Unified file system operation experience for different backend

Overview

megfile - Megvii FILE library

build docs Latest version Support python versions License

megfile provides a silky operation experience with different backends (currently including local file system and OSS), which enable you to focus more on the logic of your own project instead of the question of "Which backend is used for this file?"

megfile provides:

  • Almost unified file system operation experience. Target path can be easily moved from local file system to OSS.
  • Complete boundary case handling. Even the most difficult (or even you can't even think of) boundary conditions, megfile can help you easily handle it.
  • Perfect type hints and built-in documentation. You can enjoy the IDE's auto-completion and static checking.
  • Semantic version and upgrade guide, which allows you enjoy the latest features easily.

megfile's advantages are:

  • smart_open can open resources that use various protocols, including fs, s3, http(s) and stdio. Especially, reader / writer of s3 in megfile is implemented with multi-thread, which is faster than known competitors.
  • smart_glob is available on s3. And it supports zsh extended pattern syntax of [], e.g. s3://bucket/video.{mp4,avi}.
  • All-inclusive functions like smart_exists / smart_stat / smart_sync. If you don't find the functions you want, submit an issue.
  • Compatible with pathlib.Path interface, referring to S3Path and SmartPath.

Quick Start

Here's an example of writing a file to OSS, syncing to local, reading and finally deleting it.

from megfile import smart_open, smart_exists, smart_sync, smart_remove, smart_glob
from megfile.smart_path import SmartPath

# open a file in s3 bucket
with smart_open('s3://playground/refile-test', 'w') as fp:
    fp.write('refile is not silver bullet')

# test if file in s3 bucket exist
smart_exists('s3://playground/refile-test')

# copy files or directories
smart_sync('s3://playground/refile-test', '/tmp/playground')

# remove files or directories
smart_remove('s3://playground/refile-test')

# glob files or directories in s3 bucket
smart_glob('s3://playground/video-?.{mp4,avi}')

# or in local file system
smart_exists('/tmp/playground/refile-test')

# smart_open also support protocols like http / https
smart_open('https://www.google.com')

# SmartPath interface
path = SmartPath('s3://playground/megfile-test')
if path.exists():
    with path.open() as f:
        result = f.read(7)
        assert result == b'megfile'

Installation

PyPI

pip3 install megfile

You can specify megfile version as well

pip3 install "megfile~=0.0"

Build from Source

megfile can be installed from source

git clone [email protected]:megvii-research/megfile.git
cd megfile
pip3 install -U .

Development Environment

git clone [email protected]:megvii-research/megfile.git
cd megfile
sudo apt install libgl1-mesa-glx libfuse-dev fuse
pip3 install -r requirements.txt -r requirements-dev.txt

How to Contribute

  • We welcome everyone to contribute code to the megfile project, but the contributed code needs to meet the following conditions as much as possible:

    You can submit code even if the code doesn't meet conditions. The project members will evaluate and assist you in making code changes

    • Code format: Your code needs to pass code format check. megfile uses yapf as lint tool and the version is locked at 0.27.0. The version lock may be removed in the future

    • Static check: Your code needs complete type hint. megfile uses pytype as static check tool. If pytype failed in static check, use # pytype: disable=XXX to disable the error and please tell us why you disable it.

      Note : Because pytype doesn't support variable type annation, the variable type hint format introduced by py36 cannot be used.

      i.e. variable: int is invalid, replace it with variable # type: int

    • Test: Your code needs complete unit test coverage. megfile uses pyfakefs and moto as local file system and OSS virtual environment in unit tests. The newly added code should have a complete unit test to ensure the correctness

  • You can help to improve megfile in many ways:

    • Write code.
    • Improve documentation.
    • Report or investigate bugs and issues.
    • If you find any problem or have any improving suggestion, submit a new issuse as well. We will reply as soon as possible and evaluate whether to adopt.
    • Review pull requests.
    • Star megfile repo.
    • Recommend megfile to your friends.
    • Any other form of contribution is welcomed.
Owner
MEGVII Research
Power Human with AI. 持续创新拓展认知边界 非凡科技成就产品价值
MEGVII Research
DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

Jason Antic 15.8k Jan 04, 2023
bio_inspired_min_nets_improve_the_performance_and_robustness_of_deep_networks

