v0.6.0

Added

• New @mushin.sweep decorator: turn a plain task-style function into a runnable sweep with no subclassing — experiment.run(lr=multirun([...]), seed=multirun([...])) returns the labeled xarray.Dataset directly. Drop to experiment.workflow (the last-run MultiRunMetricsWorkflow) or experiment.workflow_cls for power features; mushin_resume and all run() resilience options carry through. The MultiRunMetricsWorkflow subclass form is unchanged.
• Resumable sweeps now survive a hard process kill (OOM, SLURM preemption): each cell records its completion durably from inside its own job, so resume=True never recomputes finished cells. A task may also declare a mushin_resume parameter to receive a ResumeContext (the cell's directory, is_resume, and the last checkpoint) and resume its own training mid-run.
• Sweeps can now use out-of-process Hydra launchers (e.g. hydra-joblib-launcher, submitit): per-cell dispatch is stdlib-picklable, so run(..., launcher="joblib") parallelizes across worker processes. Previously any process-backed launcher failed with a PicklingError. Resilience, resume, and provenance semantics are unchanged in-process and preserved out-of-process.

Changed

• Provenance capture no longer spawns git subprocesses per sweep cell. The sweep-constant part of the record (git state + package versions — three git subprocesses via _git()) is now captured once per run() and reused for every cell, instead of being recomputed per cell. An N-cell sweep now spawns 3 git subprocesses instead of 3N (e.g. ~30s less git overhead on a 1000-cell sweep). Each cell's mushin_provenance.json is byte-for-byte equivalent (only timestamp/config vary per cell, as before).
• import mushin is now ~65% faster (~1.7s → ~0.6s on a cold import): the Lightning integration (HydraDDP, MetricsCallback) and its heavy pytorch_lightning dependency (which also transitively pulled in matplotlib and scipy) now load lazily on first attribute access instead of at import time. The sweep → xarray core no longer pays for Lightning it never uses. All public names resolve unchanged; _tuning/Study still work (they import pytorch_lightning only inside functions).

Fixed

• Sweeps no longer emit Hydra's "Future Hydra versions will no longer change working directory at job runtime" deprecation warning: MultiRunMetricsWorkflow.run now sets hydra.job.chdir=True explicitly (the behavior the workflow already relies on).

Misc

• Added six runnable EQUINE-style example notebooks (sweeps, compare + batteries, Study, resilient sweeps, LLM evaluation, scikit-learn) under an "Example notebooks" section of the docs, executed in CI via nbmake so they stay current.
• CI: a batteries-clean-install job builds the wheel, installs it (non-editable) with the detection/image/audio extras into a fresh env, and runs all 7 battery examples against it — catching packaging / optional-extra issues the editable test job would miss.
• Documentation: a Built-in batteries guide covering all 7 benchmark batteries (classification, segmentation, detection, regression, retrieval, image_quality, audio) with real-model recipes and CI-tested runnable toys.
• Documentation: add a top-level Examples page indexing all runnable example scripts (sweep_to_dataset, sklearn_sweep, compare_classifiers, study_mnist, segmentation_demo, compare_llms_demo, batteries), each with a one-line description and a link, plus a pointer to the batteries guide.
• Documentation: add sweep resilience + provenance (0.5.0) to the README "What it provides" list.
• Documentation: the Built-in batteries guide now shows real captured outputs under every battery, plus a flagship notebook-style walkthrough (comparing two classifiers) with the actual summary table, significant p-values, and interpretation.