4 posts
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.
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.