Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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:zap: A Fast, Extensible Progress Bar for Python and CLI
A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support
A hyperparameter optimization framework
Parallel computing with task scheduling
NumPy aware dynamic Python compiler using LLVM
Computing with Python functions.
Asynchronous parallel SSH client library.
:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions
Coordinate Claude, Codex and Cursor windows with shared tasks, acknowledgements, prechecks, parallel tests, source-bound proofs and independent review. Built-in skills recall lessons, simplify code and forecast work. Track progress, decisions and evidence in a bilingual local dashboard, with MCP plugins. Browse skills and verified security audits.
Python bindings for MPI
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
Parallel uploads to Amazon AWS S3
Python package for inverse kinematic calculations of hybrid serial parallel robots
Python3 script that transfers files in Google Colab 10-50x faster, using multi-threaded parallel copying between Google Drive locations.
🎲 Dotflow turns an idea into flow! — Lightweight Python library for execution pipelines