Rootski - Full codebase for rootski.io (without the data)

Overview

breakdown-svg

📣 Welcome to the Rootski codebase!

This is the codebase for the application running at rootski.io.

🗒 Note: You can find information and training on the architecture, ticket board, development practices, and how to contribute on our knowledge base.

Rootski is a full-stack application for studying the Russian language by learning roots.

Rootski uses an A.I. algorithm called a "transformer" to break Russian words into roots. Rootski enriches the word breakdowns with data such as definitions, grammar information, related words, and examples and then displays this information to users for them to study.

How is the Rootski project run? (Hint, get involved here 😃 )

Rootski is developed by volunteers!

We use Rootski as a platform to learn and mentor anyone with an interest in frontend/backend development, developing data science models, data engineering, MLOps, DevOps, UX, and running a business. Although the code is open-source, the license for reuse and redistribution is tightly restricted.

The premise for building Rootski "in the open" is this: possibly the best ways to learn to write production-ready, high quality software is to

  1. explore other high-quality software that is already written
  2. develop an application meant to support a large number of users
  3. work with experienced mentors

For better or worse, it's hard to find code for large software systems built to be hosted in the cloud and used by a large number of customers. This is because virtually all apps that fit this description... are proprietary 🤣 . That makes (1) hard.

(2) can be inaccessible due to the amount of time it takes to write well-written software systems without a team (or mentorship). If you're only interested in a sub-part of engineering, or if you are a beginner, it can be infeasible to build an entire production system on your own. Think of this as working on a personal project... with a bunch of other fun people working on it with you.

Contributors

Onboarded and contributed features :D

  • Eric Riddoch - Been working on Rootski for 3 years and counting!
  • Ryan Gardner - Helping with all of the legal/business aspects and dabbling in development

Friends

Completed a lot of the Rootski onboarding and chat with us in our Slack workspace about miscellanious code questions, careers, advice, etc.

  • Isaac Robbins - Learning and building experience in MLOps and DevOps!
  • Colin Varney - Full-stack python guy. Is working his first full-time software job!
  • Fazleem Baig - MLOps guy. Quite experienced with Python and learning about AWS. Working for an AI startup in Canada.
  • Ayse (Aysha) Arslan - Learning about all things MLOps. Working her first MLE/MLOps job!
  • Sebastian Sanchez - Learning about frontend development.
  • Yashwanth (Yash) Kumar - Finishing up the Georgia Tech online masters in CS.






The Technical Stuff

How to deploy an entire Rootski environment from scratch

Going through this, you'll notice that there are several one-time, manual steps. This is common even for teams with a heavily automated infrastructure-as-code workflow, particularly when it comes to the creation of users and storing of credentials.

Once these steps are complete, all subsequent interactions with our Rootski infrastructure can be done using our infrastructure as code and other automation tools.

1. Create an AWS account and user

  1. Create an IAM user with programmatic access
  2. Install the AWS CLI
  3. Run aws configure --profile rootski and copy the credentials from step (1). Set the region to us-west-2.

🗒 Note: this IAM user will need sufficient permissions to create and access the infrastructure that will be discussed below. This includes creating several types of infrastructure using CloudFormation.

2. Create an SSH key pair

  1. In the AWS console, go to EC2 and create an SSH key pair named rootski.
  2. Download the key pair.
  3. Save the key pair somewhere you won't forget. If the pair isn't already named, I like to rename them and store them at ~/.ssh/rootski/rootski.id_rsa (private key) and ~/.ssh/rootski/rootski.id_rsa.pub (public key).
  4. Create a new GitHub account for a "Machine User". Copy/paste the contents of rootski.id_rsa.pub into any boxes you have to to make this work :D this "machine user" is now authorized to clone the rootski repository!

3. Create several parameters in AWS SSM Parameter Store

Parameter Description
/rootski/ssh/private_key The contents of the private key needed to clone the rootski repository.
/rootski/prod/database_config A stringified JSON object with database connection information (see below)
{
    "postgres_user": "rootski-db-user",
    "postgres_password": "rootski-db-pass",
    "postgres_host": "database.rootski.io",
    "postgres_port": "5432",
    "postgres_db": "rootski-db-database-name"
}

4. Purchase a domain name that happens to be rootski.io

You know, the domain name rootski.io is hard coded in a few places throughout the Rootski infrastructure. It felt wasteful to parameterize this everywhere since... it's unlikely that we will ever change our domain name.

If we ever have a need for this, we can revisit it :D

5. Create an ACM TLS certificate verified with the DNS challenge for *.rootski.io

You'll need to do this in the AWS console. This certificate will allow us to access rootski.io and all of its subdomains over HTTPS. You'll need the ARN of this certificate for a later step.

4. Create the rootski infrastructure

Before running these commands, copy/paste the ARN of the *.rootski.io ACM certificate into the appropriate place in infrastructure/iac/cloudformation/front-end/static-website.yml.

# create the S3 bucket and Route53 hosted zone for hosting the React application as a static site
...

# create the AWS Cognito user pool
...

# create the AWS Lightsail instance with the backend database (simultaneously deploys the database)
...

# deploy the API Gateway and Lambda function
...

5. Deploy the frontend site

make deploy-frontend

DONE!

