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
Official Repo of my work for SREC Nandyal Machine Learning Bootcamp

About the Bootcamp A 3-day Machine Learning Bootcamp organised by Department of Electronics and Communication Engineering, Santhiram Engineering Colle

MS 1 Nov 29, 2021
Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"

StrengthNet Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis" https://arxiv.org/abs/2110

RuiLiu 65 Dec 20, 2022
PyTorch Code for the paper "VSE++: Improving Visual-Semantic Embeddings with Hard Negatives"

Improving Visual-Semantic Embeddings with Hard Negatives Code for the image-caption retrieval methods from VSE++: Improving Visual-Semantic Embeddings

Fartash Faghri 441 Dec 05, 2022
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral)

DSA^2 F: Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral) This repo is the official imp

如今我已剑指天涯 46 Dec 21, 2022
Official Implementation for Fast Training of Neural Lumigraph Representations using Meta Learning.

Fast Training of Neural Lumigraph Representations using Meta Learning Project Page | Paper | Data Alexander W. Bergman, Petr Kellnhofer, Gordon Wetzst

Alex 39 Oct 08, 2022
Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks]

Neural Architecture Search for Spiking Neural Networks Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks] (https

Intelligent Computing Lab at Yale University 28 Nov 18, 2022
Patches desktop steam to look like the new steamdeck ui.

steam_deck_ui_patch The Deck UI patch will patch the regular desktop steam to look like the brand new SteamDeck UI. This patch tool currently works on

The_IT_Dude 3 Aug 29, 2022
A Simulation Environment to train Robots in Large Realistic Interactive Scenes

iGibson: A Simulation Environment to train Robots in Large Realistic Interactive Scenes iGibson is a simulation environment providing fast visual rend

Stanford Vision and Learning Lab 493 Jan 04, 2023
Over-the-Air Ensemble Inference with Model Privacy

Over-the-Air Ensemble Inference with Model Privacy This repository contains simulations for our private ensemble inference method. Installation Instal

Selim Firat Yilmaz 1 Jun 29, 2022
Official PyTorch implementation of Segmenter: Transformer for Semantic Segmentation

Segmenter: Transformer for Semantic Segmentation Segmenter: Transformer for Semantic Segmentation by Robin Strudel*, Ricardo Garcia*, Ivan Laptev and

594 Jan 06, 2023
Mememoji - A facial expression classification system that recognizes 6 basic emotions: happy, sad, surprise, fear, anger and neutral.

a project built with deep convolutional neural network and ❤️ Table of Contents Motivation The Database The Model 3.1 Input Layer 3.2 Convolutional La

Jostine Ho 761 Dec 05, 2022
Implementation of Neural Style Transfer in Pytorch

PytorchNeuralStyleTransfer Code to run Neural Style Transfer from our paper Image Style Transfer Using Convolutional Neural Networks. Also includes co

Leon Gatys 396 Dec 01, 2022
This repo is customed for VisDrone.

Object Detection for VisDrone(无人机航拍图像目标检测) My environment 1、Windows10 (Linux available) 2、tensorflow = 1.12.0 3、python3.6 (anaconda) 4、cv2 5、ensemble

53 Jul 17, 2022
This repository contains tutorials for the py4DSTEM Python package

py4DSTEM Tutorials This repository contains tutorials for the py4DSTEM Python package. For more information about py4DSTEM, including installation ins

11 Dec 23, 2022
Project page of the paper 'Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network' (ECCVW 2018)

EPSR (Enhanced Perceptual Super-resolution Network) paper This repo provides the test code, pretrained models, and results on benchmark datasets of ou

Subeesh Vasu 78 Nov 19, 2022
PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

Study-CSRNet-pytorch This is the PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

0 Mar 01, 2022
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution (CVPR2021)

MASA-SR Official PyTorch implementation of our CVPR2021 paper MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Re

DV Lab 126 Dec 20, 2022
Unofficial PyTorch implementation of TokenLearner by Google AI

tokenlearner-pytorch Unofficial PyTorch implementation of TokenLearner by Ryoo et al. from Google AI (abs, pdf) Installation You can install TokenLear

Rishabh Anand 46 Dec 20, 2022
StyleGAN2-ada for practice

This version of the newest PyTorch-based StyleGAN2-ada is intended mostly for fellow artists, who rarely look at scientific metrics, but rather need a working creative tool. Tested on Python 3.7 + Py

vadim epstein 170 Nov 16, 2022
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You

Prune Truong 71 Nov 18, 2022