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
Multilingual finetuning of Machine Translation model on low-resource languages. Project for Deep Natural Language Processing course.

Low-resource-Machine-Translation This repository contains the code for the project relative to the course Deep Natural Language Processing. The goal o

Andrea Cavallo 3 Jun 22, 2022
BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions

BERTopic BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable

Maarten Grootendorst 3.6k Jan 07, 2023
Transformers implementation for Fall 2021 Clinic

Installation Download miniconda3 if not already installed You can check by running typing conda in command prompt. Use conda to create an environment

Aakash Tripathi 1 Oct 28, 2021
Proquabet - Convert your prose into proquints and then you essentially have Vogon poetry

Proquabet Turn your prose into a constant stream of encrypted and meaningless-so

Milo Fultz 2 Oct 10, 2022
L3Cube-MahaCorpus a Marathi monolingual data set scraped from different internet sources.

L3Cube-MahaCorpus L3Cube-MahaCorpus a Marathi monolingual data set scraped from different internet sources. We expand the existing Marathi monolingual

21 Dec 17, 2022
NeoDays-based tileset for the roguelike CDDA (Cataclysm Dark Days Ahead)

NeoDaysPlus Reduced contrast, expanded, and continuously developed version of the CDDA tileset NeoDays that's being completed with new sprites for mis

0 Nov 12, 2022
NeurIPS'21: Probabilistic Margins for Instance Reweighting in Adversarial Training (Pytorch implementation).

source code for NeurIPS21 paper robabilistic Margins for Instance Reweighting in Adversarial Training

9 Dec 20, 2022
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

CRNN paper:An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition 1. create your ow

Tsukinousag1 3 Apr 02, 2022
The implementation of Parameter Differentiation based Multilingual Neural Machine Translation

The implementation of Parameter Differentiation based Multilingual Neural Machine Translation .

Qian Wang 21 Dec 17, 2022
Understand Text Summarization and create your own summarizer in python

Automatic summarization is the process of shortening a text document with software, in order to create a summary with the major points of the original document. Technologies that can make a coherent

Sreekanth M 1 Oct 18, 2022
Command Line Text-To-Speech using Google TTS

cli-tts Thanks to gTTS by @pndurette! This is an interactive command line text-to-speech tool using Google TTS. Just type text and the voice will be p

ReekyStive 3 Nov 11, 2022
Weaviate demo with the text2vec-openai module

Weaviate demo with the text2vec-openai module This repository contains an example of how to use the Weaviate text2vec-openai module. When using this d

SeMI Technologies 11 Nov 11, 2022
Pytorch implementation of winner from VQA Chllange Workshop in CVPR'17

2017 VQA Challenge Winner (CVPR'17 Workshop) pytorch implementation of Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challeng

Mark Dong 166 Dec 11, 2022
Installation, test and evaluation of Scribosermo speech-to-text engine

Scribosermo STT Setup Scribosermo is a LGPL licensed, open-source speech recognition engine to "Train fast Speech-to-Text networks in different langua

Florian Quirin 3 Jun 20, 2022
This is the 25 + 1 year anniversary version of the 1995 Rachford-Rice contest

Rachford-Rice Contest This is the 25 + 1 year anniversary version of the 1995 Rachford-Rice contest. Can you solve the Rachford-Rice problem for all t

13 Sep 20, 2022
CCKS-Title-based-large-scale-commodity-entity-retrieval-top1

- 基于标题的大规模商品实体检索top1 一、任务介绍 CCKS 2020:基于标题的大规模商品实体检索,任务为对于给定的一个商品标题,参赛系统需要匹配到该标题在给定商品库中的对应商品实体。 输入:输入文件包括若干行商品标题。 输出:输出文本每一行包括此标题对应的商品实体,即给定知识库中商品 ID,

43 Nov 11, 2022
Trains an OpenNMT PyTorch model and SentencePiece tokenizer.

Trains an OpenNMT PyTorch model and SentencePiece tokenizer. Designed for use with Argos Translate and LibreTranslate.

Argos Open Tech 61 Dec 13, 2022
Training code of Spatial Time Memory Network. Semi-supervised video object segmentation.

Training-code-of-STM This repository fully reproduces Space-Time Memory Networks Performance on Davis17 val set&Weights backbone training stage traini

haochen wang 128 Dec 11, 2022
Finally, some decent sample sentences

tts-dataset-prompts This repository aims to be a decent set of sentences for people looking to clone their own voices (e.g. using Tacotron 2). Each se

hecko 19 Dec 13, 2022
ChatBotProyect - This is an unfinished project about a simple chatbot.

chatBotProyect This is an unfinished project about a simple chatbot. (union_todo.ipynb) Reminders for the project: Find why one of the vectorizers fai

Tomás 0 Jul 24, 2022