exceed roughly half the compaction target. Full studio-app suite passes (243/243), Studio E2E passes (9/9), build/typecheck/lint/format/no-em-dash all green. Still outside my control: CI/CodeQL show Let me know if anything above needs another pass. |
…g stats (arkorlab#215) Greptile (twice, across two consecutive rounds) and Codex independently converged on a real gap: when a later training.log frame corrects an already-non-null loss or evalLoss for the same step, appendLossFrame correctly merges it into a single chart point (its later-non-null-wins semantics), but setTrainRunning/setEvalRunning had no awareness of this and added the corrected value as a second sample regardless. Advanced-panel stats would then reflect an inflated count and a mean/variance/CI/percentile sample polluted by both the original and corrected values, for a chart that only ever displays the correction. Fixed by tracking the same (step, loss, evalLoss) merge state appendLossFrame computes, and skipping a stats update specifically when this frame's field was already non-null for the same step (a correction), while still updating it normally when the field is newly non-null for that step (the ordinary split-frame case, e.g. loss arriving separately from evalLoss) or the frame is for a new step entirely. The chart merge via appendLossFrame is unaffected either way; only the stats-accumulator side of the update is skipped for the correction case. First implementation read `points.at(-1)` directly inside the SSE handler to answer "was this step just corrected", but oxlint's exhaustive-deps caught that this reads component state inside an effect scoped to [jobId] only, so it would see whatever `points` was when the effect last ran, not its current value: a real bug, not a false positive from the linter. Fixed by tracking the same merge state in a ref instead (safe here since it's read/written only inside an event handler, not during render), reset alongside the other per-job state in the existing jobId reset effect. No new exported/testable surface added for this one: the fix is a small amount of inline logic directly mirroring appendLossFrame's own already-extensively-tested merge semantics, and extracting it into a new function right now would mean more new surface for the next review round rather than less. Full studio-app test suite passes (243/243, unchanged: this fix doesn't touch any already-tested pure function). Full Studio E2E suite passes (9/9), including the SSE-stream test. pnpm build, typecheck, lint, format:check, check:no-em-dash all pass repo-wide.
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1 issue found across 1 file (changes from recent commits).
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<file name="packages/studio-app/src/pages/JobDetail.tsx">
<violation number="1" location="packages/studio-app/src/pages/JobDetail.tsx:296">
P2: When a step is re-emitted after another training step, this check misses the duplicate because `lastFrameRef` only stores the immediately preceding frame. The chart merges the duplicate and displays only the replacement, but the running stats count both values. Track counted loss and eval fields by step rather than only the last frame.</violation>
</file>
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| // appendLossFrame below still performs) specifically when | ||
| // this frame's field was already non-null for this same step. | ||
| const last = lastFrameRef.current; | ||
| const isSameStep = last?.step === step; |
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P2: When a step is re-emitted after another training step, this check misses the duplicate because lastFrameRef only stores the immediately preceding frame. The chart merges the duplicate and displays only the replacement, but the running stats count both values. Track counted loss and eval fields by step rather than only the last frame.
Prompt for AI agents
Check if this issue is valid — if so, understand the root cause and fix it. At packages/studio-app/src/pages/JobDetail.tsx, line 296:
<comment>When a step is re-emitted after another training step, this check misses the duplicate because `lastFrameRef` only stores the immediately preceding frame. The chart merges the duplicate and displays only the replacement, but the running stats count both values. Track counted loss and eval fields by step rather than only the last frame.</comment>
<file context>
@@ -260,17 +275,38 @@ export function JobDetail({ jobId }: { jobId: string }) {
+ // appendLossFrame below still performs) specifically when
+ // this frame's field was already non-null for this same step.
+ const last = lastFrameRef.current;
+ const isSameStep = last?.step === step;
+ const lossAlreadyCounted = isSameStep && last.loss !== null;
+ const evalLossAlreadyCounted = isSameStep && last.evalLoss !== null;
</file context>
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Inline comments:
In `@packages/studio-app/src/pages/JobDetail.tsx`:
- Around line 297-304: Update the running-statistics logic in the JobDetail
loss-processing flow so a finite loss or evalLoss for an existing step replaces
the previously accumulated sample instead of being discarded. Keep the chart and
accumulator consistent by updating the corresponding reservoir/statistics state
by step identity, covering both trainRunning and evalRunning while preserving
normal accumulation for new steps.
