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Developer

geomodular edited this page May 6, 2021 · 2 revisions

Wave ML Developer Guide

This guide is intended for a Wave ML developer i.e. to someone who wants to develop Wave ML.

Basic Setup

Clone the GitHub repository and run make setup:

> git clone https://github.com/h2oai/wave-ml.git
> cd wave-ml/
> make setup 

Source the resulting venv:

> source ./venv/bin/activate

A good start is to run Wave ML locally utilizing H2O-3. Open a different terminal window and run your Wave server inside it. You can download it from here. See the first step here if you are not sure.

Install the examples requirements and run the quickstart:

> pip install -r examples/requirements.txt
> wave run examples/quickstart.py

Development Cloud Setup

Wave ML is developed on a private Cloud instance with all the technologies Wave ML supports. In order to use it, some steps are necessary to follow.

  1. Do the basic setup above.
  2. Set up your OpenID account. Ask on the slack channel.
  3. Create a DAI instance in Steam here (login with OpenID). We will be able to use DAI multinode cluster in the future making this step obsolete.
  4. Create a script e.g. setup-cloud.sh with the following content:
export H2O_WAVE_OIDC_CLIENT_ID=
export H2O_WAVE_OIDC_CLIENT_SECRET=
export H2O_WAVE_OIDC_PROVIDER_URL=http://keycloak.44.238.254.140.nip.io/auth/realms/wave
export H2O_WAVE_OIDC_REDIRECT_URL=http://localhost:10101/_auth/callback
export H2O_WAVE_OIDC_END_SESSION_URL=http://keycloak.44.238.254.140.nip.io/auth/realms/wave/protocol/openid-connect/logout
export H2O_WAVE_ML_STEAM_ADDRESS=https://steam.44.238.254.140.nip.io/
export H2O_WAVE_ML_STEAM_INSTANCE_NAME=
export H2O_WAVE_ML_MLOPS_GATEWAY=http://api.44.238.254.140.nip.io/
export H2O_WAVE_ML_STEAM_VERIFY_SSL=False
  1. Provide the missing credentials, and your DAI instance name. Ask on the slack channel.
  2. Source the script both for Wave and your app terminal window and run the Wave.
  3. Check if OpenID is working just by looking at http://localhost:10101/ - if it's asking to log in.

This setup ensures a quick iteration during your development process. The app is running locally but Wave ML behaves like on the Cloud.

Project Management

Guides

Documentation

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