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
Square Root Bundle Adjustment for Large-Scale Reconstruction

RootBA: Square Root Bundle Adjustment Project Page | Paper | Poster | Video | Code Table of Contents Citation Dependencies Installing dependencies on

Nikolaus Demmel 205 Dec 20, 2022
[ICCV 2021 Oral] Mining Latent Classes for Few-shot Segmentation

Mining Latent Classes for Few-shot Segmentation Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao. This codebase contains baseline of our paper Mini

Lihe Yang 66 Nov 29, 2022
Resources related to EMNLP 2021 paper "FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations"

FAME: Feature-based Adversarial Meta-Embeddings This is the companion code for the experiments reported in the paper "FAME: Feature-Based Adversarial

Bosch Research 11 Nov 27, 2022
Code for paper ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.

Who Left the Dogs Out? Evaluation and demo code for our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization

Benjamin Biggs 29 Dec 28, 2022
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.

Visdom A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Python. Overview Concepts Setup Usage API To

FOSSASIA 9.4k Jan 07, 2023
The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

PRIMER The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization. PRIMER is a pre-trained model for mu

AI2 114 Jan 06, 2023
Pytorch implementation of BRECQ, ICLR 2021

BRECQ Pytorch implementation of BRECQ, ICLR 2021 @inproceedings{ li&gong2021brecq, title={BRECQ: Pushing the Limit of Post-Training Quantization by Bl

Yuhang Li 148 Dec 28, 2022
Orange Chicken: Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation

Orange Chicken: Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation This repository contains code and data f

Zoey Liu 0 Jan 07, 2022
Repository for Multimodal AutoML Benchmark

Benchmarking Multimodal AutoML for Tabular Data with Text Fields Repository for the NeurIPS 2021 Dataset Track Submission "Benchmarking Multimodal Aut

Xingjian Shi 44 Nov 24, 2022
Customer-Transaction-Analysis - This analysis is based on a synthesised transaction dataset containing 3 months worth of transactions for 100 hypothetical customers.

Customer-Transaction-Analysis - This analysis is based on a synthesised transaction dataset containing 3 months worth of transactions for 100 hypothetical customers. It contains purchases, recurring

Ayodeji Yekeen 1 Jan 01, 2022
Unofficial PyTorch implementation of MobileViT based on paper "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer".

MobileViT RegNet Unofficial PyTorch implementation of MobileViT based on paper MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TR

Hong-Jia Chen 91 Dec 02, 2022
Aircraft design optimization made fast through modern automatic differentiation

Aircraft design optimization made fast through modern automatic differentiation. Plug-and-play analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.

Peter Sharpe 394 Dec 23, 2022
Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021

DIFFNet This repo is for Self-Supervised Monocular Depth Estimation with Internal Feature Fusion(arXiv), BMVC2021 A new backbone for self-supervised d

Hang 94 Dec 25, 2022
Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++).

Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++). Built in TensorFlow 2.5. Configured for vox

Diagnostic Image Analysis Group 32 Dec 08, 2022
Model Zoo for AI Model Efficiency Toolkit

We provide a collection of popular neural network models and compare their floating point and quantized performance.

Qualcomm Innovation Center 137 Jan 03, 2023
PyArmadillo: an alternative approach to linear algebra in Python

PyArmadillo is a linear algebra library for the Python language, with an emphasis on ease of use.

Terry Zhuo 58 Oct 11, 2022
[NeurIPS 2020] This project provides a strong single-stage baseline for Long-Tailed Classification, Detection, and Instance Segmentation (LVIS).

A Strong Single-Stage Baseline for Long-Tailed Problems This project provides a strong single-stage baseline for Long-Tailed Classification (under Ima

Kaihua Tang 514 Dec 23, 2022
Codebase for "Revisiting spatio-temporal layouts for compositional action recognition" (Oral at BMVC 2021).

Revisiting spatio-temporal layouts for compositional action recognition Codebase for "Revisiting spatio-temporal layouts for compositional action reco

Gorjan 20 Dec 15, 2022
Bayesian Optimization using GPflow

Note: This package is for use with GPFlow 1. For Bayesian optimization using GPFlow 2 please see Trieste, a joint effort with Secondmind. GPflowOpt GP

GPflow 257 Dec 26, 2022
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection

Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection Introduction This repository includes codes and models of "Effect of De

Amir Abbasi 5 Sep 05, 2022