google-metrax

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 on this page — 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

google-metrax on PyPI

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

Evidence: google_metrax-0.2.4-py3-none-any.whl

Tags

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