Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Application creator and general launcher for JupyterHub

An implementation of chunked, compressed, N-dimensional arrays for Python.

File support for asyncio

Cross-platform lib for process and system monitoring in Python

Tensors and Dynamic neural networks in Python with strong GPU acceleration

A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.

Zarr core protocol for storage and retrieval of N-dimensional typed arrays

Cloud-Optimize your Scientific Data as Virtual Zarr stores, using xarray syntax.

A generative AI extension for JupyterLab

Provide the basic underpinnings for developing interfaces that have built-in implementation discovery and factory construction from configuration.

Concatenate a directory full of files into a single prompt for use with LLMs

A high-throughput and memory-efficient inference and serving engine for LLMs

The fundamental package for scientific computing with Python.

Repository for example user DTypes using the new API

RAG orchestration framework ⛵️

A specification that python filesystems should adhere to.

Panel: The powerful data exploration & web app framework for Python

AI with MongoDB

Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

Efficiently diff data in or across relational databases