afdwang Goto Github PK

afdwang.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

alpa

Training and serving large-scale neural networks with auto parallelization.

apex

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

awesome-efficient-gnn

Efficient Graph Neural Networks - a curated list of papers and projects

bert-pytorch

Google AI 2018 BERT pytorch implementation

ccf-deadlines

⏰ CCF recommendation conference Deadline Countdowns / Please star this project, thanks~

cuda-samples

Samples for CUDA Developers which demonstrates features in CUDA Toolkit

cutlass

CUDA Templates for Linear Algebra Subroutines

deeplearningexamples

Deep Learning Examples

dip2022_pku

Class project of PKU Digital Image Processing 2022.

flash-attention

Fast and memory-efficient exact attention

gnnpapers

Must-read papers on graph neural networks (GNN)

hetu

A high-performance distributed deep learning system targeting large-scale and automated distributed training. If you have any interests, please visit/star/fork https://github.com/PKU-DAIR/Hetu

hetu-galvatron

Galvatron is an automatic distributed training system designed for Transformer models, including Large Language Models (LLMs). If you have any interests, please visit/star/fork https://github.com/PKU-DAIR/Hetu-Galvatron

hetu_doc

imagebind

ImageBind One Embedding Space to Bind Them All

megatron-lm

Ongoing research training transformer models at scale

nccl-tests

NCCL Tests

nrlpapers

Must-read papers on network representation learning (NRL) / network embedding (NE)

ofasys

OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models

pkuautosubmit_online

萌新友好的，无需下载文件与配环境的，基于Github Actions的，P大学生出入校自动报备程序

plmpapers

Must-read Papers on pre-trained language models.

pvt

sphinx_rtd_theme

Sphinx theme for readthedocs.org

transformerengine

A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.