A GPipe implementation in PyTorch
-
Updated
Sep 18, 2020 - Python
A GPipe implementation in PyTorch
An Efficient Pipelined Data Parallel Approach for Training Large Model
Development of Project HPGO | Hybrid Parallelism Global Orchestration
Implementation of autoregressive language model using improved Transformer and DeepSpeed pipeline parallelism.
Model parallelism for NN architectures with skip connections (eg. ResNets, UNets)
Easy Parallel Library (EPL) is a general and efficient deep learning framework for distributed model training.
Official implementation of DynPartition: Automatic Optimal Pipeline Parallelism of Dynamic Neural Networks over Heterogeneous GPU Systems for Inference Tasks
pipeDejavu: Hardware-aware Latency Predictable, Differentiable Search for Faster Config and Convergence of Distributed ML Pipeline Parallelism
FTPipe and related pipeline model parallelism research.
Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines.
Large scale 4D parallelism pre-training for 🤗 transformers in Mixture of Experts *(still work in progress)*
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。
A curated list of awesome projects and papers for distributed training or inference
LiBai(李白): A Toolbox for Large-Scale Distributed Parallel Training
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
Bridge the gap between deep learning training and serving
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.
Docs for torchpipe: https://github.com/torchpipe/torchpipe
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.
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."