#7171st of 105 in ML frameworks and libraries
h2o-3
Distributed in-memory machine learning platform with AutoML and R/Python clients
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- Stars
- 7.5k
- License
- Apache-2.0
- Last commit
- Sep 2026
- Language
- Jupyter Notebook
7,400 on 1 Apr 20267,513 on 21 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
H2O-3 is a JVM-based, in-memory platform for distributed machine learning on tabular data. It implements GLM, GBM (including XGBoost), Random Forest, deep neural networks, Stacked Ensembles, K-Means, PCA, Word2Vec and an AutoML routine, driven from R, Python, Scala, Java, REST/JSON or the Flow web notebook. Models can be exported as POJO or MOJO for production scoring, and it integrates with Hadoop and Spark.
Who it is for: Data scientists training tabular ML models on large datasets in R or Python
Strengths
- Broad classic algorithm set plus AutoML, accessed from R, Python, Scala, Java and REST
- Exports models as POJO or MOJO for fast scoring in production
- Runs as a cluster across nodes, with Hadoop and Sparkling Water (Spark) integration
- Installs via pip or CRAN; the jar serves Flow UI on port 54321
Weaknesses
- Not an LLM or generative AI toolkit; focused on classic tabular ML
- Building from source needs JDK, Node.js, Gradle, Python and R
- No tagged release on the repository; stable builds come from h2o.ai/download
- Full test suite needs at least 8 GB RAM, 16 GB preferred
What it needs
- Docker
- Needs JDK 1.8+, Node.js (build), Gradle (build), R (build), Python (build)
- Models: GLM, GBM, XGBoost, Random Forest, Deep Learning
- port 54321
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