Making large AI models cheaper, faster and more accessible
-
Updated
Dec 2, 2024 - Python
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.
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
A GPipe implementation in PyTorch
飞桨大模型开发套件,提供大语言模型、跨模态大模型、生物计算大模型等领域的全流程开发工具链。
LiBai(李白): A Toolbox for Large-Scale Distributed Parallel Training
Personal Project: MPP-Qwen14B & MPP-Qwen-Next(Multimodal Pipeline Parallel based on Qwen-LM). Support [video/image/multi-image] {sft/conversations}. Don't let the poverty limit your imagination! Train your own 8B/14B LLaVA-training-like MLLM on RTX3090/4090 24GB.
InternEvo is an open-sourced lightweight training framework aims to support model pre-training without the need for extensive dependencies.
Easy Parallel Library (EPL) is a general and efficient deep learning framework for distributed model training.
A curated list of awesome projects and papers for distributed training or inference
Serving Inside Pytorch
Large scale 4D parallelism pre-training for 🤗 transformers in Mixture of Experts *(still work in progress)*
An Efficient Pipelined Data Parallel Approach for Training Large Model
Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines.
FTPipe and related pipeline model parallelism research.
Implementation of autoregressive language model using improved Transformer and DeepSpeed pipeline parallelism.
Official implementation of DynPartition: Automatic Optimal Pipeline Parallelism of Dynamic Neural Networks over Heterogeneous GPU Systems for Inference Tasks
Model parallelism for NN architectures with skip connections (eg. ResNets, UNets)
Docs for torchpipe: https://github.com/torchpipe/torchpipe
Development of Project HPGO | Hybrid Parallelism Global Orchestration
Add a description, image, and links to the pipeline-parallelism topic page so that developers can more easily learn about it.
To associate your repository with the pipeline-parallelism topic, visit your repo's landing page and select "manage topics."