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google-deepmind/kfac-jax

Second Order Optimization and Curvature Estimation with K-FAC in JAX.

Python ◇ bayesian-deep-learning Apache-2.0
★331STARS
⑂34FORKS
!23ISSUES
🏆#4,281GLOBAL RANK
🔥4DAYS TRENDING
🚀
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Star History

Continuous Observations
Interactive star growth chart for google-deepmind/kfac-jax
CSV

Momentum

+10

STARS · LAST 30 DAYS

1

PER DAY

#504

MOST-STARRED Python

Window7 days30 days90 days
Stars gained+7+10+90
Per day111
Forks gained+1+3+10

kfac-jax gained 10 stars in the last 30 days, about 1 a day, and now has 331. It is about 5 years old and has averaged roughly 66 stars a year. It ranks #504 among Python repositories and #4,281 across all languages on GitHubRepo.

Trending Record

kfac-jax has maintained a continuous presence across global trending indexes, peaking at #2446. Below is the 30-day activity profile:

💡 Overview

kfac-jax is an open-source project written in Python: Second Order Optimization and Curvature Estimation with K-FAC in JAX.

Engineered for speed, consistency, and developer ease, it solves common hurdles in bayesian-deep-learning, machine-learning, optimization. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

⚡ Key Features

1

Optimized execution pipeline written in Python for predictable speed.

2

Zero-friction configuration with comprehensive sensible defaults out of the box.

3

Cross-platform runtime support across Linux, macOS, and Windows environments.

4

Strong typing and modular architecture designed for easy extension and maintainability.

5

Standardized CLI and API interfaces for smooth integration into CI/CD workflows.

6

Active community maintenance with regular dependency updates and security patches.

📥 Installation

terminal
$ pip install kfac-jax

⚙ System Requirements

Platforms

  • • macOS
  • • Linux
  • • Windows

Runtime & Dependencies

Python >= 3.9, pip, virtualenv

Architecture

x86_64, ARM64 (Apple Silicon & Graviton)

🧠 How It Works

kfac-jax coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

🎯 Production Use Cases

Production System Integration

Embed kfac-jax into Python backend services to handle core application logic.

CI/CD Automated Pipelines

Run automated validation, builds, and integration suites during deployments.

Developer Tooling & Workflows

Accelerate developer onboarding with pre-configured project utilities.

Open Source Extension

Fork and customize internal modules under the repository's open Apache-2.0 license.

🚀 Getting Started

1

Install kfac-jax using your package manager: `pip install kfac-jax`

2

Initialize your project workspace or configuration file for kfac-jax.

3

Import kfac-jax into your codebase or invoke it directly from your terminal.

4

Execute your test suite or run `kfac-jax --help` to verify successful setup.

👍 Strengths

Active community backing with 331 GitHub stars and verified adoption.
Permissive open-source distribution under the Apache-2.0 license.
Built in Python for high execution speed and developer familiarity.
Cross-platform compatibility across modern Linux, macOS, and Windows environments.
Clean modular design allowing flexible configuration and pipeline integration.

⚠️ Considerations

Requires familiarity with Python and modern CLI workflows.
Ecosystem extensions may require manual configuration depending on environment constraints.
Active development roadmap means breaking API changes may occur across major versions.

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👥 Who Should Use This

Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.

🏆 Nearby in the Rankings

google-deepmind/kfac-jax is currently ranked #4,281 by stars across every repository tracked on GitHubRepo. These are adjacent projects:

RankRepositoryLanguageStarsAction
#4,274 cnoe-io/idpbuilder Go ★ 332 Compare ↗
#4,274 rwjdk/agent-framework-samples C# ★ 332 Compare ↗
#4,274 benjaminasterA/antigravity-awesome-skills Python ★ 332 Compare ↗
#4,274 yuuichieguchi/Calyx Swift ★ 332 Compare ↗
#4,274 St0ff3l/fileterm Rust ★ 332 Compare ↗
#4,281 google-deepmind/kfac-jax This Project Python ★ 331
#4,281 simondlevy/Hackflight C++ ★ 331 Compare ↗
#4,283 dakrauth/django-swingtime Python ★ 330 Compare ↗
#4,283 e107inc/e107 PHP ★ 330 Compare ↗
#4,283 xensik/gsc-tool C++ ★ 330 Compare ↗
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Frequently Asked Questions

What does kfac-jax do? +

Second Order Optimization and Curvature Estimation with K-FAC in JAX.

What language is kfac-jax written in? +

The primary language is Python. Topics include: bayesian-deep-learning, machine-learning, optimization.

Is kfac-jax actively maintained? +

Yes, the last recorded push was on Sep 25, 2026 with 23 open issues being tracked.

How many stars does kfac-jax have? +

kfac-jax has 331 stars and 34 forks on GitHub.

How does kfac-jax rank among GitHub repositories? +

With 331 stars, google-deepmind/kfac-jax is ranked #4,281 globally across all repositories tracked on GitHubRepo and #504 among Python projects.

What license is kfac-jax distributed under? +

The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.

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