# openmlsys/openmlsys

《Machine Learning Systems: Design and Implementation》 (V2 is launching soon）

Repository: https://github.com/openmlsys/openmlsys
Canonical: https://ross.abutalabs.com/products/openmlsys
Homepage: https://openmlsys.github.io/v1/cn/
Language: TeX
License Family: other
Topics: machine-learning, software-architecture, textbook, computer-systems
Last push: 2026-03-15T21:51:18+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 72, release rhythm 35, longevity 100
- inputs: {"age_days": 1688, "days_push": 171, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4845, forks 478 (observed 2026-08-28T04:09:01.338669+00:00)

## What it is
An open-source bilingual (Chinese/English) textbook, 'Machine Learning Systems: Design and Implementation', covering the full ML systems stack from programming interfaces and computational graphs to AI compilers, distributed training, and GPU cluster management. The source is written in TeX/Markdown and built into an online book with mdBook.

## Use cases
- learn how machine learning systems are designed and implemented
- understand AI compiler internals and IR design
- study distributed training and parallelism strategies
- learn GPU programming with CUDA, Triton, and CUTLASS
- understand model serving and inference optimization
- learn how large-scale GPU clusters are scheduled and managed
- prepare for ML infrastructure engineering roles

## When to choose
- you want a free, comprehensive, open-source introduction to ML systems design
- you are a student, researcher, or engineer building or customizing ML infrastructure
- you need to understand the full stack from tensors and autodiff to compilers and cluster management

## When to avoid
- you need hands-on code for a specific framework rather than conceptual system design
- you want an introductory machine learning algorithms course rather than systems content
- you need a formally licensed, citable publication (the repo has no license file)

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, compiler, gpu-computing, documentation, developer-tools
- domain: machine-learning, deep-learning, gpu-computing, microservices, tutorials, education
- platform: cross-platform
- tags: open-source-textbook, ml-systems, ai-compilers, distributed-training, mdbook, tex, web

## Member repositories
- openmlsys/openmlsys (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:01.338669+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 2026-08-29T18:18:27.099527+00:00, confidence not recorded.
  - readme: https://github.com/openmlsys/openmlsys (fetched 2026-08-28T04:09:01.338669+00:00, sha 16b7a7aef6e8)
  - homepage: https://openmlsys.github.io/v1/cn/ (fetched 2026-08-29T09:01:06.071713+00:00, sha b2ebc5256272)
- Data as of 2026-08-30T08:39:29.467469+00:00.