Technology · Library
JAX
Library · Google · Catalog tech@1
Composable transformations of numerical Python programs.
- Categories
- Matched as
- jax
Momentum
Each signal is ranked as a percentile among the technologies that have it. A signal without enough history is not counted, and never shown as zero. How momentum works
Momentum
Collecting0 of 5 signalsNo score yet. Momentum is published only when at least 2 of the 5 signals have history. JAX has 0, so there is no single number: each signal is shown on its own below. It is not a zero, and nothing is estimated in its place.
Snapshot OCT 24, 2026 · Scoring tech@1
- Official repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.36,371 stars now
- Member repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.0 new members in 30 days
- Package adoption
- Not published by its registries
- Its packages’ registries publish no adoption figure, so there is nothing to grow.
- Open models
- No official models
- No official model on Hugging Face, so this signal does not apply.
- News
- Collecting
- News counts once there are 3 events across the last 60 days; it has 1.1 in the last 30 days · 0 in the 30 before
Adoption by registry
The growth of JAX’s own packages, one row per registry in that registry’s own unit. Registries count different things over different windows, so they are never added together.
No registry has two observations of JAX’s own packages about 30 days apart yet, so there is no growth to show. Nothing is estimated in its place.
News activity
- Events · 7 days
- 0published events that mention it
- Events · 30 days
- 1published events that mention it
- Previous 30 days
- 0events in the 30 days before
Momentum history
Momentum history starts OCT 8, 2026. A chart appears once two daily snapshots carry a score; none does so far.
Repositories
Official 1
- jax-ml/jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
36.4K stars
Members 43repositories tagged with one of its aliases, most-starred first
- google-research/scenic
Scenic: A Jax Library for Computer Vision Research and Beyond
3.8K stars
- NVIDIA/TransformerEngine
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
3.6K stars
- PennyLaneAI/pennylane
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.
3.5K stars
- google-deepmind/dm-haiku
JAX-based neural network library
3.3K stars
- grame-cncm/faust
Functional programming language for signal processing and sound synthesis
3.2K stars
- patrick-kidger/equinox
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
3K stars
- bethgelab/foolbox
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
3K stars
- explosion/thinc
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
2.9K stars
- pyro-ppl/numpyro
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
2.8K stars
- google-deepmind/mctx
Monte Carlo tree search in JAX
2.7K stars
- EMI-Group/evox
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
2.6K stars
- young-geng/EasyLM
Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.
2.5K stars
Packages
“Is” packages are JAX itself; ecosystem packages depend on them. The two are counted separately and never added together. Adoption is each registry’s own figure, with its percentile within that registry.
Is 6the technology itself, most-adopted first
- jax
Differentiate, compile, and transform Numpy code.
Not published by PyPI - jax-cuda12-pjrt
JAX XLA PJRT Plugin for NVIDIA GPUs
Not published by PyPI - jax-cuda12-plugin
JAX Plugin for NVIDIA GPUs
Not published by PyPI - jax-cuda13-pjrt
JAX XLA PJRT Plugin for NVIDIA GPUs
Not published by PyPI - jax-cuda13-plugin
JAX Plugin for NVIDIA GPUs
Not published by PyPI - jaxlib
XLA library for JAX
Not published by PyPI
Ecosystem 56packages that depend on it, most-adopted first
- aqtp
Accurate Quantized Training library.
Not published by PyPI - augmax
Efficiently Composable Data Augmentation on the GPU with Jax
Not published by PyPI - blackjax
Flexible and fast sampling in Python
Not published by PyPI - blackjax-nightly
Flexible and fast sampling in Python
Not published by PyPI - chex
Chex: Testing made fun, in JAX!
Not published by PyPI - clu
Set of libraries for ML training loops in JAX.
Not published by PyPI - cuequivariance-jax
CUDA accelerated equivariant operations
Not published by PyPI - diffrax
GPU+autodiff-capable ODE/SDE/CDE solvers written in JAX.
Not published by PyPI - distrax
Distrax: Probability distributions in JAX.
Not published by PyPI - drjax
DrJAX - Scalable and Differentiable MapReduce Primitives in JAX.
Not published by PyPI - dynamax
Dynamic State Space Models in JAX.
Not published by PyPI - e3nn-jax
Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors.
Not published by PyPI
Latest news the latest event that mention it
- 22d agoOCT 3, 2026