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
Jun 12, 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.
One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)
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.
Framework for Multi-Agent Deep Reinforcement Learning in Poker
RayLLM - LLMs on Ray
A parallel framework for population-based multi-agent reinforcement learning.
Examples on how to use LangChain and Ray
⚡ ⚡ 𝘋𝘦𝘦𝘱 𝘙𝘓 𝘈𝘭𝘨𝘰𝘵𝘳𝘢𝘥𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘙𝘢𝘺 𝘈𝘗𝘐
A basic Ray Tracer that exploits numpy arrays and functions to work fast.
Write local debuggable Python which traverses your powerful remote infra. Deploy as-is. Unobtrusive, unopinionated, PyTorch-like APIs.
pythonic interface to virtual screening software
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
Multi & Single Agent Reinforcement Learning for Traffic Signal Control Problem
an open-sourced highly automated machine learning Python framework for data-driven geochemistry discovery
A multiple parties joint, distributed execution engine based on Ray, to help build your own federated learning frameworks in minutes.
An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial
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