From e7055fe250401174d557f7213fa31ad26f1a6c67 Mon Sep 17 00:00:00 2001 From: JIMMY ZHAO Date: Sun, 1 Dec 2024 23:57:22 -0500 Subject: [PATCH] add Prime --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index d86edfcc..b6add321 100644 --- a/README.md +++ b/README.md @@ -737,6 +737,7 @@ Please review our [CONTRIBUTING.md](https://github.com/EthicalML/awesome-product * [Nos](https://github.com/nebuly-ai/nos) ![](https://img.shields.io/github/stars/nebuly-ai/nos.svg?style=social) - Nos is an open-source platform to efficiently run AI workloads on Kubernetes, increasing GPU utilization and reducing infrastructure and operational costs. * [NVIDIA TensorRT](https://github.com/NVIDIA/TensorRT) ![](https://img.shields.io/github/stars/NVIDIA/TensorRT.svg?style=social) - TensorRT is a C++ library for high-performance inference on NVIDIA GPUs and deep learning accelerators. * [Open Platform for AI](https://github.com/Microsoft/pai) ![](https://img.shields.io/github/stars/Microsoft/pai.svg?style=social) - Platform that provides complete AI model training and resource management capabilities. +* [Prime](https://github.com/PrimeIntellect-ai/prime) ![](https://img.shields.io/github/stars/PrimeIntellect-ai/prime.svg?style=social) - Prime is a framework for efficient, globally distributed training of AI models over the internet. * [PyCaret](https://github.com/pycaret/pycaret) ![](https://img.shields.io/github/stars/pycaret/pycaret.svg?style=social)) - low-code library for training and deploying models (scikit-learn, XGBoost, LightGBM, spaCy) * [Sematic](https://github.com/sematic-ai/sematic) ![](https://img.shields.io/github/stars/sematic-ai/sematic.svg?style=social) - Platform to build resource-intensive pipelines with simple Python. * [Skaffold](https://github.com/GoogleContainerTools/skaffold) ![](https://img.shields.io/github/stars/GoogleContainerTools/skaffold.svg?style=social) - Skaffold is a command line tool that facilitates continuous development for Kubernetes applications. You can iterate on your application source code locally then deploy to local or remote Kubernetes clusters.