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 official repo for CVPR2021——ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search [paper] Introduction This is the official implementation of ViPNAS: Efficient V

Lumin 42 Sep 26, 2022
Chinese Mandarin tts text-to-speech 中文 (普通话) 语音 合成 , by fastspeech 2 , implemented in pytorch, using waveglow as vocoder,

Chinese mandarin text to speech based on Fastspeech2 and Unet This is a modification and adpation of fastspeech2 to mandrin(普通话). Many modifications t

291 Jan 02, 2023
Objax Apache-2Objax (🥉19 · ⭐ 580) - Objax is a machine learning framework that provides an Object.. Apache-2 jax

Objax Tutorials | Install | Documentation | Philosophy This is not an officially supported Google product. Objax is an open source machine learning fr

Google 729 Jan 02, 2023
I3-master-layout - Simple master and stack layout script

Simple master and stack layout script | ------ | ----- | | | | | Ma

Tobias S 18 Dec 05, 2022
Some tentative models that incorporate label propagation to graph neural networks for graph representation learning in nodes, links or graphs.

Some tentative models that incorporate label propagation to graph neural networks for graph representation learning in nodes, links or graphs.

zshicode 1 Nov 18, 2021
PyTorch implementation of Densely Connected Time Delay Neural Network

Densely Connected Time Delay Neural Network PyTorch implementation of Densely Connected Time Delay Neural Network (D-TDNN) in our paper "Densely Conne

Ya-Qi Yu 64 Oct 11, 2022
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation

PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation Winner method of the ICCV-2021 SemKITTI-DVPS Challenge. [arxiv] [

Yuan Haobo 38 Jan 03, 2023
Source code, datasets and trained models for the paper Learning Advanced Mathematical Computations from Examples (ICLR 2021), by François Charton, Amaury Hayat (ENPC-Rutgers) and Guillaume Lample

Maths from examples - Learning advanced mathematical computations from examples This is the source code and data sets relevant to the paper Learning a

Facebook Research 171 Nov 23, 2022
PyTorch Code for NeurIPS 2021 paper Anti-Backdoor Learning: Training Clean Models on Poisoned Data.

Anti-Backdoor Learning PyTorch Code for NeurIPS 2021 paper Anti-Backdoor Learning: Training Clean Models on Poisoned Data. The Anti-Backdoor Learning

Yige-Li 51 Dec 07, 2022
PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM

Quasi-Recurrent Neural Network (QRNN) for PyTorch Updated to support multi-GPU environments via DataParallel - see the the multigpu_dataparallel.py ex

Salesforce 1.3k Dec 28, 2022
Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

Algo-ScriptML Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The goal of this project is not t

Algo Phantoms 81 Nov 26, 2022
Implementation of the paper "Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning"

Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning This is the implementation of the paper "Self-Promoted Prototype Refinement

Kai Zhu 78 Dec 02, 2022
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

SimGNN ⠀⠀⠀ A PyTorch implementation of SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (WSDM 2019). Abstract Graph similarity s

Benedek Rozemberczki 534 Dec 25, 2022
Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases.

Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases. Ivy wraps the functional APIs of existing frameworks. Framework-agnostic functions, libraries an

Ivy 8.2k Jan 02, 2023
Reading Group @mila-iqia on Computational Optimal Transport for Machine Learning Applications

Computational Optimal Transport for Machine Learning Reading Group Over the last few years, optimal transport (OT) has quickly become a central topic

Ali Harakeh 11 Aug 26, 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
A pytorch implementation of faster RCNN detection framework (Use detectron2, it's a masterpiece)

Notice(2019.11.2) This repo was built back two years ago when there were no pytorch detection implementation that can achieve reasonable performance.

Ruotian(RT) Luo 1.8k Jan 01, 2023
Official Pytorch implementation of C3-GAN

Official pytorch implemenation of C3-GAN Contrastive Fine-grained Class Clustering via Generative Adversarial Networks [Paper] Authors: Yunji Kim, Jun

NAVER AI 114 Dec 02, 2022
Implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork.

YOLOv4-large This is the implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork. YOLOv4-CSP YOLOv4-tiny YOLOv4-

Kin-Yiu, Wong 2k Jan 02, 2023
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval (NeurIPS'21)

Baleen Baleen is a state-of-the-art model for multi-hop reasoning, enabling scalable multi-hop search over massive collections for knowledge-intensive

Stanford Future Data Systems 22 Dec 05, 2022