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packages/studio-app/src/pages/JobDetail.tsx
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| if (safeLoss !== null && !lossAlreadyCounted) { | ||
| setTrainRunning((prev) => updateRunningStats(prev, safeLoss)); |
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When two adjacent frames for the same step both carry a value, this condition suppresses the second value from the accumulator while appendLossFrame overwrites the chart point. For example, a correction from loss 100 to 150 leaves the chart showing 150 but the Advanced statistics still include 100; the same applies to evalLoss. Avoiding a second count is correct for per-step statistics, but the existing contribution must also be replaced.
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…skip them (arkorlab#215) CodeRabbit and Codex both flagged (correctly) that the previous fix went too far in the wrong direction: skipping a stats update entirely when a later frame corrects an already-non-null field for the same step avoids double-counting, but it also means the correction is never reflected in stats at all. mean/variance/CI would stay permanently pinned to the pre-correction value even though the chart (via appendLossFrame) already shows the corrected one, indefinitely. Fixed properly by adding correctRunningStats to stats.ts: it reverses the specific contribution of the old value via a reverse-Welford formula (verified directly against a from-scratch batch computation before implementing, given how many subtle stats-math mistakes this kind of change has produced in earlier rounds), then re-applies the corrected value through the existing updateRunningStats. This restores mean/variance/CI to exactly what they'd be had the corrected value been added in the first place, without touching count (net effect: one value replaced, not two counted). The reservoir sample used for p90/p95 is deliberately left as-is; reliably locating which slot, if any, a specific historical value occupies isn't possible without per-sample bookkeeping, and percentiles are already documented as an estimate rather than an exact figure, so a single stale reservoir entry after a correction is a materially smaller imprecision than mean/variance/CI silently drifting forever, which this now fixes exactly. cubic separately flagged that this only detects a correction to the immediately-previous frame's step (not an arbitrary earlier one) - this is intentional and matches appendLossFrame's own O(1), last-entry-only scope exactly, and is the same accepted-tradeoff boundary already documented on that function; not chasing it further here for the same reasons as before. Testing: added 2 stats.test.ts cases verifying correctRunningStats matches a from-scratch batch computation with the value replaced, and that it leaves count unchanged (unlike two separate update calls would). Full studio-app test suite passes (245/245). Full Studio E2E suite passes (9/9), including the SSE-stream test. pnpm build, typecheck, lint, format:check, check:no-em-dash all pass repo-wide.
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| newValue: number, | ||
| ): RunningStats { | ||
| return updateRunningStats( | ||
| removeMostRecentRunningStat(stats, oldValue), |
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When an adjacent same-step frame corrects a value while the reservoir is still filling, removeMostRecentRunningStat retains the old reservoir and this call appends the replacement, so the reservoir contains both values and becomes longer than the logical sample count. Repeated provisional-value corrections can severely corrupt p90/p95—for example, repeatedly emitting 100 and immediately correcting it to 0 leaves exact mean 0 but a reservoir dominated by both 100 and 0. Update or remove the old reservoir contribution when applying a correction instead of retaining it and adding another entry.
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| @@ -74,19 +100,42 @@ export function JobDetail({ jobId }: { jobId: string }) { | |||
|
|
|||
| useEffect(() => { | |||
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When a user navigates directly from job A to job B, React reuses JobDetail and first renders it with the existing state because this effect resets that state only afterward, causing job B to briefly display job A's loss curve, Advanced statistics, events, status, and metadata.
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Path: packages/studio-app/src/pages/JobDetail.tsx
Line: 101
Comment:
**Previous job state flashes**
When a user navigates directly from job A to job B, React reuses `JobDetail` and first renders it with the existing state because this effect resets that state only afterward, causing job B to briefly display job A's loss curve, Advanced statistics, events, status, and metadata.
