# autumnai/leaf

Open Machine Intelligence Framework for Hackers. (GPU/CPU)

Repository: https://github.com/autumnai/leaf
Canonical: https://ross.abutalabs.com/products/autumnai-leaf
Language: Rust
License: Apache-2.0
License Family: permissive
Last push: 2024-03-20T09:51:14+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3964, "days_push": 896, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5541, forks 268 (observed 2026-08-28T04:09:22.216595+00:00)

## What it is
Leaf is an open-source machine learning framework written in Rust for building classical, deep, or hybrid ML applications, inspired by TensorFlow, Torch, and Caffe. It emphasizes a simple API, modularity, and portability across CPUs, GPUs, and FPGAs via OpenCL or CUDA through its Collenchyma backend layer.

## Use cases
- train neural networks in rust
- build deep learning models with gpu acceleration
- run machine learning on fpgas or embedded devices
- prototype deep learning apps with a minimal api
- cross-platform machine learning without python

## When to choose
- you want a lean, Rust-native deep learning framework
- you need portability across CPU, GPU, and FPGA backends
- you prefer minimal dependencies and a simple API

## When to avoid
- you need a maintained framework with an active community
- you need modern deep learning features like transformers or autodiff ecosystems
- you want production-grade tooling and broad model support

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, deep-learning, gpu-computing
- domain: machine-learning, deep-learning
- platform: rust, windows
- tags: neural-networks, opencl, cuda, rust, linux, macos, gpu

## Member repositories
- autumnai/leaf (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:22.216595+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:55:42.032134+00:00, confidence not recorded.
  - readme: https://github.com/autumnai/leaf (fetched 2026-08-28T04:09:22.216595+00:00, sha 4962fce52b38)
  - registry_crates: https://crates.io/api/v1/crates/leaf (fetched 2026-08-29T08:50:56.976457+00:00, sha 71cf3b590e7d)
- Data as of 2026-08-30T08:39:29.467469+00:00.