# import ray
# from ray import tune
#
#
# def objective(step, alpha, beta):
#     return (0.1 + alpha * step / 100)**(-1) + beta * 0.1
#
#
# def training_function(config):
#     # Hyperparameters
#     alpha, beta = config["alpha"], config["beta"]
#     for step in range(10):
#         # Iterative training function - can be any arbitrary training procedure.
#         intermediate_score = objective(step, alpha, beta)
#         # Feed the score back back to Tune.
#         tune.report(mean_loss=intermediate_score)
#
#
# analysis = tune.run(
#     training_function,
#     config={
#         "alpha": tune.grid_search([0.001, 0.01, 0.1]),
#         "beta": tune.choice([1, 2, 3])
#     })
#
# print("Best config: ", analysis.get_best_config(
#     metric="mean_loss", mode="min"))
#
# # Get a dataframe for analyzing trial results.
# df = analysis.results_df



import platform
import os

print(platform.node())
print(os.getpid())