autotrainer 0.18.0

[0.18.0] - 2026-10-22

Added

• Support for dictionary and tuple batches in autotrainer.train(): PyTorch training now natively parses complex batch structures (e.g. HuggingFace datasets yielding {"input_ids": ..., "labels": ...} or multi-input tuples (x1, x2)) via split_xy() and forwards them cleanly to model forward methods via robust_forward().
• DDP model.no_sync() optimization in autotrainer.accumulate(): Intermediate micro-batches during gradient accumulation now bypass redundant cross-GPU gradient all-reductions using model.no_sync(). An explicit sync: bool | None = None parameter on _Accumulator.backward() allows callers to force synchronization on tail micro-batches when dataset lengths are not multiples of accumulation steps.
• CI example validation and linting: Added examples/ to GitHub Actions workflow for ruff check and ruff format --check, keeping all runnable example scripts strictly linted.

Fixed

• Keras EarlyStopping without validation data: When training without validation data, autotrainer.train() now sets EarlyStopping(monitor="loss") instead of the default val_loss, eliminating noisy runtime warnings and ensuring early stopping triggers reliably.
• Scikit-learn 1.8+ LogisticRegression deprecation warning: Setting n_jobs on LogisticRegression (and any custom subclasses evaluated via MRO) is now skipped in sklearn_backend.prepare(), preventing deprecation FutureWarning while preserving multithreading configuration on all other estimators (e.g., RandomForestClassifier).
• Robust batch unpacking in train(): Replaced hardcoded (xb, yb) unpacking with split_xy(batch) across dataloader iterations, preventing ValueError or key-iteration string errors on non-2-tuple dataloaders.