Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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Updated
May 26, 2024 - Python
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
RayLLM - LLMs on Ray
One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)
Write local debuggable Python which traverses your powerful remote infra. Deploy as-is. Unobtrusive, unopinionated, PyTorch-like APIs.
A parallel framework for population-based multi-agent reinforcement learning.
A basic Ray Tracer that exploits numpy arrays and functions to work fast.
GPU environment and cluster management with LLM support
Framework for Multi-Agent Deep Reinforcement Learning in Poker
TorchX is a universal job launcher for PyTorch applications. TorchX is designed to have fast iteration time for training/research and support for E2E production ML pipelines when you're ready.
Examples on how to use LangChain and Ray
Distributed Keras Engine, Make Keras faster with only one line of code.
⚡ ⚡ 𝘋𝘦𝘦𝘱 𝘙𝘓 𝘈𝘭𝘨𝘰𝘵𝘳𝘢𝘥𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘙𝘢𝘺 𝘈𝘗𝘐
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
Unified storage framework for the entire machine learning lifecycle
CamTools: Camera Tools for Computer Vision
Multi & Single Agent Reinforcement Learning for Traffic Signal Control Problem
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