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