google-deepmind/optax

Health v2 · maintenance only

Adoption not part of the score

2325 stars · 361 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Optax is a gradient processing and optimization library for JAX, offering composable building blocks like optimizers and loss functions. It is developed by DeepMind to support machine learning research with easily recombined components.

Use cases

• train neural networks in jax with adam optimizer
• implement custom optimizers from composable gradient transforms
• compute loss functions for machine learning models
• run gradient descent research experiments in jax
• optimize reinforcement learning policies
• schedule learning rates during training

When to choose

• you use JAX and need well-tested optimizers or loss functions
• you want to compose custom gradient processing pipelines
• you need readable implementations that match standard equations

When to avoid

• you work in PyTorch or TensorFlow rather than JAX
• you need a full high-level training framework with data loading and experiment tracking

Facets

library · maturity stable

machine-learning llm-training deep-learning machine-learning deep-learning reinforcement-learning python cross-platform jax optimizers gradient-processing loss-functions deepmind algorithms gpu

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markdown · JSON · MCP: product_card(name="google-deepmind/optax")