11 posts
ToRSh 0.2.0 completes autograd backward coverage, consolidates GPU compute onto the pure-Rust oxicuda stack, ships full NumPy/pandas/SciPy interop and PyTorch-compatible LR schedulers, and replaces fabricated success paths with honest errors. 10,638 tests passing. The sovereign deep-learning layer for COOLJAPAN.
TenfloweRS 0.2.0 rewires the Python-facing, PyTorch-style implicit autograd system for real: every layer type gets tape-backed .backward()/.grad(), all 9 optimizers perform genuine gradient updates, and a correctness sweep fixes wrong Softmax/BatchNorm/GroupNorm/LayerNorm gradients — 14,536+ tests passing.
TenfloweRS 0.1.2 adds PyTorch-style implicit autograd for Python, real ONNX protobuf import/export, a pure-Rust Blosc codec for Zarr, and a wide honesty-hardening sweep that replaces fabricated results with real computation or honest errors across the framework.
ToRSh 0.1.3 lands the oxicuda GPU backend (no CUDA SDK at build time), eliminates the last C/asm dependency, ships a bandwidth-optimal ring all-reduce, completes the Node.js N-API layer, and delivers 15–30% throughput gains from phase-4 chunking helpers.
The pure-Rust NVIDIA CUDA Toolkit replacement adds nine new GPU deep-learning crates — generative diffusion, graph neural nets, Mamba SSMs, vision transformers, audio/speech, time-series, Bayesian DL, federated learning, and NAS — growing to ~320K lines across 37 crates with 9,568 passing tests. No CUDA SDK, no nvcc.
ToRSh is a pure-Rust, PyTorch-compatible deep-learning framework with native tensor sharding. 0.1.2 lands real AVX2/NEON SIMD for f32 ops and activations, a true zero-copy buffer pool (100% heap-block reduction on hot loops), and SIMD + parallel enabled by default.
TenfloweRS 0.1.1 is an ergonomics + compatibility patch for the pure-Rust TensorFlow alternative: a declarative tensor! macro, an expanded prelude (transformers and RNNs), ndarray interop, ONNX re-exports, wgpu v29 compatibility across 57 sites, and richer typed Python FFI errors.
TenfloweRS is a pure-Rust alternative to TensorFlow, and this is its first stable release. It ships dual eager + graph execution, reverse-mode autodiff, 150+ research domains, and WGPU GPU acceleration — built on the SciRS2 and NumRS2 ecosystem, with no C++, CUDA-C, or Python runtime.
ToRSh is a PyTorch-compatible deep-learning framework in pure Rust with native tensor sharding. The 0.1.1 release hardens the 33-crate workspace onto consistent, published crates.io dependencies and adds the new torsh-convert model-converter CLI.
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.
Drop-in PyTorch replacement in pure Rust. Full SciRS2 integration (18 crates), SIMD CPU backend, autograd, and native sharding support. 2—3× faster inference, 50% less memory, single-binary deployment — no Python, no CUDA required.