FastyAPI is a Stack boilerplate optimised for heavy loads.

Related tags

Deep LearningFastyAPI
Overview

Logo

FastyAPI

A FastAPI based Stack boilerplate for heavy loads.
Explore the docs »

View Demo · Report Bug · Request Feature

Table of Contents
  1. About The Project
  2. Getting Started
  3. Roadmap
  4. Contributing
  5. License

About The Project

FastyAPI is a FastAPI based Stack boilerplate designed for heavy workloads and simple developement in mind.

Here's why:

  • FastAPI provides such a great developement experience due to its simple structure and the auto generated docs.
  • we've improves this further by providing you with a simple design pattern, no subfolders <3
  • every Stack element is carefully chosen and tested/optimised against heavy workloads
  • boiletplate code for different situations, websocket, crud etc.. yet without bloat.

(back to top)

Built With

Our stack is as follows

  • Gunicorn is a Python Web Server Gateway Interface (WSGI) HTTP server. It is a pre-fork worker model
    • Gunicorn would act as a process manager, listening on the port and the IP. And it would transmit the communication to the worker processes running the Uvicorn class.
  • FastAPI is a Web framework for developing RESTful APIs in Python.
    • minimalistic, simple and scales well
  • Celery soon + optional
  • Flower soon + optional
  • Redis is an in-memory data structure store, used as a distributed, in-memory key–value database, cache and message broker
  • Motor presents a coroutine-based API for non-blocking access to MongoDB
  • MongoDB is a source-available cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas.
    • Sharding is the process of storing data records across multiple machines and it is MongoDB's approach to meeting the demands of data growth.
  • Docker container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another.

(back to top)

Getting Started

Set of instructions to get started with FastyAPI

Prerequisites

  • Python3
  • pip3
  • venv
    python3 -m pip install --user virtualenv

Environment setup

  1. Create the environment
    python3 -m venv .
  2. Activate the environment
    source env/bin/activate

Installation

  1. Clone the repo
    git clone https://github.com/achaayb/FastyAPI
  2. Install the dependencies
    cd FastyAPI 
    pip3 install -r requirements.txt

Running and testing

  1. run uvicorn
    uvicorn app:app --reload
  2. test the app
    • navigate to : http://localhost:8000
    • response should be something like this :
      {"data":"","code":"success","message":"FastyAPI live!"}

(back to top)

Roadmap

  • Base boilerplate
  • Follow a naming convention
  • Add comments and stuff
  • Optimise the base boilerplate
  • Finish up the base stack
    • Gunicorn w/uvicorn workers
    • FastAPI
    • Motor
    • Mongodb (sharding)
  • Stress test 1
    • Normal test (fork)
    • Websocket stress (fork)
  • implement stack extentions
    • Celery
    • Redis
    • Flower
  • Stress test 2
    • Normal test (fork)
    • Cpu bound operations test (fork)
  • Docker

See the open issues for a full list of proposed features (and known issues).

(back to top)

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

(back to top)

License

Distributed under the MIT License. See LICENSE.txt for more information.

(back to top)

Project Link: https://github.com/achaayb/FastyAPI

(back to top)

Owner
Ali Chaayb
Backend developer, cybersecurity and scaling enthusiast.
Ali Chaayb
Molecular AutoEncoder in PyTorch

MolEncoder Molecular AutoEncoder in PyTorch Install $ git clone https://github.com/cxhernandez/molencoder.git && cd molencoder $ python setup.py insta

Carlos Hernández 80 Dec 05, 2022
Code accompanying the paper on "An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers" published at NeurIPS, 2021

Code for "An Empirical Investigation of Domian Generalization with Empirical Risk Minimizers" (NeurIPS 2021) Motivation and Introduction Domain Genera

Meta Research 15 Dec 27, 2022
Code for the paper "MASTER: Multi-Aspect Non-local Network for Scene Text Recognition" (Pattern Recognition 2021)

