Popular GitHub Repositories for Graphics Pipeline

Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.

Making large AI models cheaper, faster and more accessible

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Cross-platform, customizable ML solutions for live and streaming media.

Image-to-Image Translation in PyTorch

?? Build multimodal AI applications with cloud-native stack

Filament是一个实时物理渲染引擎，支持Android、iOS、Windows、Linux、macOS和WebGL2平台。

Turns Data and AI algorithms into production-ready web applications in no time.

Instant neural graphics primitives: lightning fast NeRF and more

Creative Coding: Generative Art, Data visualization, Interaction Design, Resources.

Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.

Image-to-image translation with conditional adversarial nets

A collaboration friendly studio for NeRFs

? Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading

Synthesizing and manipulating 2048x1024 images with conditional GANs

Evidently is ??an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.

An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.

Official implementation of "Neuralangelo: High-Fidelity Neural Surface Reconstruction" (CVPR 2023)