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JAX
Accelerated array computation and automatic differentiation for ML
Apache-2.0
ABOUT
Machine learning research and development requires fast, differentiable numerical computation across diverse hardware. JAX unifies NumPy-style array programming with composable transformations — autodiff, JIT compilation, vectorized mapping, and parallelization — that run seamlessly on CPUs, GPUs, and TPUs. It eliminates the need to write separate forward and backward passes, manage device transfers manually, or switch frameworks when moving from research experiments to production training loops.
INSTALL
pip install -U jax
pip install -U "jax[cuda13]"
INTEGRATION GUIDE
1. Train neural networks with automatic differentiation through complex control flow and recursive structures
2. JIT-compile compute-intensive array operations for CPU, GPU, and TPU acceleration
3. Build and optimize large-scale ML models with function transformations like vmap, pmap, and xmap
4. Accelerate scientific computing and simulation workloads with GPU/TPU-backed NumPy array operations
5. Implement custom training loops and optimizer logic without framework-imposed abstractions
TAGS
deep-learningautomatic-differentiationjitgputpunumpytensorpythonmachine-learning