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s444417 2022-05-15 17:46:05 +02:00
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@ -27,10 +27,10 @@ IUM_8 opis sposobu rozwiązania zadań i podpunktów
---
Zadanie 1
1. lab8/trainScript.py log_param: epoch i learning_rate i log_metric final_loss
2. lab8/MLproject
2. lab8/MLproject docker_env
Zadanie 2
1. na końcu pliku lab8/trainScript.py, zawiera input_example, MLproject docker_env
2.
3. zarejestronwany model np. http://tzietkiewicz.vm.wmi.amu.edu.pl/#/experiments/17/runs/811420769d2642b8be694693c75b3587/artifactPath/linear-model, model rejestruje w pliku lab8/trainScript.py
4. [projekt](https://tzietkiewicz.vm.wmi.amu.edu.pl:8080/job/s444417-predict-s449288-from-registry/) realizuje predykcje skryptem lab8/predictMlflow.py i printuje ją w consoli builda,
1. plik lab8/trainScript.py, używa MLflow, zawiera input_example,
2. model z artifactów pobierany w lab8/Jenkinsfile.artifact, predykcja zrobiona skryptem lab8/predictArtifact.py, output predykcji konsola w [projekcie](https://tzietkiewicz.vm.wmi.amu.edu.pl:8080/job/s444417-predict-s449288/)
3. zarejestronwany model np. http://tzietkiewicz.vm.wmi.amu.edu.pl/#/experiments/17/runs/811420769d2642b8be694693c75b3587/artifactPath/linear-model, model jest rejestrowany w pliku lab8/trainScript.py
4. [projekt](https://tzietkiewicz.vm.wmi.amu.edu.pl:8080/job/s444417-predict-s449288-from-registry/) predykcja realizowana skryptem lab8/predictMlflow.py i printowana w consoli builda,