Changelog

A summary of MemScale releases. The authoritative, always-current changelog lives at memscale.id/changelog.

v1.2.0 — Unreleased — “Intelligence Foundation”

The v1.2 cycle builds the data and framework foundation for an ML-trained optimization policy:

• A two-stage optimization flow — Stage 1 strategy selection, Stage 2 the existing rule engine. Opt-in via Config.auto_policy=True.
• Expanded opt-in v2 telemetry: bucketed, non-identifying model and decision metadata to serve as a training corpus.

v1.1.0 — 2026-05-17 — “The Performance Release”

The first release with empirically-backed, reproducible benchmark numbers committed to the codebase.

• Experimental async CPU offload — wrap(model, async_offload=True) enables a tier-aware async offload engine. Default off; existing users unaffected. Also settable via MEMSCALE_ASYNC_OFFLOAD=1.
• Benchmark CLI — python -m memscale.benchmarks, a reproducible VRAM benchmark suite over a registry of standard models.
• The Hugging Face Trainer integration now reports real samples/sec throughput (previously hard-coded to 0.0).
• estimate_savings() now defaults to an honest same-GPU cost comparison.

v1.0.x — May 2026

• v1.0.4 (2026-05-15) and v1.0.3 (2026-05-15) — stability and correctness fixes, including auto-disabling HuggingFace use_cache when activation checkpointing is enabled, to avoid a recompute shape mismatch.
• v1.0.2 (2026-05-12) — no API key required; the library works fully offline. Optional anonymous opt-in telemetry introduced.
• v1.0.1 (2026-05-09) — post-launch fixes.
• v1.0.0 (2026-05-09) — Library Launch.

Earlier (0.x)

• v0.3.0 (2026-04-26) — “Production Foundations.”
• v0.2.0 (2026-04-26).
• v0.1.0 (2026-04-26) — Initial public release.

Versioning

MemScale follows Semantic Versioning. The public API surface — wrap, optimize, detach, Config, OptimizationMode — is what semantic-versioning guarantees apply to; internal modules may change between releases.