# apache/systemds

An open source ML system for the end-to-end data science lifecycle

Repository: https://github.com/apache/systemds
Canonical: https://ross.abutalabs.com/products/systemds
Homepage: https://systemds.apache.org/
Language: Java
License: Apache-2.0
License Family: permissive
Topics: systemds, java, dml, python
Last push: 2026-08-31T17:01:59+00:00

## Health v2 (maintenance only)
Score: 87/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 100, release rhythm 62, longevity 100
- inputs: {"age_days": 3949, "days_push": 2, "days_rel": 40, "gap_med": 453, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1097, forks 546 (observed 2026-09-01T02:13:58.849357+00:00)

## What it is
Apache SystemDS is an open-source machine learning system covering the end-to-end data science lifecycle, from data cleaning and feature engineering to distributed model training and serving. Scripts written in an R-like DML language or via Python/Java APIs are compiled into hybrid runtime plans combining local in-memory CPU/GPU operations with distributed execution on Apache Spark.

## Use cases
- train machine learning models on large datasets with Spark
- write ML pipelines in an R-like scripting language
- run distributed matrix computations for data science
- prototype ML algorithms and scale them to a cluster
- clean and prepare data before model training
- serve trained ML models in production

## When to choose
- you need scalable ML on large datasets with Spark integration
- you want algorithm customizability via a high-level R-like or Python-like language
- you need hybrid local and distributed execution with automatic optimization

## When to avoid
- you need deep learning with modern GPU frameworks like PyTorch or TensorFlow
- you want a large ecosystem of pretrained models and community libraries
- your datasets fit in memory and simpler tools like scikit-learn suffice

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, etl, compiler
- domain: machine-learning, data-science, big-data, microservices
- platform: jvm, python, cross-platform
- tags: dml, apache, distributed-ml, spark, federated-learning, r-like-syntax, gpu

## Member repositories
- apache/systemds (main) score 87

## Provenance
- Observed fields: from GitHub, fetched 2026-09-01T02:13:58.849357+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 2026-08-30T06:46:23.574033+00:00, confidence not recorded.
  - readme: https://github.com/apache/systemds (fetched 2026-09-01T02:13:58.849357+00:00, sha 78e0c9c09c6a)
  - homepage: https://systemds.apache.org/ (fetched 2026-08-29T12:49:37.585874+00:00, sha 3e2a1709f3b4)
  - site_page: https://systemds.apache.org/docs/3.4.0 (fetched 2026-08-29T12:49:37.588352+00:00, sha cd066d21a863)
  - site_page: https://systemds.apache.org/documentation.html (fetched 2026-08-29T12:49:37.590301+00:00, sha 0300eedaee5e)
- Data as of 2026-08-30T08:39:29.467469+00:00.