aitech-ium/IUM_07/mongo_observer.py

40 lines
1.6 KiB
Python
Raw Permalink Normal View History

2023-04-19 11:55:13 +02:00
from sacred.observers import MongoObserver
from sacred import Experiment
import random
import time
ex = Experiment("sacred_scopes", interactive=True)
2023-04-19 12:23:05 +02:00
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
#ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password@localhost:27017',
# db_name='sacred')) # Tutaj podajemy dane uwierzytelniające i nazwę bazy skonfigurowane w pliku .env podczas uruchamiania bazy.
2023-04-19 11:55:13 +02:00
# W przypadku instancji na Jenkinsie url będzie wyglądał następująco: mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017
@ex.config
def my_config():
recipient = "Świecie"
greeting = "Witaj"
@ex.capture
def prepare_message(recipient, greeting):
return "{0} {1}!".format(greeting, recipient)
@ex.main
def my_main(recipient, greeting, _run):
print(prepare_message()) ## Nie musimy przekazywać wartości
counter = 0
while counter < 20:
counter+=1
value = counter
ms_to_wait = random.randint(5, 5000)
time.sleep(ms_to_wait/1000)
noise = 1.0 + 0.1 * (random.randint(0, 10) - 5)
# This will add an entry for training.loss metric in every second iteration.
# The resulting sequence of steps for training.loss will be 0, 2, 4, ...
if counter % 2 == 0:
_run.log_scalar("training.loss", value * 1.5 * noise, counter)
# Implicit step counter (0, 1, 2, 3, ...)
# incremented with each call for training.accuracy:
_run.log_scalar("training.accuracy", value * 2 * noise)
ex.run()