5 posts
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.
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.
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.
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.
SciRS2 is a pure-Rust SciPy/NumPy/scikit-learn replacement, and 0.3.0 is the biggest release yet — transformers, GNNs, diffusion, MoE/RLHF, Gaussian processes, MCMC, survival analysis, radar/compressed sensing, LOBPCG/AMG, plus new Julia and Python bindings. 19,644 tests, ~2.59M lines of Rust. No C, no Fortran.