{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"Metrax provides standard evaluation metrics for machine learning models implemented in JAX, supporting classification, regression, recommendation, and language modeling tasks.","skillfed_tags":["jax","metrics","ml-evaluation"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"google-metrax","links":{"html":"https://skillfed.io/packages/google-metrax","md":"https://skillfed.io/packages/google-metrax.md","pypi":"https://pypi.org/project/google-metrax/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-11-13","license_spdx":null,"license_treatment":"permissive","name":"google-metrax","python_support":"supports_current","summary":"A centralized JAX metrics library."},"popularity":{"monthly_downloads":75599,"position":14697,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.4"}