Sky Computing: Accelerating Geo-distributed Computing in Federated Learning

Overview

Sky Computing

Introduction

Sky Computing is a load-balanced framework for federated learning model parallelism. It adaptively allocate model layers to devices based on the their hardware sepcification. Sky Computing outperforms the baseline method by 55% in training time when training 160-layer BERT in a 64-node cluster. Our paper can be found at https://arxiv.org/abs/2202.11836

The concept sky computing was first introduced by Dr. Katarzyna Keahey et al. They used this word to describe a cross-cloud compute pattern. And later Prof. Stoica and Prof. Shenker generalized this word to geo-distributed computing. Our project is based on their definition. [1] [2]

Installation

git clone [email protected]:hpcaitech/SkyComputing.git
python -m pip install -r requirements.txt
cd ./scaelum
python -m pip install -v -e .

Experiment (using BERT)

To benchmark the Sky Computing, we prepared a single demo which you can run on your cluster to train BERT.

Prepare BERT model

Bidirectional Encoder Representations from Transformers (aka BERT) is one of the state-of-the-art deep learning models for Natural Language Processing. In the experiment part, we use BERT to run a simple benchmark.

cd $PROJECT
mkdir -p BERT/model && cd BERT/model 
wget https://storage.googleapis.com/bert_models/2019_05_30/wwm_uncased_L-24_H-1024_A-16.zip
unzip wwm_uncased_L-24_H-1024_A-16.zip

Prepare GLUE MNLI dataset

The General Language Understanding Evaluation (aka GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems. And the Multi-Genre Natural Language Inference (aka MNLI) is one of the tasks in GLUE, it is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information.

cd $PROJECT
mkdir -p BERT/data && cd BERT/data
wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/1502038877f6a88c225a34450793fbc3ea87eaba/download_glue_data.py
python download_glue_data.py --data_dir ./glue_data --tasks MNLI

Configuration

To run dllb in your cluster, you need to write a config file which contains the necessary information about training, e.g. model layers, useful environment variables. We have provided a well-commentted example, and here are some most important option:

# your project path
PROJECT = os.getenv("PROJECT")

# allocation type, valid values are even, optimal and dynamic
ALLOCATE_TYPE = "even"

# num of node (including the central server)
CORE_NUM = 4

Run scripts

Slurm is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small Linux clusters. We used slurm script to run our experiment.

#!/bin/sh

#SBATCH --job-name=gpu16   # Job name
#SBATCH -o gpu16.o%j       # Name of stdout output file
#SBATCH -e gpu16.e%j       # Name of stderr error file
#SBATCH -N 16              # Node numbers
#SBATCH -n 16              # GPU numbers
#SBATCH --time=02:00:00    # Run time (hh:mm:ss)

# run
python ./ip_addr.py > "./HOST"
srun python ./launch.py -c "./experiment/config.py"

Citation

@misc{zhu2022sky,
      title={Sky Computing: Accelerating Geo-distributed Computing in Federated Learning}, 
      author={Jie Zhu and Shenggui Li and Yang You},
      year={2022},
      eprint={2202.11836},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Reference

@article{keahey2009sky,
  title={Sky computing},
  author={Keahey, Katarzyna and Tsugawa, Mauricio and Matsunaga, Andrea and Fortes, Jose},
  journal={IEEE Internet Computing},
  volume={13},
  number={5},
  pages={43--51},
  year={2009},
  publisher={IEEE}
}
@inproceedings{stoica2021cloud,
  title={From cloud computing to sky computing},
  author={Stoica, Ion and Shenker, Scott},
  booktitle={Proceedings of the Workshop on Hot Topics in Operating Systems},
  pages={26--32},
  year={2021}
}
Owner
HPC-AI Tech
We are a global team to help you train and deploy your AI models
HPC-AI Tech
A Structured Self-attentive Sentence Embedding

Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR

Kaushal Shetty 488 Nov 28, 2022
ManiSkill-Learn is a framework for training agents on SAPIEN Open-Source Manipulation Skill Challenge (ManiSkill Challenge), a large-scale learning-from-demonstrations benchmark for object manipulation.

