Benchmark of federated learning. Dedicated to the community. 🤗

LLM驱动的 A/H股智能分析器，多数据源行情 + 实时新闻 + Gemini 决策仪表盘 + 多渠道推送，零成本，纯白嫖，定时运行

PyTorch implementation of Layer-wised Model Aggregation for Personalized Federated Learning

PyTorch Implementation of Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Some common CUDA kernel implementations (Not the fastest).

Implementation of SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

No description provided.

torchcomms: a modern PyTorch communications API

📚LeetCUDA: Modern CUDA Learn Notes with PyTorch for Beginners🐑, 200+ CUDA Kernels, Tensor Cores, HGEMM, FA-2 MMA.🎉

PyTorch Implementation of Federated Reconstruction: Partially Local Federated Learning

TritonParse: A Compiler Tracer, Visualizer, and Reproducer for Triton Kernels

Implementation of FedAvg

A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.

A tweak to enhance Spotify experience

Material for gpu-mode lectures

SGLang is a fast serving framework for large language models and vision language models.

LLM training in simple, raw C/CUDA

Community maintained hardware plugin for vLLM on Ascend

Implementation of Improving Federated Learning Personalization via Model Agnostic Meta Learning

A high-throughput and memory-efficient inference and serving engine for LLMs

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.