---
id: google-metrax
version: "0.2.4"
license: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# google-metrax — A centralized JAX metrics library.

License: permissive · Maintenance: active · Downloads: 75.6K/mo

## What it is and what it does

Metrax is a JAX metrics library that fills a gap in the JAX ecosystem by providing standard evaluation metrics commonly found in TensorFlow and PyTorch. It builds on CLU (CommonLoopUtils) to ensure compatibility with distributed and scaled training environments. The library includes predefined metrics for classification, regression, recommendation, and language modeling tasks, allowing developers to evaluate JAX models without writing custom metric implementations.

The package is developed on GitHub and used by several Google core products, indicating production-level maturity. It depends on core JAX libraries (jax, numpy, flax) plus clu for distributed training support and tensorboardx for visualization integration. Installation is straightforward via pip, and the library is actively maintained with recent development activity.

Use it for:

- Evaluate classification models in JAX by computing standard metrics like accuracy, precision, recall, and F1-score.
- Measure regression model performance using built-in loss and error metrics compatible with JAX workflows.
- Assess recommendation system quality with metrics designed for ranking and recommendation tasks.
- Monitor language model evaluation metrics during training with tensorboardx integration for visualization.
- Build distributed training pipelines with metrics that scale across multiple devices via CLU integration.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Metrax provides standard evaluation metrics for machine learning models implemented in JAX, supporting classification, regression, recommendation, and language modeling tasks.

Yes. Metrax is actively maintained, has low install friction, carries a permissive Apache 2.0 license, and addresses a real gap in the JAX ecosystem. It's backed by Google and used in production. Install it if you're building JAX-based ML models and need standard evaluation metrics without implementing them yourself.

## Install

pip install google-metrax
uv add google-metrax
poetry add google-metrax

## Installing google-metrax

Before you install: Low install friction with a pure Python wheel. Actively maintained with recent commits and used by Google products. Depends on JAX ecosystem libraries (jax, flax, clu, numpy) and tensorboardx, which are standard in JAX-based ML workflows.

License in practice: Licensed under Apache License 2.0 (permissive). You may use, modify, and distribute the package freely in commercial and private projects, provided you include the license and attribute the original work.

Quickstart:

pip install google-metrax

import metrax
# Use predefined metrics for model evaluation
# (specific metric usage depends on your model type)

Requires JAX and its dependencies (jax, numpy, flax) to be installed; JAX itself has platform-specific requirements.

Verify before relying:

- Which specific metrics are included for each model type (classification, regression, recommendation, language modeling).
- Whether metrax integrates with common JAX training loops or requires custom integration.
- Performance characteristics when used with large-scale distributed training.

## Package facts

- License: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 75.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

jax metrics library, machine learning evaluation metrics, classification regression metrics, jax model evaluation, distributed training metrics, language model evaluation, recommendation system metrics, jax, metrics, ml-evaluation

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