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docs: add "How training works" page#26

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divyasinghds wants to merge 6 commits intodevelopfrom
docs/how-training-works
Open

docs: add "How training works" page#26
divyasinghds wants to merge 6 commits intodevelopfrom
docs/how-training-works

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@divyasinghds
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@divyasinghds divyasinghds commented May 7, 2026

Summary

  • Adds tools-help/how-training-works.mdx — a transparency page that walks through what the tracebloc client does to your data and model in each of the nine supported use cases.
  • Each accordion covers: input format, preprocessing (incl. split strategy), training/validation step, default loss/optimizer, cycle-level metrics with the underlying library calls (sklearn, torchmetrics, lifelines), and inference output.
  • Closes with a 6-step "reproduce locally" checklist so an evaluating user can run the same pipeline on the same data and compare metrics number-for-number.
  • Wired into the Tools & Help group in docs.json.

Replaces #25 (renamed from "pipeline reference" → "How training works"; rebased onto develop).

Test plan

  • mint dev renders the page and the new nav entry appears under Tools & Help
  • All nine accordions expand correctly
  • mint broken-links passes
  • Spot-check by an owner of core/metrics/ and core/domains/ in tracebloc-client that the per-use-case metric lists and loss formulas are accurate (notably segmentation boundary metrics and the Cox loss description)

🤖 Generated with Claude Code


Note

Low Risk
Low risk documentation-only changes; main risk is incorrect or out-of-date training/metric descriptions misleading users, not runtime behavior.

Overview
Adds a new join-use-case/how-training-works.mdx doc that подробно describes the platform’s training/inference pipeline and per-use-case preprocessing, loss/optimizer behavior, metrics, and inference outputs, plus guidance for reproducing runs locally.

Wires the new page into the Join a Use Case navigation in docs.json, explicitly ignores .github/ in .mintignore to prevent Mintlify dev server MDX parsing issues, and cleans up a minor formatting/encoding issue at the start of evals.json.

Reviewed by Cursor Bugbot for commit 6eb7094. Bugbot is set up for automated code reviews on this repo. Configure here.

Documents the training and inference pipeline for all nine supported
use cases so a user evaluating tracebloc can reproduce a run locally
and compare metrics against what the platform reports.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
@divyasinghds divyasinghds self-assigned this May 7, 2026
@LukasWodka
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👋 Heads-up — Code review queue is at 12 / 8

Above the WIP limit. The team convention is to review existing PRs before opening new work.

Open PRs currently in Code review (oldest first):

Pull from review before opening new work. (This is a nudge from the kanban WIP check, not a block.)

divyasinghds and others added 3 commits May 7, 2026 15:39
The file started with U+200B (UTF-8 e2 80 8b) before the opening
bracket, which broke JSON parsing and caused mint dev to fail with a
YAML parser error.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Lives between hyperparameters and model optimization, where users are
already configuring a run and want to understand what the platform
does with their model and data.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
mint dev was parsing .github/pull_request_template.md as MDX and
failing on the HTML comments. The file claims .github is auto-ignored
but some CLI versions still scan it; listing it explicitly is harmless
and unblocks local preview.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Expand the page from a high-level overview into a reference users can
match runs against. Each of the nine supported use cases (image
classification, object detection, semantic segmentation, keypoint
detection, text classification, tabular classification, tabular
regression, time series forecasting, time-to-event prediction) now has
a consistent plain-English breakdown of preprocessing, train/val split,
training step, validation step, cycle metrics, and inference output —
including the platform-side defaults and reproduction-load-bearing
details (mask handling for SS, OD-vs-YOLO image-size pinning,
augmentation pipeline behavior, frozen-in-cycle-1 preprocessing state,
scaled-vs-original-target metric scales, etc.).

Also adds a shared "Experiment parameters" table grounded in the SDK's
actual starting defaults (SGD, lr=0.001, batch_size=16, epochs=10,
dynamic per-dataset validation_split) and a tightened
"Reproducing a run locally" checklist.

.mintignore: consolidate the .github/ entries into a single block so
the dev server stops tripping over GitHub PR-template HTML comments.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Clarify that reproducing a tracebloc run locally — even with everything
matched — will produce small numerical variation, with the major
sources called out: hardware/CUDA differences, GPU non-determinism,
library versions, data-loader worker timing, federated averaging
between cycles, stateful layer behavior, and mixed-precision rounding.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2 participants