docs: add "How training works" page#26
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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>
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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.) |
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>
This was referenced May 7, 2026
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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>
3 tasks
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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Summary
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.sklearn,torchmetrics,lifelines), and inference output.docs.json.Replaces #25 (renamed from "pipeline reference" → "How training works"; rebased onto develop).
Test plan
mint devrenders the page and the new nav entry appears under Tools & Helpmint broken-linkspassescore/metrics/andcore/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.mdxdoc 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 Casenavigation indocs.json, explicitly ignores.github/in.mintignoreto prevent Mintlify dev server MDX parsing issues, and cleans up a minor formatting/encoding issue at the start ofevals.json.Reviewed by Cursor Bugbot for commit 6eb7094. Bugbot is set up for automated code reviews on this repo. Configure here.