# mars-project/mars

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

Repository: https://github.com/mars-project/mars
Canonical: https://ross.abutalabs.com/products/mars-project-mars
Homepage: https://mars-project.readthedocs.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: python, numpy, tensor, pandas, machine-learning, scikit-learn, tensorflow, pytorch, xgboost, lightgbm, ray, statsmodels, joblib, dataframe, dask
Last push: 2024-01-02T10:00:14+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2828, "days_push": 974, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2742, forks 324 (observed 2026-08-28T04:07:16.590698+00:00)

## What it is
Mars is a tensor-based unified framework for large-scale data computation that provides NumPy-, pandas-, and scikit-learn-compatible APIs which scale to distributed and out-of-memory workloads. It lets existing Python data science code run on a local session or a Mars cluster with minimal code changes.

## Use cases
- scale numpy computations beyond a single machine's memory
- run pandas dataframes on a cluster
- distribute scikit-learn model training
- process large tensors in parallel
- dask alternative for distributed dataframes
- out-of-core data processing in python

## When to choose
- you have NumPy/pandas/sklearn code that outgrows one machine and want a familiar API
- you need distributed execution with lazy evaluation and explicit .execute() control
- you want a lighter-weight alternative to Spark or Dask for Python data workloads

## When to avoid
- you need the most active ecosystem - Dask or Ray may have larger communities
- your data fits comfortably in memory on one machine
- you need deep Spark/Hadoop ecosystem integration

## Facets
- artifact type: library
- maturity: maintenance
- function: data-science, machine-learning, etl, streaming, concurrency
- domain: data-science, big-data, machine-learning, microservices
- platform: python, cloud
- tags: distributed-computing, numpy-compatible, pandas-compatible, tensor, dataframe, scale-up, dask-alternative, data-engineering, linux, macos, docker, kubernetes

## Member repositories
- mars-project/mars (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:16.590698+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 2026-08-30T08:19:07.475903+00:00, confidence not recorded.
  - readme: https://github.com/mars-project/mars (fetched 2026-08-28T04:07:16.590698+00:00, sha ec659545c043)
- Data as of 2026-08-30T08:39:29.467469+00:00.