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README.md

Goptuna

Software License GoDoc Go Report Card tests

Distributed hyperparameter optimization framework, inspired by Optuna [1]. This library is particularly designed for machine learning, but everything will be able to optimize if you can define the objective function (e.g. Optimizing the number of goroutines of your server and the memory buffer size of the caching systems).

Supported algorithms:

Goptuna supports various state-of-the-art Bayesian optimization, Evolution strategy and Multi-armed bandit algorithms. These algorithms are implemented in pure Go and continuously benchmarked on GitHub Actions.

  • Random search
  • TPE: Tree-structured Parzen Estimators [2]
  • CMA-ES: Covariance Matrix Adaptation Evolution Strategy [3]
  • IPOP-CMA-ES: CMA-ES with increasing population size [4]
  • BIPOP-CMA-ES: BI-population CMA-ES [5]
  • Median Stopping Rule [6]
  • ASHA: Asynchronous Successive Halving Algorithm (Optuna flavored version) [1,7,8]

Built-in dashboard:

Manage optimization results Interactive live-updating graphs
state-of-the-art-algorithms visualization

Projects using Goptuna:

Installation

You can integrate Goptuna in wide variety of Go projects because of its portability of pure Go.

$ go get -u github.com/c-bata/goptuna

Usage

Goptuna supports Define-by-Run style API like Optuna. You can dynamically construct the search spaces.

Basic usage

package main

import (
    "log"
    "math"

    "github.com/c-bata/goptuna"
    "github.com/c-bata/goptuna/tpe"
)

// ① Define an objective function which returns a value you want to minimize.
func objective(trial goptuna.Trial) (float64, error) {
    // ② Define the search space via Suggest APIs.
    x1, _ := trial.SuggestFloat("x1", -10, 10)
    x2, _ := trial.SuggestFloat("x2", -10, 10)
    return math.Pow(x1-2, 2) + math.Pow(x2+5, 2), nil
}

func main() {
    // ③ Create a study which manages each experiment.
    study, err := goptuna.CreateStudy(
        "goptuna-example",
        goptuna.StudyOptionSampler(tpe.NewSampler()))
    if err != nil { ... }

    // ④ Evaluate your objective function.
    err = study.Optimize(objective, 100)
    if err != nil { ... }

    // ⑤ Print the best evaluation parameters.
    v, _ := study.GetBestValue()
    p, _ := study.GetBestParams()
    log.Printf("Best value=%f (x1=%f, x2=%f)",
        v, p["x1"].(float64), p["x2"].(float64))
}

Link: Go Playground

Furthermore, I recommend you to use RDB storage backend for following purposes.

  • Continue from where we stopped in the previous optimizations.
  • Scale studies to tens of workers that connecting to the same RDB storage.
  • Check optimization results via built-in dashboard.

Advanced usage

Distributed optimization using MySQL

There is no complicated setup to use RDB storage backend. First, setup MySQL server like following to share the optimization result.

$ docker pull mysql:8.0
$ docker run \
  -d \
  --rm \
  -p 3306:3306 \
  --mount type=volume,src=mysql,dst=/etc/mysql/conf.d \
  -e MYSQL_USER=goptuna \
  -e MYSQL_DATABASE=goptuna \
  -e MYSQL_PASSWORD=password \
  -e MYSQL_ALLOW_EMPTY_PASSWORD=yes \
  --name goptuna-mysql \
  mysql:8.0

Then, create a study object using goptuna CLI.

$ goptuna create-study --storage mysql://goptuna:password@localhost:3306/yourdb --study yourstudy
yourstudy
$ mysql --host 127.0.0.1 --port 3306 --user goptuna -ppassword -e "SELECT * FROM studies;"
+----------+------------+-----------+
| study_id | study_name | direction |
+----------+------------+-----------+
|        1 | yourstudy  | MINIMIZE  |
+----------+------------+-----------+
1 row in set (0.00 sec)

Finally, run the Goptuna workers which contains following code. You can execute distributed optimization by just executing this script from multiple server instances.

package main

import ...

func main() {
    db, _ := gorm.Open("mysql", "goptuna:password@tcp(localhost:3306)/yourdb?parseTime=true")
    storage := rdb.NewStorage(db)
    defer db.Close()

    study, _ := goptuna.LoadStudy(
        "yourstudy",
        goptuna.StudyOptionStorage(storage),
        ...,
    )
    _ = study.Optimize(objective, 50)
    ...
}

Full source code is available here.

Built-in Realtime Web Dashboard

You can check optimization results by built-in web dashboard.

SQLite3:

$ goptuna dashboard --storage sqlite:///example.db

MySQL:

$ goptuna dashboard --storage mysql://goptuna:password@127.0.0.1:3306/yourdb

goptuna dashboard

Shell script to reproduce this (SQLite3 version is here).

Links

References:

Presentations:

Blog posts:

Status:

License

This software is licensed under the MIT license, see LICENSE for more information.

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