h2o
H2O, Fast Scalable Machine Learning, for python
Decision gist · record as of 2026-08-14
Yes, with conditions. H2O is a mature, actively maintained platform for distributed machine learning with permissive licensing. However, the high install friction and large package footprint require adequate disk and memory resources. Two known security vulnerabilities warrant review before deployment in sensitive environments. Suitable for teams needing scalable predictive modeling on big data; less ideal for lightweight or resource-constrained setups.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- The package is large and may require significant disk and memory resources; H2O-3 sends optional anonymous telemetry by default (disabled unless explicitly enabled with h2o.set_telemetry(True)).
- High install friction: the package ships as a large archive (h2o-3.46.0.12.tar.gz) with no declared runtime dependencies, suggesting a self-contained or system-level dependency model.
- Maintenance is active with a recent release (2 days old) and steady repository activity.
License · maintenance · safety
Apache v2 (permissive) — Licensed under Apache v2 (permissive), allowing commercial and private use with minimal restrictions, making it suitable for most production and research contexts.
last release 2026-08-12 (2 days) · last repo commit 2026-08-14 · 7,504 stars
2 known vulnerabilities (OSV.dev, 2026-08-14) · 215,655 downloads/mo, #9,396 on PyPI
Alternatives
Verify before relying
pip install h2o
import h2o
h2o.init()- Whether the high install friction reflects Java/system dependencies or just package size
- What specific machine learning algorithms and statistical methods are available
- Performance characteristics and scalability limits for typical workloads
- Details on the two known security vulnerabilities (GHSA-p2vc-m5fv-9w9m, PYSEC-2026-1443) and their impact
What it is and what it does
H2O is a machine learning and statistical computing platform designed to handle large datasets across distributed systems. It provides Python, R, Excel, and JSON interfaces to build, train, and score predictive models using a range of algorithms from simple to advanced. The platform abstracts away the complexity of parallel computation, allowing data scientists and analysts to focus on model development rather than infrastructure.
The package is production-stable and actively maintained, with support for Python 3.7 through 3.11. It targets multiple industries including finance, healthcare, telecommunications, and research. Users can explore and munge datasets, build models, and deploy scoring pipelines within a single platform. The package includes optional telemetry (off by default) to help developers understand usage patterns.
Use it for
- Build and deploy predictive models on datasets too large for single-machine tools
- Perform statistical analysis and data mining across distributed clusters
- Develop machine learning pipelines for financial risk modeling or insurance underwriting
- Train classification and regression models for healthcare or research applications
- Score new data in production using models trained offline
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
H2O is a mature, actively maintained platform for distributed machine learning with permissive licensing. However, the high install friction and large package footprint require adequate disk and memory resources. Two known security vulnerabilities warrant review before deployment in sensitive environments. Suitable for teams needing scalable predictive modeling on big data; less ideal for lightweight or resource-constrained setups.
Install
h2o on PyPI
Before you install
High install friction: the package ships as a large archive (h2o-3.46.0.12.tar.gz) with no declared runtime dependencies, suggesting a self-contained or system-level dependency model. Maintenance is active with a recent release (2 days old) and steady repository activity.
The package is large and may require significant disk and memory resources; H2O-3 sends optional anonymous telemetry by default (disabled unless explicitly enabled with h2o.set_telemetry(True)).
License in practice
Licensed under Apache v2 (permissive), allowing commercial and private use with minimal restrictions, making it suitable for most production and research contexts.
Quickstart
pip install h2o
import h2o
h2o.init()Verify before relying
- Whether the high install friction reflects Java/system dependencies or just package size
- What specific machine learning algorithms and statistical methods are available
- Performance characteristics and scalability limits for typical workloads
- Details on the two known security vulnerabilities (GHSA-p2vc-m5fv-9w9m, PYSEC-2026-1443) and their impact
Package facts
| License | Apache v2 permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Actively maintained 2 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 215,655 / month, #9,396 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | 2 GHSA-p2vc-m5fv-9w9m, PYSEC-2026-1443 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Customer ServiceIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Financial and Insurance IndustryIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchIntended Audience :: Telecommunications IndustryLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Information Analysis |
Evidence: h2o-3.46.0.12.tar.gz
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