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
The original implementation of TNDM used in the NeurIPS 2021 paper (no longer being updated)

TNDM - Targeted Neural Dynamical Modeling Note: This code is no longer being updated. The official re-implementation can be found at: https://github.c

1 Jul 21, 2022
A powerful framework for decentralized federated learning with user-defined communication topology

Scatterbrained Decentralized Federated Learning Scatterbrained makes it easy to build federated learning systems. In addition to traditional federated

Johns Hopkins Applied Physics Laboratory 7 Sep 26, 2022
Rendering color and depth images for ShapeNet models.

Color & Depth Renderer for ShapeNet This library includes the tools for rendering multi-view color and depth images of ShapeNet models. Physically bas

Yinyu Nie 41 Dec 19, 2022
Near-Duplicate Video Retrieval with Deep Metric Learning

Near-Duplicate Video Retrieval with Deep Metric Learning This repository contains the Tensorflow implementation of the paper Near-Duplicate Video Retr

2 Jan 24, 2022
A program to recognize fruits on pictures or videos using yolov5

Yolov5 Fruits Detector Requirements Either Linux or Windows. We recommend Linux for better performance. Python 3.6+ and PyTorch 1.7+. Installation To

Fateme Zamanian 30 Jan 06, 2023
Code for our NeurIPS 2021 paper: Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains

GateL0RD This is a lightweight PyTorch implementation of GateL0RD, our RNN presented in "Sparsely Changing Latent States for Prediction and Planning i

Autonomous Learning Group 16 Nov 03, 2022
pytorch implementation for PointNet

PointNet.pytorch This repo is implementation for PointNet in pytorch. The model is in pointnet/model.py. It is teste

Fei Xia 1.7k Dec 30, 2022
Ağ tarayıcı.Gönderdiği paketler ile ağa bağlı olan cihazların IP adreslerini gösterir.

NetScanner.py Ağ tarayıcı.Gönderdiği paketler ile ağa bağlı olan cihazların IP adreslerini gösterir. Linux'da Kullanımı: git clone https://github.com/

4 Aug 23, 2021
Paddle implementation for "Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation" (NAACL 2021)

L1-Refinement Paddle implementation for "Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation" (NAACL 2021) 🙈 A more detailed readme is co

Lincedo Lab 4 Jun 09, 2021
Combine Tacotron2 and Hifi GAN to generate speech from text

EndToEndTextToSpeech Combine Tacotron2 and Hifi GAN to generate speech from text Download weights Hifi GAN - hifi_gan/checkpoint/ : pretrain 2.5M ste

Phạm Quốc Huy 1 Dec 18, 2021
AdaFocus (ICCV 2021) Adaptive Focus for Efficient Video Recognition

AdaFocus (ICCV 2021) This repo contains the official code and pre-trained models for AdaFocus. Adaptive Focus for Efficient Video Recognition Referenc

Rainforest Wang 115 Dec 21, 2022
Simple object detection app with streamlit

object-detection-app Simple object detection app with streamlit. Upload an image and perform object detection. Adjust the confidence threshold to see

Robin Cole 68 Jan 02, 2023
Bling's Object detection tool

BriVL for Building Applications This repo is used for illustrating how to build applications by using BriVL model. This repo is re-implemented from fo

chuhaojin 47 Nov 01, 2022
Fast Soft Color Segmentation

Fast Soft Color Segmentation

3 Oct 29, 2022
Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022)

Blockwise Sequential Model Learning Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022) For ins

2 Jun 17, 2022
Turn based roguelike in python

pyTB Turn based roguelike in python Documentation can be found here: http://mcgillij.github.io/pyTB/index.html Screenshot Dependencies Written in Pyth

Jason McGillivray 4 Sep 29, 2022
A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network"

DGC-Net: Dense Geometric Correspondence Network This is a PyTorch implementation of our work "DGC-Net: Dense Geometric Correspondence Network" TL;DR A

191 Dec 16, 2022
Submanifold sparse convolutional networks

Submanifold Sparse Convolutional Networks This is the PyTorch library for training Submanifold Sparse Convolutional Networks. Spatial sparsity This li

Facebook Research 1.8k Jan 06, 2023
Syntax-Aware Action Targeting for Video Captioning

Syntax-Aware Action Targeting for Video Captioning Code for SAAT from "Syntax-Aware Action Targeting for Video Captioning" (Accepted to CVPR 2020). Th

59 Oct 13, 2022
A package related to building quasi-fibration symmetries

qf A package related to building quasi-fibration symmetries. If you'd like to learn more about how it works, see the brief explanation and References

Paolo Boldi 1 Dec 01, 2021