# xorbitsai/xorbits

Scalable Python DS & ML, in an API compatible & lightning fast way.

Repository: https://github.com/xorbitsai/xorbits
Canonical: https://ross.abutalabs.com/products/xorbits
Homepage: https://xorbits.readthedocs.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: data-science, distributed-systems, numpy, pandas, python, scalable, lightgbm, machine-learning, ml, xgboost
Last push: 2026-02-14T04:04:01+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 67, release rhythm 40, longevity 100
- inputs: {"age_days": 1498, "days_push": 200, "days_rel": 616, "gap_med": 29, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1199, forks 69 (observed 2026-08-28T04:03:57.856394+00:00)

## What it is
Xorbits is an open-source distributed computing framework that scales Python data science and machine learning workloads from a laptop to large clusters. It offers APIs compatible with pandas, NumPy, PyTorch, XGBoost, and LightGBM, often requiring only a one-line code change to scale existing workflows.

## Use cases
- scale pandas dataframes beyond single-machine memory
- speed up numpy computations using multiple cores or GPUs
- distribute XGBoost or LightGBM model training across a cluster
- run data preprocessing pipelines on terabytes of data
- accelerate existing notebooks without rewriting code
- serve and tune ML models at scale

## When to choose
- your pandas or numpy workflows outgrow a single machine
- you want pandas-like APIs with distributed execution
- you need to scale ML training and serving with minimal code changes
- you want multi-core or GPU acceleration on a single node

## When to avoid
- your datasets fit comfortably in memory with pandas
- you need a mature ecosystem like Dask or Ray with broader community support
- you require strict pandas API parity for edge-case behaviors
- you only need simple single-node data processing

## Facets
- artifact type: library
- maturity: active
- function: data-science, machine-learning, etl, streaming
- domain: data-science, machine-learning, big-data, microservices
- platform: python, cloud
- tags: pandas-compatible, numpy-compatible, distributed-computing, xgboost, lightgbm, gpu-acceleration, scalable-dataframes, data-engineering, linux, macos, docker, kubernetes

## Member repositories
- xorbitsai/xorbits (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:57.856394+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 2026-08-30T06:21:07.372571+00:00, confidence not recorded.
  - readme: https://github.com/xorbitsai/xorbits (fetched 2026-08-28T04:03:57.856394+00:00, sha f6977b117309)
- Data as of 2026-08-30T08:39:29.467469+00:00.