# tinygrad/tinygrad

You like pytorch? You like micrograd? You love tinygrad! ❤️

Repository: https://github.com/tinygrad/tinygrad
Canonical: https://ross.abutalabs.com/products/tinygrad
Language: Python
License: MIT
License Family: permissive
Last push: 2026-08-27T00:22:54+00:00

## Health v2 (maintenance only)
Score: 91/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 75, longevity 100
- inputs: {"age_days": 2145, "days_push": 7, "days_rel": 9, "gap_med": 94, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 33509, forks 4286 (observed 2026-08-28T04:11:58.327846+00:00)

## What it is
tinygrad is a small, hackable deep learning framework providing a PyTorch-like tensor library with autograd, plus a visible IR and compiler that fuses and lowers kernels across hardware backends. It includes nn, optim, and dataset modules for real training, with JIT and graph execution support.

## Use cases
- train neural networks with a lightweight pytorch-like api
- learn how a deep learning framework works internally
- compile and fuse gpu kernels with a hackable ir
- run deep learning on diverse hardware backends
- prototype autograd and tensor operations like micrograd
- experiment with kernel scheduling and codegen

## When to choose
- you want a minimal, readable alternative to pytorch for training models
- you need to understand or modify the compiler and kernel generation
- you want deep learning support across many accelerators from one codebase
- you are learning how autodiff and jit compilation work

## When to avoid
- you need the full ecosystem, vmap/pmap transforms, or production maturity of pytorch or jax
- you rely on extensive pretrained model hubs and third-party integrations
- you need guaranteed stability for critical production workloads

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, compiler, llm-training, gpu-computing
- domain: deep-learning, machine-learning, compilers, gpu-computing
- platform: python, cross-platform
- tags: tensor-library, autograd, deep-learning-framework, kernel-fusion, jit, pytorch-alternative, gpu

## Member repositories
- tinygrad/tinygrad (main) score 91

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:58.327846+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 2026-08-29T16:28:18.478007+00:00, confidence not recorded.
  - readme: https://github.com/tinygrad/tinygrad (fetched 2026-08-28T04:11:58.327846+00:00, sha 2da35287fe12)
- Data as of 2026-08-30T08:39:29.467469+00:00.