lensacom/sparkit-learn
PySpark + Scikit-learn = Sparkit-learn observed · 2026-08-28
Health v2 · maintenance only
23/100
- Activity 0
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 4340
- days_rel: n/a
- days_push: 2072
- n_releases_24m: 0
Adoption not part of the score
1150 stars · 254 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Sparkit-learn provides scikit-learn's API and functionality on top of PySpark, operating on distributed RDDs of numpy arrays and sparse matrices. It lets users run familiar sklearn-style estimators and transformers across a Spark cluster.
Use cases
- run scikit-learn models on a spark cluster
- distributed machine learning with pyspark
- scale sklearn transformers across partitions
- train models on large datasets that don't fit in memory
- use sklearn API on RDDs
When to choose
- you already have a Spark cluster and want sklearn-style APIs
- your data lives in RDDs and you need distributed preprocessing and modeling
When to avoid
- you need actively maintained software (last release 2020, supports old Python/Spark versions)
- you use Spark DataFrames rather than RDDs
- you want modern distributed ML like Spark MLlib or Dask-ML
Facets
library · maturity maintenance
machine-learning etl machine-learning data-science big-data python jvm cross-platform pyspark scikit-learn distributed-computing rdd apache-spark
2 sources
- readme: https://github.com/lensacom/sparkit-learn · fetched 2026-08-28 · a28f3050f14f
- registry_pypi: https://pypi.org/pypi/sparkit-learn/json · fetched 2026-08-29 · 2c559662630c
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| lensacom/sparkit-learn | main | 23 |
For agents
markdown · JSON · MCP: product_card(name="lensacom/sparkit-learn")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem