Large dataset storage format for Pytorch

Related tags

Deep Learningpytorchdatasethdf5hdf5-library
Overview

H5Record

Large dataset ( > 100G, <= 1T) storage format for Pytorch (wip)

Support python 3

pip install h5record

Why?

  • Writing large dataset is still a wild west in pytorch. Approaches seen in the wild include:

    • large directory with lots of small files : slow IO when complex file is fetched, deserialized frequently
    • database approach : depend on what kind of database engine used, usually multi-process read is not supported
    • the above method scale non linear in terms of data - storage size

  • TFRecord solved the above problems well ( multiprocess fetch, (de)compression ), fast serialization ( protobuf )

  • However TFRecord port does not support data size evaluation (used frequently by Dataloader ), no index level access available ( important for data evaluation or verification )

H5Record aim to tackle TFRecord problems by compressing the dataset into HDF5 file with an easy to use interface through predefined interfaces ( String, Image, Sequences, Integer).

Some advantage of using H5Record

  • Support multi-process read

  • Relatively simple to use and low technical debt

  • Support compression/de-compression on the fly

  • Quick load to memory if required

Simple usage

pip install h5record
  1. Sentence Similarity
from h5record import H5Dataset, Float, String

schema = (
    String(name='sentence1'),
    String(name='sentence2'),
    Float(name='label')
)
data = [
    ['Sent 1.', 'Sent 2', 0.1],
    ['Sent 3', 'Sent 4', 0.2],
]

def pair_iter():
    for row in data:
        yield {
            'sentence1': row[0],
            'sentence2': row[1],
            'label': row[2]
        }

dataset = H5Dataset(schema, './question_pair.h5', pair_iter())
for idx in range(len(dataset)):
    print(dataset[idx])

Note

Due to in progress development, this package should be use in care in storage with FAT, FAT-32 format

Comparison between different compression algorithm

No chunking is used

Compression Type File size Read speed row/second
no compression 2.0G 2084.55 it/s
lzf 1.7G 1496.14 it/s
gzip 1.1G 843.78 it/s

benchmarked in i7-9700, 1TB NVMe SSD

If you are interested to learn more feel free to checkout the note as well!

You might also like...
A large-scale video dataset for the training and evaluation of 3D human pose estimation models
A large-scale video dataset for the training and evaluation of 3D human pose estimation models

ASPset-510 ASPset-510 (Australian Sports Pose Dataset) is a large-scale video dataset for the training and evaluation of 3D human pose estimation mode

Aiden Nibali 36 Oct 30, 2022
A large-scale video dataset for the training and evaluation of 3D human pose estimation models
A large-scale video dataset for the training and evaluation of 3D human pose estimation models

ASPset-510 (Australian Sports Pose Dataset) is a large-scale video dataset for the training and evaluation of 3D human pose estimation models. It contains 17 different amateur subjects performing 30 sports-related actions each, for a total of 510 action clips.

Aiden Nibali 25 Jun 20, 2021
A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

ICT.MIRACLE lab 75 Dec 26, 2022
Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination
Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination

Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination (ICCV 2021) Dataset License This work is l

DongYoung Kim 33 Jan 4, 2023
LIVECell - A large-scale dataset for label-free live cell segmentation

LIVECell dataset This document contains instructions of how to access the data associated with the submitted manuscript "LIVECell - A large-scale data

Sartorius Corporate Research 112 Jan 7, 2023
A large-scale face dataset for face parsing, recognition, generation and editing.
A large-scale face dataset for face parsing, recognition, generation and editing.

CelebAMask-HQ [Paper] [Demo] CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA da

switchnorm 1.7k Dec 26, 2022
N-Omniglot is a large neuromorphic few-shot learning dataset
N-Omniglot is a large neuromorphic few-shot learning dataset

N-Omniglot [Paper] || [Dataset] N-Omniglot is a large neuromorphic few-shot learning dataset. It reconstructs strokes of Omniglot as videos and uses D

null 11 Dec 5, 2022
PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

PyTorch-LIT PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices. With

Amin Rezaei 157 Dec 11, 2022
This is the dataset and code release of the OpenRooms Dataset.
This is the dataset and code release of the OpenRooms Dataset.

This is the dataset and code release of the OpenRooms Dataset.

