Youhe Jiang

Expert
@Youhe-Jiang

@ Cambridge CS PhD Candidate @ HKUST CSE RA

IJCAI2023-OptimalShardedDataParallel. [IJCAI2023] An automated parallel training system that combines the advantages from both data and model parallelism. If you have any interests, please visit/star/fork https://github.com/Youhe-Jiang/OptimalShardedDataParallel

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Hetu. A high-performance distributed deep learning system targeting large-scale and automated distributed training.

2

AutoSharded_Transformer_Based_on_PyTorch. Python

2

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

1

Dynasor. Simple extension on vLLM to help you speed up reasoning model without training.

1

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

1

baidu-allreduce. Cuda

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annotation.

1

DCT. A Conditional Independence Test in the Presence of Discretization

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bagua. Bagua is a deep learning training acceleration framework for PyTorch. It provides a one-stop training acceleration solution, including faster distributed training compared to PyTorch DDP, faster dataloader, kernel fusion, and more.

1

ChooseTreeStructure. An algorithm to display tree structure

1

AutoShard_Tool. a tool for recursive autoshard

1

AllReduce-Over-MPI. C++

1

BenchmarkDDP. Python

1

AutoTopology-CostModelForOperators. Modeling and simulating experiments on communication and computing costs.

1

ThunderServe. ThunderServe: Accelerating LLM Serving in Cloud Environments

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