Pushing back the limits on numerical computing.
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The jax-ml organization on GitHub focuses on advancing numerical computing through a variety of projects. Their repositories primarily use Python, C++, HTML, and Jupyter Notebook. Notable projects include jax, a library for composable transformations of Python+NumPy programs, and scaling-book, a resource for scaling models on TPUs.
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Public repositories
jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
scaling-book
Home for "How To Scale Your Model", a short blog-style textbook about scaling LLMs on TPUs
jax-triton
jax-triton contains integrations between JAX and OpenAI Triton
ml_dtypes
A stand-alone implementation of several NumPy dtype extensions used in machine learning.
oryx
Oryx is a library for probabilistic programming and deep learning built on top of Jax.
jax-ai-stack
No description provided for this repository.
jax-llm-examples
Minimal yet performant LLM examples in pure JAX
bayeux
State of the art inference for your bayesian models.
bonsai
Minimal, lightweight JAX implementations of popular models.
coix
Inference Combinators in JAX
jax-tpu-embedding
No description provided for this repository.
australis
No description provided for this repository.
jax-blog
No description provided for this repository.
.allstar
No description provided for this repository.
.github
No description provided for this repository.
Frequently asked questions
What does jax-ml build on GitHub?
jax-ml builds a range of repositories focused on numerical computing, machine learning, and probabilistic programming. Key projects include jax for Python transformations and oryx for deep learning.
Which programming languages does jax-ml use?
The primary programming languages used by jax-ml include Python, C++, HTML, and Jupyter Notebook. This diverse set supports their various projects in numerical computing and machine learning.
Are jax-ml's repositories public?
Yes, all repositories under the jax-ml organization are public on GitHub. This transparency allows developers to explore and contribute to their projects focused on advanced numerical computing.
Is this exposure intended?
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