Visual Intelligence Lab of UCSD 95 Jan 8, 2023
Comments
  • Example about Image dataset

    Example about Image dataset

    Thanks for your work. Do you have an end to end example about image dataset which includes creating h5records file similar to tfrecord files and then using it in dataloader mechanism just like tf dataset api loader mechanism?

    documentation question 
    opened by meet-minimalist 1
Releases(1.0.4)
Owner
theblackcat102
¯\_(ツ)_/¯
theblackcat102
GitHub Repository https://pypi.org/project/h5record
Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning

Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning Yansong Tang *, Zhenyu Jiang *, Zhenda Xie *, Yue

Zhenyu Jiang 12 Nov 16, 2022
Meta Learning Backpropagation And Improving It (VSML)

Meta Learning Backpropagation And Improving It (VSML) This is research code for the NeurIPS 2021 publication Kirsch & Schmidhuber 2021. Many concepts

Louis Kirsch 22 Dec 21, 2022
Dense matching library based on PyTorch

Dense Matching A general dense matching library based on PyTorch. For any questions, issues or recommendations, please contact Prune at

Prune Truong 399 Dec 28, 2022
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style [NeurIPS 2021] Official code to reproduce the results and data p

Yash Sharma 27 Sep 19, 2022
PyTorch Lightning implementation of Automatic Speech Recognition

lasr Lightening Automatic Speech Recognition An MIT License ASR research library, built on PyTorch-Lightning, for developing end-to-end ASR models. In

Soohwan Kim 40 Sep 19, 2022
NCVX (NonConVeX): A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning.

NCVX NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning. Please check https://ncvx.org for detailed instruction

SUN Group @ UMN 28 Aug 03, 2022
Official PyTorch implementation of "IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos", CVPRW 2021

IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos Introduction This repo is official PyTorch implementatio

Gyeongsik Moon 29 Sep 24, 2022
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

Zhao Jian 3.1k Jan 02, 2023
Distributionally robust neural networks for group shifts

Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization This code implements the g

151 Dec 25, 2022
ECCV2020 paper: Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards. Code and Data.

This repo contains some of the codes for the following paper Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards. Code

Xuewen Yang 56 Dec 08, 2022
Source code and notebooks to reproduce experiments and benchmarks on Bias Faces in the Wild (BFW).

Face Recognition: Too Bias, or Not Too Bias? Robinson, Joseph P., Gennady Livitz, Yann Henon, Can Qin, Yun Fu, and Samson Timoner. "Face recognition:

Joseph P. Robinson 41 Dec 12, 2022
Learning from Synthetic Humans, CVPR 2017

Learning from Synthetic Humans (SURREAL) Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev and Cordelia Schmid,

Gul Varol 538 Dec 18, 2022
📚 A collection of Jupyter notebooks for learning and experimenting with OpenVINO 👓

A collection of ready-to-run Python* notebooks for learning and experimenting with OpenVINO developer tools. The notebooks are meant to provide an introduction to OpenVINO basics and teach developers

OpenVINO Toolkit 840 Jan 03, 2023
Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.

How The New York Times can increase Engagement on Facebook Using machine learning to understand characteristics of news content that garners "high" Fa

Jessica Miles 0 Sep 16, 2021
[CVPRW 21] "BNN - BN = ? Training Binary Neural Networks without Batch Normalization", Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu, Zhiqiang Shen, Zhangyang Wang

BNN - BN = ? Training Binary Neural Networks without Batch Normalization Codes for this paper BNN - BN = ? Training Binary Neural Networks without Bat

VITA 40 Dec 30, 2022
Official implementation of Self-supervised Graph Attention Networks (SuperGAT), ICLR 2021.

SuperGAT Official implementation of Self-supervised Graph Attention Networks (SuperGAT). This model is presented at How to Find Your Friendly Neighbor

Dongkwan Kim 127 Dec 28, 2022
PyTorch Implementation of PIXOR: Real-time 3D Object Detection from Point Clouds

PIXOR: Real-time 3D Object Detection from Point Clouds This is a custom implementation of the paper from Uber ATG using PyTorch 1.0. It represents the

Philip Huang 270 Dec 14, 2022
Pytorch implementation of YOLOX、PPYOLO、PPYOLOv2、FCOS an so on.

简体中文 | English miemiedetection 概述 miemiedetection是女装大佬咩酱基于YOLOX进行二次开发的个人检测库（使用的深度学习框架为pytorch），支持Windows、Linux系统，以女装大佬咩酱的名字命名。miemiedetection是一个不需要安装的

248 Jan 02, 2023
Official Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"

Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral): Official Project Webpage This repository provides the off

Kakao Enterprise Corp. 68 Dec 17, 2022
Out-of-Town Recommendation with Travel Intention Modeling (AAAI2021)

TrainOR_AAAI21 This is the official implementation of our AAAI'21 paper: Haoran Xin, Xinjiang Lu, Tong Xu, Hao Liu, Jingjing Gu, Dejing Dou, Hui Xiong

Jack Xin 13 Oct 19, 2022