Getting Started
Installation
Prerequisite
Optuna Dashboard supports Python 3.7 or newer.
Installing from PyPi
You can install optuna-dashboard via PyPI or Anaconda Cloud.
$ pip install optuna-dashboard
Also, you can install following optional dependencies to make optuna-dashboard faster.
$ pip install optuna-fast-fanova gunicorn
Installing from the source code
Since it requires to build TypeScript files, pip install git+https://.../optuna-dashboard.git
does not actually work.
Please clone the git repository and execute following commands to build sdist package:
$ git clone git@github.com:optuna/optuna.git
$ cd optuna
# Node.js v16 is required to compile TypeScript files.
$ npm install
$ npm run build:prd
$ python setup.py sdist
Then you can install it like:
$ pip install dist/optuna-dashboard-x.y.z.tar.gz
See CONTRIBUTING.md for more details.
Command-line Interface
The most common usage of Optuna Dashboard is using the command-line interface.
Assuming that Optuna’s optimization history is persisted using RDBStorage
,
you can use the command line interface like optuna-dashboard <STORAGE_URL>
.
import optuna
def objective(trial):
x = trial.suggest_float("x", -100, 100)
y = trial.suggest_categorical("y", [-1, 0, 1])
return x**2 + y
study = optuna.create_study(
storage="sqlite:///db.sqlite3", # Specify the storage URL here.
study_name="quadratic-simple"
)
study.optimize(objective, n_trials=100)
print(f"Best value: {study.best_value} (params: {study.best_params})")
$ optuna-dashboard sqlite:///db.sqlite3
Listening on http://localhost:8080/
Hit Ctrl-C to quit.
If you are using JournalStorage classes introduced in Optuna v3.1, you can use them like below:
# JournalFileStorage
$ optuna-dashboard ./path/to/journal.log
# JournalRedisStorage
$ optuna-dashboard redis://localhost:6379
Using an official Docker image
You can also use an official Docker image instead of setting up your Python environment. The Docker image only supports SQLite3, MySQL(PyMySQL), and PostgreSQL(Psycopg2).
SQLite3
$ docker run -it --rm -p 8080:8080 -v `pwd`:/app -w /app ghcr.io/optuna/optuna-dashboard sqlite:///db.sqlite3
MySQL (PyMySQL)
$ docker run -it --rm -p 8080:8080 ghcr.io/optuna/optuna-dashboard mysql+pymysql://username:password@hostname:3306/dbname
PostgreSQL (Psycopg2)
$ docker run -it --rm -p 8080:8080 ghcr.io/optuna/optuna-dashboard postgresql+psycopg2://username:password@hostname:5432/dbname
Python Interface
Python interfaces are also provided for users who want to use other storage implementations (e.g. InMemoryStorage
).
You can use run_server()
function like below:
import optuna
from optuna_dashboard import run_server
def objective(trial):
x = trial.suggest_float("x", -100, 100)
y = trial.suggest_categorical("y", [-1, 0, 1])
return x**2 + y
storage = optuna.storages.InMemoryStorage()
study = optuna.create_study(storage=storage)
study.optimize(objective, n_trials=100)
run_server(storage)
Using Gunicorn or uWSGI server
Optuna Dashboard uses wsgiref module, which is in the Python’s standard libraries, by default.
However, as described here, wsgiref
is implemented for testing or debugging purpose.
You can switch to other WSGI server implementations by using wsgi()
function.
:caption: wsgi.py
from optuna.storages import RDBStorage
from optuna_dashboard import wsgi
storage = RDBStorage("sqlite:///db.sqlite3")
application = wsgi(storage)
Then please execute following commands to start.
$ pip install gunicorn
$ gunicorn --workers 4 wsgi:application
or
$ pip install uwsgi
$ uwsgi --http :8080 --workeers 4 --wsgi-file wsgi.py
Google Colaboratory
When you want to check the optimization history on Google Colaboratory,
you can use google.colab.output()
function as follows:
import optuna
import threading
from google.colab import output
from optuna_dashboard import run_server
def objective(trial):
x = trial.suggest_float("x", -100, 100)
return (x - 2) ** 2
# Run optimization
storage = optuna.storages.InMemoryStorage()
study = optuna.create_study(storage=storage)
study.optimize(objective, n_trials=100)
# Start Optuna Dashboard
port = 8081
thread = threading.Thread(target=run_server, args=(storage,), kwargs={"port": port})
thread.start()
output.serve_kernel_port_as_window(port, path='/dashboard/')
Then please open http://localhost:8081/dashboard to browse.