#Ray
Showing 54 of 54 repositories tagged #ray, ranked by stars
Learn how to develop, deploy and iterate on production-grade ML applications.
Ray is an AI compute engine. 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.
High-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale
CSGHub is a brand-new open-source platform for managing LLMs, developed by the OpenCSG team. It offers both open-source and on-premise/SaaS solutions, with features comparable to Hugging Face. Gain full control over the lifecycle of LLMs, datasets, and agents, with Python SDK compatibility with Hugging Face. Join us! โญ๏ธ
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).
Learn how to design, develop, deploy and iterate on production-grade ML applications.
LakeSoul is an end-to-end, realtime cloud-native Lakehouse framework for fast data ingestion, concurrent updates, incremental analytics, multimodal data processing and vector search โ powering next-generation BI and AI workloads.
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
A toolkit to run Ray applications on Kubernetes
A comprehensive guide to building RAG-based LLM applications for production.
LuxCore source repository
RayLLM - LLMs on Ray (Archived). Read README for more info.
Distribute and run AI workloads on Kubernetes magically in Python, like PyTorch for ML infra.
Open source project for data preparation for GenAI applications
DoEKS is a tool to build, deploy and scale Data Platforms on Amazon EKS
Benchmark of federated learning. Dedicated to the community. ๐ค
GPU environment and cluster management with LLM support
Crater is a cloud-native AI training & inference platform.
Framework for Multi-Agent Deep Reinforcement Learning in Poker
This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.
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.
๐ญ Mega Scale Multimodal DataPipeline for SOTA Foundation Models
Notebooks for the O'Reilly book "Learning Ray"
syftr is an agent optimizer that helps you find the best agentic workflows for your budget.
Simplifying the definition and execution, scaling and deployment of pipelines on the cloud.
Examples on how to use LangChain and Ray
Distributed Keras Engine, Make Keras faster with only one line of code.
:zap: :zap: ๐๐ฆ๐ฆ๐ฑ ๐๐ ๐๐ญ๐จ๐ฐ๐ต๐ณ๐ข๐ฅ๐ช๐ฏ๐จ ๐ธ๐ช๐ต๐ฉ ๐๐ข๐บ ๐๐๐
A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.
Python and Pandas are known to have issues around scalability and efficiency. You will learn how to use libraries such as Modin, Dask, Ray, Vaex etc to overcome the problems faced by Pandas.
an open-sourced highly automated machine learning Python framework for data-driven geochemistry discovery
A Web Crawler based on LLMs implemented with Ray and Huggingface. The embeddings are saved into a vector database for fast clustering and retrieval. Use it for your RAG.
A multiple parties joint, distributed execution engine based on Ray, to help build your own federated learning frameworks in minutes.
โ๏ธ Kubernetes-native platform for deploying and managing AI inference across multiple providers
A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!
Full-stack Highly Scalable Cloud-native Machine Learning system for demand forecasting with realtime data streaming, inference, retraining loop, and more
Computer go engine using Monte-Carlo Tree Search (MCTS)
A simple service that integrates vLLM with Ray Serve for fast and scalable LLM serving.
FlatlandRT is a 2D ray tracer visualization tool
A next-generation dynamic and high-performance language for AI and IOT with natural born distributed computing ability.
Python ๆฐๆฎ็งๅญฆๅ ้๏ผDaskใRayใXorbitsใmpi4py
๐ช 1-click Kubeflow using ArgoCD
Malicious URLs identified by scanning various public URL sources using the Google Safe Browsing API (over 6 billion URLs scanned daily)
Ray integration for Dagster
Notes on Data Engineering with Pandas, PySpark, Dask, Ray, Arrow DataFusion, Polars etc.
Supporting content (slides and exercises) for the Pearson video series covering best practices for developing scalable applications with Spark and Ray in the context of a data scientist's standard workflow.
๐ Scale your RAG pipeline using Ragswift: A scalable centralized embeddings management platform
A scalable, declarative, low-code framework for real-time and batch feature calculation/management (quant finance, anomaly/fraud detection, etc.), predictive ML training/inference and simulation. Built on top of Ray
Efficient Pandas and Ray Kafka Producer for python using actor model.
Prototype an integration of ray with Open Data Hub, using a singleuser profile to provision a ray cluster
JupyterLab Notebook for Mesosphere DC/OS
Social Media Analysis, scalable solution, flexible deployment that analyses social media contents