Posts tagged #classical-ml

Jul 14, 2026 · 9 min

sklears 0.2.0 Released — A Real CUDA GPU Foundation, Not Another Stub

sklears 0.2.0 ships sklears-core::gpu, a real oxicuda-backed CUDA foundation (GpuBackend, GpuArray, GpuMatrixOps) powering on-device GEMM, Cholesky/LU/QR/SVD solves, and HNSW k-NN across 9 downstream crates, plus a wide correctness sweep. 12,721 tests passing, 36 crates, >99% scikit-learn API coverage held.

releasesklearsmachine-learning

Jun 30, 2026 · 6 min

sklears 0.1.2 Released — Preprocessing Completions, SciRS2 0.5.1, and AVX2 Quicksort

sklears 0.1.2 brings 12 real preprocessing implementations (no more stubs), SciRS2 0.5.1 upgrade, AVX2 quicksort in sklears-simd, advanced categorical imputers, and a full benchmarking regression-detection subsystem — 12,242 tests across 36 crates, >99% scikit-learn API coverage held.

releasesklearsmachine-learning

Apr 27, 2026 · 5 min

sklears 0.1.1 Released — Correctness Fixes for HDBSCAN, Streaming, and Pipelines

sklears 0.1.1 is a correctness patch for pure-Rust scikit-learn: HDBSCAN cluster-persistence fix, streaming scaler/imputer Default cleanups, a pipeline lifetime fix, and serialization fixes — 11,586+ tests across 36 crates, >99% scikit-learn API coverage held.

releasesklearsmachine-learning

Mar 20, 2026 · 6 min

sklears 0.1.0 Released — Pure Rust scikit-learn with >99% API Coverage

The first stable release of sklears: a pure-Rust scikit-learn alternative spanning 36 crates with >99% API coverage, type-safe Untrained→Trained state machines, and a SciRS2 foundation. No Python runtime, no GIL, no C or Fortran.

releasesklearsmachine-learning