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Deep Learning for humans

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

Efficiently computes derivatives of NumPy code.

Flax is a neural network library for JAX that is designed for flexibility.

A retargetable MLIR-based machine learning compiler and runtime toolkit.

PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.

JAX-based neural network library

Training and serving large-scale neural networks with auto parallelization.

Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.

A simple, performant, and scalable Jax LLM!

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

Automated Machine Learning on Kubernetes

A Graph Neural Network Library in Jax

Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with support for Gymnasium/Gym, NVIDIA Isaac Lab, MuJoCo Playground and other environments

Pretrained model hub for Keras 3.