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a code notebook for the machine learning platform h2o-3

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torrent 🌊

torrent is a code notebook for the machine learning platform h2o-3. torrent combines code execution, text, mathematics, plots and rich media to build machine learning workflows.

torrent is written in es2015 Javascript. torrent uses the Pug templating engine (formerly known as Jade) to define HTML views and lay out pages. torrent uses the KnockoutJS Model-View-View-Model library for declarative data binding.

torrent contains a veritable heap of little embedded DSLs for reactive dataflow programming, markup generation, lazy evaluation and multicast signals/slots. torrent sandboxes and evaluates user-Javascript in the browser via static analysis and tree-rewriting. torrent's storytelling environment for exploratory data analysis & machine learning, features async, re-scriptable record/replay capabilities.

torrent is a fork of the h2o-flow project

install

user guide

there is a nice user guide for h2o-flow housed over in the h2o-3 repo that describes many features also found in torrent

dev setup

clone h2o-3 and torrent in the same parent directory.

it is best to run h2o-3 in the Java IDE IntelliJ IDEA. This enables you the developer to see your changes to torrent in the browser immediately after you run the npm run build command, without waiting the 90+ secods to build a new h2o.jar binary and run java -jar h2o.jar to restart h2o-3.

inside of IntelliJ IDEA:

  1. clean up all previous built files: cd h2o-3 && ./gradlew clean
  2. open the h2o-3 directory in IDEA, then build and launch H2OApp.java
  3. Run
cd torrent
npm install
npm run build

to install torrent's dependencies and create the torrent bundle. you can now access and debug torrent at http://localhost:54321/

  1. after each change you make to the torrent source code, run the command

npm run build

to copy your changes to the instance of h2o-3 running locally inside of IDEA

further instructions for setting up the h2o-3 project in IntelliJ IDEA are here

testing a new torrent feature with Sparkling Water

torrent can also be used with Sparkling Water
follow this guide develop and test new Sparkling Water features in torrent.
adapted from the comments on this PR h2oai/h2o-flow#13

copy built js files from one place to another

in the h2o-3 directory run:
cp h2o-web/src/main/resources/www/flow/js/* h2o-web/lib/h2o-flow/build/js/

build h2o-3

in the h2o-3 directory run:
./gradlew publishToMavenLocal -x test

build sparkling water

in sparkling-water directory run:
./gradlew clean build -x test -x integTest

open the Sparkling Water Shell

in sparkling-water directory run:
bin/sparkling-shell

in the sparkling water shell
at the scala> prompt run:
import org.apache.spark.h2o._
H2OContext.getOrCreate(sc)

now open torrent at the IP address specified
in the sparkling water shell

now test your changes in torrent

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a code notebook for the machine learning platform h2o-3

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