Popular GitHub Repositories for Airflow Operators

PipelineAI

Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.

Learning in infinite dimension with neural operators.

High-efficiency floating-point neural network inference operators for mobile, server, and Web

Elyra extends JupyterLab with an AI centric approach.

Several simple examples for popular neural network toolkits calling custom CUDA operators.

A Data Engineering & Machine Learning Knowledge Hub

PDEBench: An Extensive Benchmark for Scientific Machine Learning

? ??? ???? ????? ?-????? ????? ????????? | ????? ??? & ????? for free by designing, building and deploying an end-to-end ML batch system ~ ?????? ???? + 2.5 ????? ?? ??????? & ????? ?????????

Physics-Informed Neural networks for Advanced modeling

deployKF builds machine learning platforms on Kubernetes. We combine the best of Kubeflow, Airflow?, and MLflow? into a complete platform.

OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM

An end-to-end LLM reference implementation providing a Q&A interface for Airflow and Astronomer

Construct a modern data stack and orchestration the workflows to create high quality data for analytics and ML applications.

Low-Level Graph Neural Network Operators for PyG

? Yet another operator for running large language models on Kubernetes with ease. Powered by Ollama! ?

This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs

A Simple & Flexible Cross Framework Operators Toolkit

A Library for Advanced Neural PDE Solvers.

Apache Liminals goal is to operationalise the machine learning process, allowing data scientists to quickly transition from a successful experiment to an automated pipeline of model training, validation, deployment and inference in production. Liminal provides a Domain Specific Language to build ML workflows on top of Apache Airflow.