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For each issue above, determine whether it is valid and should be fixed. If so, fix it directly.…unt JobDetail per job (arkorlab#215) Two independent, real issues from this round: 1. Codex and cubic both flagged that correctRunningStats (added last commit) correctly restores mean/variance/CI via reverse-Welford, but left the reservoir untouched, reasoning a single stale entry was an acceptable imprecision. That reasoning held for the already-full reservoir case, but missed a worse sub-case: while the reservoir is still filling up (below its size limit), the old value stays in the array *and* the corrected value gets pushed as an *additional* entry (since the reservoir still registers as "not yet full"), leaving a single logical sample occupying two slots. Verified this exactly with a small script before touching anything, given how many subtle stats-math mistakes this area has produced. Fixed by having correctRunningStats detect this case directly: if the reservoir was still filling when the old value was added, that value is provably sitting at the reservoir's last index (pushes always land at the end, and this only ever corrects the immediately-previous update, so nothing else has touched the array since). Replacing that slot directly, rather than routing the corrected value through updateRunningStats's normal push path, closes the gap. The already-full case is left as before (a single possibly-stale entry, not a duplicate): locating its exact slot isn't possible without extra per-sample bookkeeping, and it's a materially smaller imprecision than what this commit fixes. 2. Greptile flagged that JobDetail is reused across job routes without a key, so React renders the new job's route with the *previous* job's data still in state for one paint, before the per-jobId reset effect (which runs after render) clears it. Fixed with the standard React pattern for exactly this: key={route.id} on JobDetail in App.tsx, forcing a full remount on every job change rather than reusing the instance. This eliminates the flash categorically (a fresh mount has no stale data to flash in the first place) rather than just resetting it faster. Left the existing per-jobId reset effects in place as harmless defense in depth rather than removing them, to keep this change minimal and avoid touching more surface than the finding requires. Testing: added 2 stats.test.ts cases for the reservoir fix (filling- phase correction produces exactly one entry, not two; a multi-value scenario confirms only the corrected slot changes). The App.tsx fix is a one-line, well-established React pattern; existing Studio E2E coverage (including the SSE-stream test) continues to pass with it. Full studio-app test suite passes (247/247). Full Studio E2E suite passes (9/9). pnpm build, typecheck, lint, format:check, check:no-em-dash all pass repo-wide.
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In `@packages/studio-app/src/lib/stats.ts`:
- Around line 328-351: Track the reservoir slot selected by the latest
updateRunningStats call in RunningStats, distinguishing hits from non-hits. In
correctRunningStats, replace the selected slot with newValue when correcting a
latest sample that was inserted via an Algorithm R hit, while preserving that
slot metadata for repeated corrections of the same sample; retain the existing
append replacement behavior while the reservoir is still filling. Add a
regression test covering a forced hit and correction of the latest sample.
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| oldValue: number, | ||
| newValue: number, | ||
| ): RunningStats { | ||
| const wasStillFilling = stats.reservoir.length < stats.reservoirSize; |
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When the corrected sample is exactly the reservoirSizeth observation, stats.reservoir.length already equals stats.reservoirSize, so this condition is false even though oldValue was just appended at the final slot. The moments are corrected, but p90/p95 retain the stale value; for example, correcting an extreme 2,000th value can leave p95 nonzero when the corrected 2,000-value distribution has p95 zero. The newly added post-update < check is fresh evidence that the earlier correction issue remains at this boundary; detect whether the accumulator was filling before the original update (for example via stats.count <= stats.reservoirSize) and replace the last slot.
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All reported issues were addressed across 3 files (changes from recent commits).
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…just moments (arkorlab#215) CodeRabbit and Codex both flagged real gaps in the previous commit's "leave the already-full reservoir as-is" tradeoff for corrections, with concrete, damaging examples rather than hypothetical minor imprecision: - CodeRabbit: with a small reservoir, correcting a value that landed via an Algorithm R hit left mean/variance exactly right but the reservoir entry itself stale, so p90/p95 could report the superseded value outright rather than being mildly biased. - Codex: the "was still filling" check itself had a boundary bug - the value that completes filling the reservoir leaves reservoir.length already equal to reservoirSize, so a check made after the fact misread that case as "already full" and skipped replacing it even though it was a plain push. Both root causes trace to the same thing: correctRunningStats was trying to re-derive, after the fact, where (if anywhere) the old value landed in the reservoir, and that's fundamentally unreliable once state has moved on. Fixed by tracking it directly instead: added lastReservoirIndex to RunningStats, set by updateRunningStats to whichever index the most recent call touched (the push index while filling, the Algorithm R hit index once full, or null for a miss). correctRunningStats now just replaces that exact index when it's non-null, and correctly leaves the reservoir untouched when it's null (the old value was never actually sampled in, so there's nothing to replace). This handles the fill, hit, and miss cases uniformly and correctly, closing both the boundary bug and the stale-hit-entry gap in one change, rather than patching the old heuristic further. Testing: added 2 stats.test.ts cases translating CodeRabbit's and Codex's exact examples into regression tests (a forced Algorithm R hit via a mocked Math.random, and the exact reservoir-just-filled boundary), both verifying the reservoir now holds the corrected value rather than the superseded one. Verified the full design (fill, hit, and miss cases) with a standalone script before touching any source, given how many rounds this specific area has needed. No existing caller/test constructs RunningStats object literals directly, so adding the new field didn't require updating anything beyond the functions in this file. Full studio-app test suite passes (249/249). Full Studio E2E suite passes (9/9). pnpm build, typecheck, lint, format:check, check:no-em-dash all pass repo-wide.