ManiSkill-Learn ManiSkill-Learn is a framework for training agents on SAPIEN Open-Source Manipulation Skill Challenge, a large-scale learning-from-dem

Hao Su's Lab, UCSD 48 Dec 30, 2022
Unofficial implementation of the ImageNet, CIFAR 10 and SVHN Augmentation Policies learned by AutoAugment using pillow

AutoAugment - Learning Augmentation Policies from Data Unofficial implementation of the ImageNet, CIFAR10 and SVHN Augmentation Policies learned by Au

Philip Popien 1.3k Jan 02, 2023
TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks

TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks [Paper] [Project Website] This repository holds the source code, pretra

Humam Alwassel 83 Dec 21, 2022
Poplar implementation of "Bundle Adjustment on a Graph Processor" (CVPR 2020)

Poplar Implementation of Bundle Adjustment using Gaussian Belief Propagation on Graphcore's IPU Implementation of CVPR 2020 paper: Bundle Adjustment o

Joe Ortiz 34 Dec 05, 2022
[ICCV 2021] A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation

[ICCV 2021] A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation

CodingMan 45 Dec 12, 2022
LERP : Label-dependent and event-guided interpretable disease risk prediction using EHRs

LERP : Label-dependent and event-guided interpretable disease risk prediction using EHRs This is the code for the LERP. Dataset The dataset used is MI

5 Jun 18, 2022
Fortuitous Forgetting in Connectionist Networks

Fortuitous Forgetting in Connectionist Networks Introduction This repository includes reference code for the paper Fortuitous Forgetting in Connection

Hattie Zhou 14 Nov 26, 2022
SingleVC performs any-to-one VC, which is an important component of MediumVC project.

SingleVC performs any-to-one VC, which is an important component of MediumVC project. Here is the official implementation of the paper, MediumVC.

谷下雨 26 Dec 28, 2022
Face recognize and crop them

Face Recognize Cropping Module Source 아이디어 Face Alignment with OpenCV and Python Requirement 필요 라이브러리 imutil dlib python-opence (cv2) Usage 사용 방법 open

Cho Moon Gi 1 Feb 15, 2022
Reimplementation of the paper "Attention, Learn to Solve Routing Problems!" in jax/flax.

JAX + Attention Learn To Solve Routing Problems Reinplementation of the paper Attention, Learn to Solve Routing Problems! using Jax and Flax. Fully su

Gabriela Surita 7 Dec 01, 2022
Learning to Predict Gradients for Semi-Supervised Continual Learning

Learning to Predict Gradients for Semi-Supervised Continual Learning Code for project: "Learning to Predict Gradients for Semi-Supervised Continual Le

Yan Luo 2 Mar 05, 2022
Official Pytorch Implementation of GraphiT

GraphiT: Encoding Graph Structure in Transformers This repository implements GraphiT, described in the following paper: Grégoire Mialon*, Dexiong Chen

Inria Thoth 80 Nov 27, 2022
Official pytorch code for "APP: Anytime Progressive Pruning"

APP: Anytime Progressive Pruning Diganta Misra1,2,3, Bharat Runwal2,4, Tianlong Chen5, Zhangyang Wang5, Irina Rish1,3 1 Mila - Quebec AI Institute,2 L

Landskape AI 12 Nov 22, 2022
Answering Open-Domain Questions of Varying Reasoning Steps from Text

This repository contains the authors' implementation of the Iterative Retriever, Reader, and Reranker (IRRR) model in the EMNLP 2021 paper "Answering Open-Domain Questions of Varying Reasoning Steps

26 Dec 22, 2022
ICRA 2021 - Robust Place Recognition using an Imaging Lidar

Robust Place Recognition using an Imaging Lidar A place recognition package using high-resolution imaging lidar. For best performance, a lidar equippe

Tixiao Shan 293 Dec 27, 2022
An efficient framework for reinforcement learning.

rl: An efficient framework for reinforcement learning Requirements Introduction PPO Test Requirements name version Python =3.7 numpy =1.19 torch =1

16 Nov 30, 2022
An official source code for paper Deep Graph Clustering via Dual Correlation Reduction, accepted by AAAI 2022

Dual Correlation Reduction Network An official source code for paper Deep Graph Clustering via Dual Correlation Reduction, accepted by AAAI 2022. Any

yueliu1999 109 Dec 23, 2022
Official pytorch code for SSC-GAN: Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation(ICCV 2021)

SSC-GAN_repo Pytorch implementation for 'Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation'.PDF SSC-GAN:Sem

tyty 4 Aug 28, 2022
Implementation of SE3-Transformers for Equivariant Self-Attention, in Pytorch.

SE3 Transformer - Pytorch Implementation of SE3-Transformers for Equivariant Self-Attention, in Pytorch. May be needed for replicating Alphafold2 resu

Phil Wang 207 Dec 23, 2022