# mindspore-ai/mindspore

MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.

Repository: https://github.com/mindspore-ai/mindspore
Canonical: https://ross.abutalabs.com/products/mindspore
Homepage: https://gitee.com/mindspore/mindspore
Language: C++
License: Apache-2.0
License Family: permissive
Last push: 2024-07-29T01:48:05+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2395, "days_push": 766, "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 4700, forks 748 (observed 2026-08-28T04:08:57.191236+00:00)

## What it is
MindSpore is an open-source deep learning framework for training and inference across mobile, edge, and cloud scenarios. It provides automatic differentiation, automatic parallelism, and native support for Huawei Ascend AI processors with software-hardware co-optimization.

## Use cases
- train deep neural networks on Ascend or GPU hardware
- run model inference on mobile, edge, and cloud devices
- automatically parallelize large model training across clusters
- build and train models with a Python API and automatic differentiation
- deploy distributed training jobs with msrun

## When to choose
- you use Huawei Ascend AI processors and want native hardware support
- you need automatic parallelism for large-scale distributed training
- you want a single framework spanning mobile, edge, and cloud deployment

## When to avoid
- your team and ecosystem depend on PyTorch or TensorFlow tooling
- you need the broadest third-party model and library compatibility
- you have no access to Ascend hardware and prefer mainstream GPU ecosystems

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, llm-training, llm-inference, gpu-computing
- domain: deep-learning, machine-learning, artificial-intelligence
- platform: python, cpp, cross-platform
- tags: automatic-differentiation, automatic-parallelism, ascend, training-framework, inference, mobile-edge-cloud, linux, gpu, docker

## Member repositories
- mindspore-ai/mindspore (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:57.191236+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 2026-08-29T18:19:13.373891+00:00, confidence not recorded.
  - readme: https://github.com/mindspore-ai/mindspore (fetched 2026-08-28T04:08:57.191236+00:00, sha 7bc812cfb2be)
  - homepage: https://gitee.com/mindspore/mindspore (fetched 2026-08-29T09:04:01.589352+00:00, sha c9e971dac2ce)
  - site_page: https://gitee.com/about_us (fetched 2026-08-29T09:04:01.599161+00:00, sha 061b862ef0b5)
  - site_page: https://gitee.com/mindspore/mindspore/tree/master/docs (fetched 2026-08-29T09:04:01.600957+00:00, sha 7802062f5638)
  - site_page: https://compass.gitee.com/zh/docs/dimensions-define (fetched 2026-08-29T09:04:01.610050+00:00, sha 6392ce42534d)
  - site_page: https://gitee.com/all-about-git (fetched 2026-08-29T09:04:01.612030+00:00, sha fb606340594e)
  - site_page: https://gitee.com/features/gitee-go (fetched 2026-08-29T09:04:01.614009+00:00, sha bfa2599dcb24)
  - site_page: https://gitee.com/mindspore/mindspore/releases (fetched 2026-08-29T09:04:01.602879+00:00, sha 99715bbbedeb)
  - site_page: https://gitee.com/mindspore/mindspore/releases/tag/v2.7.2 (fetched 2026-08-29T09:04:01.608271+00:00, sha 873865d9309f)
- Data as of 2026-08-30T08:39:29.467469+00:00.