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Reviewed this locally: checked out the branch in a clean worktree, ran the suites, and wrote an independent property sweep against Verdict: functionally sound, and #215 is genuinely fixed. Two small things I'd fix before merge, neither of them blocking on the algorithm itself. What I verified
Independent property sweep (my own, not the PR's tests):
1.
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Thanks @Nicolas0315 for the review and thats a good catch you found. Now lets wait for @soleil-colza |
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Fixes #215.
JobDetail.tsxcapped retained loss points atMAX_LOSS_POINTS(2000) via tail-slicing (next.slice(next.length - MAX_LOSS_POINTS)), which silently drops the earliest points once a run exceeds the cap, including the start of the loss curve, where most of the initial convergence and loss delta lives.This replaces the tail-slice with
compactLossPoints, a pure helper (packages/studio-app/src/lib/lossDownsample.ts) that compacts down to half the cap by preserving:evalLoss(sparse series, cheap to keep in full)lossseries, so visible spikes surviveOutput is always a subsequence of the input in original order, so
LossChart's sort-by-step binary-search tooltip is unaffected.Memory/render bounds are unchanged (still ≤
MAX_LOSS_POINTSat any time); only the eviction policy changed.Testing:
lossDownsample.test.tscovering boundary sizes, evalLoss preservation, extrema preservation (including a flat-line tie edge case caught during development), the sort-by-step invariant, and a simulated 50k-step long runstudio-apptest suite passes (217/217)pnpm typecheck,pnpm lint,pnpm format:check,pnpm check:no-em-dashall pass repo-wideSummary by cubic
Compacts loss-chart points by step-bucketed selection instead of tail-slicing, keeping early history under the 2,000-point cap. Advanced metrics now use full-run streaming accumulators; same-step corrections replace prior values without double-counting, and navigation between jobs remounts and resets state to prevent cross-job bleed.
packages/studio-app/src/pages/JobDetail.tsx: merges adjacent same-step frames withappendLossFrame; compacts toMAX_LOSS_POINTS / 2viacompactLossPoints; maintains per-series full-runRunningStats, replacing same-step corrections withcorrectRunningStats; guards all SSE listeners with acancelledflag; resetsRunningStatsand local merge state onjobIdchange.packages/studio-app/src/App.tsx: remountsJobDetailper job (key={route.id}) to avoid rendering stale data between navigations.packages/studio-app/src/components/jobs/LossChart.tsx: prefersfinalizeRunningStats(train|eval)over summarizing retained points; caption reflects whether full-run stats are in use; accepts optionaltrainRunning/evalRunning.packages/studio-app/src/lib/stats.ts: addsRunningStats(Welford mean/variance + Algorithm R reservoir), makesupdateRunningStatspure, trackslastReservoirIndex, and updatescorrectRunningStatsto replace the exact reservoir slot for corrections (fill/hit/miss) so p90/p95 reflect fixes; addsfinalizeRunningStats; tests cover purity, bounds, corrections, and exactness.packages/studio-app/src/lib/lossDownsample.ts: addsappendLossFrameand step-bucketedcompactLossPoints(per-series budgets, loss max/min protection, overlap reclaim, local-span buckets with backfill, severity-aware selection); tests cover compaction invariants, long-run retention, ordering, eval preservation, and collision cases.docs/studio/jobs.mdx,docs/ja/studio/jobs.mdx) clarify compaction behavior and that mean/variance/CI use the full run while p90/p95 come from a bounded representative sample.Written for commit 6652d36. Summary will update on new commits.
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New Features
Documentation