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OPTAX
Composable gradient optimization for JAX
Apache-2.0
ABOUT
Training neural networks in JAX requires implementing optimization logic from scratch or adapting frameworks built for eager-execution paradigms. Optax provides a library of composable gradient transformations — Adam, SGD with momentum, RMSProp, gradient clipping, weight decay, learning rate schedules, and more — that combine cleanly via functional composition. Each transformation is a pure function that can be chained, inspected, and debugged independently. This lets researchers experiment with optimizer variants (AdamW, Lion, AdaFactor, schedule-free methods) by composing transformations rather than rewriting loops.
INSTALL
pip install optaxINTEGRATION GUIDE
1. Train a ResNet on ImageNet with a custom optimizer chain combining Adam, gradient clipping, and cosine decay
2. Experiment with different optimizer schedules (warmup, cosine, step decay) by swapping chain components
3. Implement distributed training with multiple gradient accumulations and synchronized weight updates
4. Add gradient noise and norm clipping for privacy-preserving training with differentially private SGD
5. Debug training instability by tracing per-layer gradient statistics through the transform chain
TAGS
pythonjaxoptimizationgradient-processingdeep-learningdeepmind