MASTER-PyTorch PyTorch reimplementation of "MASTER: Multi-Aspect Non-local Network for Scene Text Recognition" (Pattern Recognition 2021). This projec

Wenwen Yu 255 Dec 29, 2022
《LightXML: Transformer with dynamic negative sampling for High-Performance Extreme Multi-label Text Classification》(AAAI 2021) GitHub:

LightXML: Transformer with dynamic negative sampling for High-Performance Extreme Multi-label Text Classification

76 Dec 05, 2022
Official PyTorch Implementation for "Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes"

PVDNet: Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes This repository contains the official PyTorch implementatio

Junyong Lee 98 Nov 06, 2022
A Human-in-the-Loop workflow for creating HD images from text

A Human-in-the-Loop? workflow for creating HD images from text DALL·E Flow is an interactive workflow for generating high-definition images from text

Jina AI 2.5k Jan 02, 2023
Semi-supervised Domain Adaptation via Minimax Entropy

Semi-supervised Domain Adaptation via Minimax Entropy (ICCV 2019) Install pip install -r requirements.txt The code is written for Pytorch 0.4.0, but s

Vision and Learning Group 243 Jan 09, 2023
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu,

GEMS Lab: Graph Exploration & Mining at Scale, University of Michigan 70 Dec 18, 2022
Code for the paper "Improved Techniques for Training GANs"

Status: Archive (code is provided as-is, no updates expected) improved-gan code for the paper "Improved Techniques for Training GANs" MNIST, SVHN, CIF

OpenAI 2.2k Jan 01, 2023
Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)

Swin-Transformer-Tensorflow A direct translation of the official PyTorch implementation of "Swin Transformer: Hierarchical Vision Transformer using Sh

52 Dec 29, 2022
🐾 Semantic segmentation of paws from cute pet images (PyTorch)

🐾 paw-segmentation 🐾 Semantic segmentation of paws from cute pet images 🐾 Semantic segmentation of paws from cute pet images (PyTorch) 🐾 Paw Segme

Zabir Al Nazi Nabil 3 Feb 01, 2022
Code and data for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources.

Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS) The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limit

Yu Bai 43 Nov 07, 2022
Revisting Open World Object Detection

Revisting Open World Object Detection Installation See INSTALL.md. Dataset Our new data division is based on COCO2017. We divide the training set into

58 Dec 23, 2022
Official implementation of the Neurips 2021 paper Searching Parameterized AP Loss for Object Detection.

Parameterized AP Loss By Chenxin Tao, Zizhang Li, Xizhou Zhu, Gao Huang, Yong Liu, Jifeng Dai This is the official implementation of the Neurips 2021

46 Jul 06, 2022
Spatial color quantization in Rust

rscolorq Rust port of Derrick Coetzee's scolorq, based on the 1998 paper "On spatial quantization of color images" by Jan Puzicha, Markus Held, Jens K

Collyn O'Kane 37 Dec 22, 2022
QICK: Quantum Instrumentation Control Kit

QICK: Quantum Instrumentation Control Kit The QICK is a kit of firmware and software to use the Xilinx RFSoC to control quantum systems. It consists o

81 Dec 15, 2022
Code for the paper Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations (AKBC 2021).

Relation Prediction as an Auxiliary Training Objective for Knowledge Base Completion This repo provides the code for the paper Relation Prediction as

Facebook Research 85 Jan 02, 2023
The Official TensorFlow Implementation for SPatchGAN (ICCV2021)

SPatchGAN: Official TensorFlow Implementation Paper "SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation"

39 Dec 30, 2022
This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

9 Sep 01, 2022
Pyramid addon for OpenAPI3 validation of requests and responses.

Validate Pyramid views against an OpenAPI 3.0 document Peace of Mind The reason this package exists is to give you peace of mind when providing a REST

Pylons Project 79 Dec 30, 2022