Code Submission for: Bio-inspired Min-Nets Improve the Performance and Robustness of Deep Networks Run with docker To build a docker environment, chan

0 Dec 09, 2021
Revisting Open World Object Detection

Revisting Open World Object Detection Installation See INSTALL.md. Dataset Our n

58 Dec 23, 2022
Tiny Object Detection in Aerial Images.

AI-TOD AI-TOD is a dataset for tiny object detection in aerial images. [Paper] [Dataset] Description AI-TOD comes with 700,621 object instances for ei

jwwangchn 116 Dec 30, 2022
PyTorch code for our paper "Attention in Attention Network for Image Super-Resolution"

Under construction... Attention in Attention Network for Image Super-Resolution (A2N) This repository is an PyTorch implementation of the paper "Atten

Haoyu Chen 71 Dec 30, 2022
A Python library that provides a simplified alternative to DBAPI 2

A Python library that provides a simplified alternative to DBAPI 2. It provides a facade in front of DBAPI 2 drivers.

Tony Locke 44 Nov 17, 2021
Complete* list of autonomous driving related datasets

AD Datasets Complete* and curated list of autonomous driving related datasets Contributing Contributions are very welcome! To add or update a dataset:

Daniel Bogdoll 13 Dec 19, 2022
Fast Differentiable Matrix Sqrt Root

Fast Differentiable Matrix Sqrt Root Geometric Interpretation of Matrix Square Root and Inverse Square Root This repository constains the official Pyt

YueSong 42 Dec 30, 2022
A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perform basic tasks.

AI_Personal_Voice_Assistant_Using_Python A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perf

Chumui Tripura 1 Oct 30, 2021
Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other

ML_Model_implementaion Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other dectree_model: Implementation o

Anshuman Dalai 3 Jan 24, 2022
Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)

Self-labelling via simultaneous clustering and representation learning 🆗 🆗 🎉 NEW models (20th August 2020): Added standard SeLa pretrained torchvis

Yuki M. Asano 469 Jan 02, 2023
Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling

Permutation Invariant Graph Generation via Score-Based Generative Modeling This repo contains the official implementation for the paper Permutation In

64 Dec 29, 2022
A PyTorch re-implementation of the paper 'Exploring Simple Siamese Representation Learning'. Reproduced the 67.8% Top1 Acc on ImageNet.

Exploring simple siamese representation learning This is a PyTorch re-implementation of the SimSiam paper on ImageNet dataset. The results match that

Taojiannan Yang 72 Nov 09, 2022
Code for "Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation". [AAAI 2021]

Graph Evolving Meta-Learning for Low-resource Medical Dialogue Generation Code to be further cleaned... This repo contains the code of the following p

Shuai Lin 29 Nov 01, 2022
Perfect implement. Model shared. x0.5 (Top1:60.646) and 1.0x (Top1:69.402).

Shufflenet-v2-Pytorch Introduction This is a Pytorch implementation of faceplusplus's ShuffleNet-v2. For details, please read the following papers:

423 Dec 07, 2022
Code for the ICCV2021 paper "Personalized Image Semantic Segmentation"

PSS: Personalized Image Semantic Segmentation Paper PSS: Personalized Image Semantic Segmentation Yu Zhang, Chang-Bin Zhang, Peng-Tao Jiang, Ming-Ming

张宇 15 Jul 09, 2022
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

512x512 flowers after 12 hours of training, 1 gpu 256x256 flowers after 12 hours of training, 1 gpu Pizza 'Lightweight' GAN Implementation of 'lightwe

Phil Wang 1.5k Jan 02, 2023
Unofficial Tensorflow-Keras implementation of Fastformer based on paper [Fastformer: Additive Attention Can Be All You Need](https://arxiv.org/abs/2108.09084).

Fastformer-Keras Unofficial Tensorflow-Keras implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need. Tensorflo

Yam Peleg 10 Jan 30, 2022
Public implementation of the Convolutional Motif Kernel Network (CMKN) architecture

CMKN Implementation of the convolutional motif kernel network (CMKN) introduced in Ditz et al., "Convolutional Motif Kernel Network", 2021. Testing Yo

1 Nov 17, 2021
IsoGCN code for ICLR2021

IsoGCN The official implementation of IsoGCN, presented in the ICLR2021 paper Isometric Transformation Invariant and Equivariant Graph Convolutional N

horiem 39 Nov 25, 2022