{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/5"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Optax provides composable building blocks for gradient processing and optimization in JAX, including implementations of popular optimizers and loss functions that can be combined into custom solutions.","skillfed_tags":["jax-ecosystem","gradient-optimization","composable-components"],"use_cases":["Training neural networks with custom optimizer combinations by composing Optax building blocks.","Implementing gradient clipping, weight decay, or other transformations in a modular way.","Prototyping new optimization algorithms by combining existing components.","Using standard optimizers like Adam or RMSprop in JAX-based machine learning projects.","Computing loss functions like L2 or cross-entropy within JAX training loops."],"what_it_does":"Optax is a gradient processing and optimization library built on top of JAX. It provides well-tested, efficient implementations of core optimization components\u2014such as Adam, SGD, and other popular optimizers\u2014along with loss functions and gradient transformation utilities. The library is designed around composability: rather than monolithic optimizer classes, it offers small building blocks that can be recombined in custom ways to create new optimizers or gradient processing pipelines.\n\nThe package targets researchers and practitioners working with JAX who need flexible, modular optimization tools. It depends on JAX, jaxlib, numpy, and absl-py. The library evolved from an earlier experimental JAX module and is now maintained as a standalone project by DeepMind. It supports current Python versions and is actively maintained, making it suitable for both research prototyping and production use in JAX-based machine learning workflows.","worth_installing":"Yes. Optax is actively maintained, has no known vulnerabilities, installs with low friction, and is the standard gradient optimization library for JAX. Install it if you are building machine learning systems with JAX and need flexible, composable optimizer and loss components. The only gotcha is the compiled JAX/jaxlib dependency, which may require platform-specific setup."},"id":"optax","links":{"html":"https://skillfed.io/packages/optax","md":"https://skillfed.io/packages/optax.md","pypi":"https://pypi.org/project/optax/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-20","license_spdx":null,"license_treatment":"permissive","name":"optax","python_support":"supports_current","summary":"A gradient processing and optimization library in JAX."},"popularity":{"monthly_downloads":3760118,"position":2503,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.2.8"}