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Zadania realizowane w ramach zajęć Inżynieria Uczenia Maszynowego
55 Commits 2 Branches 0 Tags 76 KiB
Jupyter Notebook 77.8%
Python 17.6%
Shell 4.1%
Dockerfile 0.5%
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Marcin Kostrzewski 6a01cf5307
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Mount /tmp/mlruns while training
2022-05-09 14:42:38 +02:00
.gitignore Add Sacred FileStorageObserver 2022-05-06 21:39:18 +02:00
Dockerfile Dockerfile runs training 2022-04-24 22:27:58 +02:00
download_dataset.sh Save artifacts 2022-03-27 23:29:37 +02:00
eval_model.py Move common methods and functions to model.py 2022-05-06 21:51:49 +02:00
Jenkinsfile-build Revert "Do not use string interpolation" 2022-05-07 10:34:33 +02:00
Jenkinsfile-eval Revert "Add model build selector" 2022-05-06 18:45:04 +02:00
Jenkinsfile-stats Cleanup Jenkinsfiles 2022-05-06 20:16:15 +02:00
Jenkinsfile-train Mount /tmp/mlruns while training 2022-05-09 14:42:38 +02:00
model.py Move common methods and functions to model.py 2022-05-06 21:51:49 +02:00
power_plant_data_stats.ipynb Added first solution 2022-03-20 18:07:34 +01:00
power_plant_data_stats.py Dockerization 2022-04-01 22:25:05 +02:00
README.md Added first solution 2022-03-20 18:07:34 +01:00
requirements.txt Initial MLFlow setup 2022-05-09 13:57:15 +02:00
stats.sh Added statistics script 2022-03-27 23:46:51 +02:00
train_model.py Initial MLFlow setup 2022-05-09 13:57:15 +02:00

README.md

ium_444409

Zadania realizowane w ramach zajęć Inżynieria Uczenia Maszynowego.

Zbiór

Solar Power Generation Data https://www.kaggle.com/datasets/anikannal/solar-power-generation-data?select=Plant_1_Generation_Data.csv

Wymagania

  • python3
  • pip
  • API token z kaggle.com

Uruchamianie

  • Instalujemy potrzebne pakiety:
$ pip install -r requirements.txt
  • Pobieramy zbiór danych z Kaggle. Skorzystamy ze skryptu w repo, który pobierze i podzieli dane na podzbiory:
$ ./download_dataset.sh