Advisor

Introduction

Advisor is the hyper parameters tuning system for black box optimization.

It is the open-source implementation of Google Vizier with the features.

Get suggestions from API, WEB or CLI
Support abstractions of Study and Trial
Included search and early stop algorithms
Recommend parameters with trained model
Same programming interfaces as Google Vizier

Algorithms

Usage

Advisor Server

Run the advisor server.

pip install -r ./requirements.txt

./manage.py runserver 0.0.0.0:8000

Open http://127.0.0.1:8000 in the browser.

Docker Server

You can run the server with docker as well.

docker run -d -p 8000:8000 tobegit3hub/advisor

Advisor Client

Install with pip.

pip install advisor_clients

Run with Python SDK.

client = AdvisorClient()

# Create the study
study_configuration = {
        "goal": "MAXIMIZE",
        "maxTrials": 5,
        "maxParallelTrials": 1,
        "params": [
                {
                        "parameterName": "hidden1",
                        "type": "INTEGER",
                        "minValue": 40,
                        "maxValue": 400,
                        "scallingType": "LINEAR"
                }
        ]
}
study = client.create_study("Study", study_configuration)

# Get suggested trials
trials = client.get_suggestions(study, 3)

# Complete the trial
client.complete_trial(trial, trial_metrics)

Run with command-line tool.

advisor study list

advisor trial list --study_id 1

Please checkout examples for more usage.

Concepts

Study configuration describe the search space of parameters. It supports four types and here is the example.

{
  "goal": "MAXIMIZE",
  "randomInitTrials": 1,
  "maxTrials": 5,
  "maxParallelTrials": 1,
  "params": [
    {
      "parameterName": "hidden1",
      "type": "INTEGER",
      "minValue": 1,
      "maxValue": 10,
      "scallingType": "LINEAR"
    },
    {
      "parameterName": "learning_rate",
      "type": "DOUBLE",
      "minValue": 0.01,
      "maxValue": 0.5,
      "scallingType": "LINEAR"
    },
    {
      "parameterName": "hidden2",
      "type": "DISCRETE",
      "minValue": 0,
      "maxValue": 0,
      "feasiblePoints": "1.5, -1.5, 2.5, 4.5",
      "scallingType": "LINEAR"
    },
    {
      "parameterName": "optimizer",
      "type": "CATEGORICAL",
      "minValue": 0,
      "maxValue": 0,
      "feasiblePoints": "sgd, adagrad, adam, ftrl",
      "scallingType": "LINEAR"
    },
    {
      "parameterName": "batch_normalization",
      "type": "CATEGORICAL",
      "minValue": 0,
      "maxValue": 0,
      "feasiblePoints": "true, false",
      "scallingType": "LINEAR"
    }
  ]
}

Visualization

You can visualize one-dimentation Bayesian Optimization with the notebooks in visualization.

Screenshots

List all the studies and create/delete the studies easily.

List the detail of study and all the related trials.

List all the trials and create/delete the trials easily.

List the detail of trial and all the related metrics.

XiaolinZHONG/advisor