xadrianzetx

import time

import optuna
import optuna_distributed


def objective(trial):
    x = trial.suggest_float("x", -100, 100)
    y = trial.suggest_categorical("y", [-1, 0, 1])
    time.sleep(1.0)

xadrianzetx

sequential_start = time.time()
study = optuna.create_study()
study.optimize(objective, n_trials=20)
sequential_duration = time.time() - sequential_start
sequential_best_value = study.best_value

distributed_start = time.time()
distributed_study = optuna_distributed.from_study(study)
distributed_study.optimize(objective, n_trials=20)
distributed_duration = time.time() - distributed_start

xadrianzetx

def objective(trial):
    x = trial.suggest_float("x", -100, 100)
    y = trial.suggest_categorical("y", [-1, 0, 1])
    time.sleep(1.0)
    return x**2 + y

xadrianzetx

import time

import optuna
import sklearn.datasets
import sklearn.ensemble
import sklearn.model_selection
import sklearn.svm
from optuna.integration.wandb import WeightsAndBiasesCallback

import wandb

xadrianzetx

import cv2
import requests
import platform
import numpy as np
import tflite_runtime.interpreter as tflite


IMG = {
    'fname': 'test_img.jpg',
    'origin': '1ew-FdIdrhks9yw8fDXc14uuxWUfmdqaD'