Owner
Eric
In modern Applied Mathematics, we specialize in algorithms. I'm a data scientist with a strong background in algorithm design and software development.
Eric
Search for documents in a domain through Google. The objective is to extract metadata

MetaFinder - Metadata search through Google _____ __ ___________ .__ .___ / \

Josué Encinar 85 Dec 16, 2022
Toward a Visual Concept Vocabulary for GAN Latent Space, ICCV 2021

Toward a Visual Concept Vocabulary for GAN Latent Space Code and data from the ICCV 2021 paper Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Kl

Sarah Schwettmann 13 Dec 23, 2022
Top2Vec is an algorithm for topic modeling and semantic search.

Top2Vec is an algorithm for topic modeling and semantic search. It automatically detects topics present in text and generates jointly embedded topic, document and word vectors.

Dimo Angelov 2.4k Jan 06, 2023
Graphical user interface for Argos Translate

Argos Translate GUI Website | GitHub | PyPI Graphical user interface for Argos Translate. Install pip3 install argostranslategui

Argos Open Tech 16 Dec 07, 2022
BiQE: Code and dataset for the BiQE paper

BiQE: Bidirectional Query Embedding This repository includes code for BiQE and the datasets introduced in Answering Complex Queries in Knowledge Graph

Bhushan Kotnis 1 Oct 20, 2021
⛵️The official PyTorch implementation for "BERT-of-Theseus: Compressing BERT by Progressive Module Replacing" (EMNLP 2020).

BERT-of-Theseus Code for paper "BERT-of-Theseus: Compressing BERT by Progressive Module Replacing". BERT-of-Theseus is a new compressed BERT by progre

Kevin Canwen Xu 284 Nov 25, 2022
NLP Core Library and Model Zoo based on PaddlePaddle 2.0

PaddleNLP 2.0拥有丰富的模型库、简洁易用的API与高性能的分布式训练的能力,旨在为飞桨开发者提升文本建模效率,并提供基于PaddlePaddle 2.0的NLP领域最佳实践。

6.9k Jan 01, 2023
JaQuAD: Japanese Question Answering Dataset

JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension (2022, Skelter Labs)

SkelterLabs 84 Dec 27, 2022
Transformer-based Text Auto-encoder (T-TA) using TensorFlow 2.

T-TA (Transformer-based Text Auto-encoder) This repository contains codes for Transformer-based Text Auto-encoder (T-TA, paper: Fast and Accurate Deep

Jeong Ukjae 13 Dec 13, 2022
Code for the Python code smells video on the ArjanCodes channel.

7 Python code smells This repository contains the code for the Python code smells video on the ArjanCodes channel (watch the video here). The example

55 Dec 29, 2022
Code for "Generative adversarial networks for reconstructing natural images from brain activity".

Reconstruct handwritten characters from brains using GANs Example code for the paper "Generative adversarial networks for reconstructing natural image

K. Seeliger 2 May 17, 2022
Official code for Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset

Official code for our Interspeech 2021 - Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset [1]*. Visually-grounded spoken language datasets c

Ian Palmer 3 Jan 26, 2022
Maix Speech AI lib, including ASR, chat, TTS etc.

Maix-Speech 中文 | English Brief Now only support Chinese, See 中文 Build Clone code by: git clone https://github.com/sipeed/Maix-Speech Compile x86x64 c

Sipeed 267 Dec 25, 2022
A calibre plugin that generates Word Wise and X-Ray files then sends them to Kindle. Supports KFX, AZW3 and MOBI eBooks. X-Ray supports 18 languages.

WordDumb A calibre plugin that generates Word Wise and X-Ray files then sends them to Kindle. Supports KFX, AZW3 and MOBI eBooks. Languages X-Ray supp

172 Dec 29, 2022
Huggingface Transformers + Adapters = ❤️

adapter-transformers A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models adapter-transformers is an extension of

AdapterHub 1.2k Jan 09, 2023
SEJE is a prototype for the paper Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering.

SEJE is a prototype for the paper Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering. Contents Inst

0 Oct 21, 2021
nlp-tutorial is a tutorial for who is studying NLP(Natural Language Processing) using Pytorch

nlp-tutorial is a tutorial for who is studying NLP(Natural Language Processing) using Pytorch. Most of the models in NLP were implemented with less than 100 lines of code.(except comments or blank li

Tae-Hwan Jung 11.9k Jan 08, 2023
Malaya-Speech is a Speech-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow.

Malaya-Speech is a Speech-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow. Documentation Proper documentation is available at

HUSEIN ZOLKEPLI 151 Jan 05, 2023
Training RNNs as Fast as CNNs

News SRU++, a new SRU variant, is released. [tech report] [blog] The experimental code and SRU++ implementation are available on the dev branch which

Tao Lei 14 Dec 12, 2022
NLP command-line assistant powered by OpenAI

NLP command-line assistant powered by OpenAI

Axel 16 Dec 09, 2022