Auxiliary Javascript libraries for Backend.AI front-end developers
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README.md

Backend.AI Media Library

A front-end side Javascript library to ease rendering of JSON-encoded media outputs generated by our kernel runners as well as web terminals.

Backend.AI Kernel Runner used by user-written codes inside Backend.AI kernels generates various multimedia outputs, not only stdout/stderr console outputs. Backend.AI Media library eases rendering of them in the web-browser.

Note that, this library is not a direct client of Backend.AI. For that purpose, please check out another repository. This library only takes the computation results and renders them into HTML.

It means that it's your job to connect with the Backend.AI on the cloud or on-premise servers, and you should have your own scripts to retrieve Backend.AI computation results in your own front-end.

Your front-end should call BackendAI.Media.handle_all(<media-output-array>, <result-identifier>, <result-ctonainer>) where result-identifier should be a unique string for each code block and result-container should be a reference to HTML element such as <div> blocks used for rendering the execution results.

For Developers

Setting up

We use yarn and webpack for bundling Javascript files and CSS resources so that we keep the main script small (less than 100KB) while the main script loads all the necessary stuffs dynamically.

Check out the installation instruction of yarn package manager first.

# Use package.json to install all dependencies locally:
$ yarn install
# To run a local development server serving auto-rebuilt in-memory bundles:
$ yarn run devserver
# To run the production build:
$ yarn run build

Integration with a front-end

You need to specify BackendAI.assetRoot in Javascript to let our scripts know which location to fetch additoinal scripts from. The main.min.js is designed to be small for faster page loads and most functionality (e.g., drawing support) are loaded on demand.

For production:

<script>
window.BackendAI = window.BackendAI || {};
window.BackendAI.assetRoot = '//<backendai-frontend-serving-host>/<hash>';
</script>
<script src="//<backendai-frontend-serving-host>/<hash>/js/main.min.js"></script>

<backendai-frontend-serving-host> would be placed by a template variable from application server settings.

For development:

<script>
window.BackendAI = window.BackendAI || {};
window.BackendAI.assetRoot = 'http://localhost:8002/latest';
</script>
<script src="http://localhost:8002/latest/js/main.min.js"></script>

When receiving Backend.AI execution results:

var response = ...;
var result_id = ...;
var result_container = document.getElementById(...);
// media is a list of (type, data) tuples produced by server-side Pyhon packages
BackendAI.Media.handle_all(response.media, result_id, result_container);

Developing with local neumann frontend instances

We use webpack-dev-server to automatically recompile the sources on the memory whenever they change (aka "watch-mode"). However, you need to manually refresh the page to get the latest bundles as we do not use "hot module refresh" (HMR) due to conflicts with script tags without src attributes (e.g., ZenDesk-injected scripts).

$ yarn run devserver
# Bundled scripts are served at http://127.0.0.1:8002/latest/js/...

Note that the port number in the configuration is fixed for Lablup's internal development configuration. You may change it if you have different frontends.

Deploying for production service

We use the standard aws-cli tool. You should configure your AWS access key and the secret key to make it working.

Before uploading, we first need to compile the resources for production.

$ yarn run update
# This includes "yarn run build" process.

This script will write the compiled resources into assets/<hash> directory, where the hash value depends on the content of all resource files. It also deletes all other assets/<old-hash> directories automatically to avoid duplicate transfers below. To debug the webpack build process, simply run webpack and see what it says.

Then, run the following to upload all assets:

$ aws s3 cp assets s3://backendai-assets/ --recursive

Afterwards, you must update the production configuration (e.g., BACKENDAI_ASSET_ROOT in Django/Flask settings) for your front-end using the latest hash value. (e.g., https://s3.ap-northeast-2.amazonaws.com/backendai-assets/1234567890abcdef1234 )