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michalzareba 2021-05-23 17:12:13 +02:00
parent 301151eaa8
commit 6549b4dc98
2 changed files with 14 additions and 0 deletions
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2
Jenkinsfile_training
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@ -17,6 +17,8 @@ pipeline {
steps { steps {
copyArtifacts fingerprintArtifacts: true, projectName: 's430705-create-dataset', selector: buildParameter('BUILD_SELECTOR') copyArtifacts fingerprintArtifacts: true, projectName: 's430705-create-dataset', selector: buildParameter('BUILD_SELECTOR')
sh "rm -rf movies_imdb" sh "rm -rf movies_imdb"
sh "python3 lab08_mfl.py"
sh "export MLFLOW_TRACKING_URI=http://172.17.0.1:5000"
sh "python3 lab08_mfl.py" sh "python3 lab08_mfl.py"
sh "python3 lab06_training.py ${epochs}" sh "python3 lab06_training.py ${epochs}"
} }

12
lab08_mfl.py
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@ -7,6 +7,10 @@ from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense, Dropout from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential from tensorflow.keras.models import Sequential
from urllib.parse import urlparse
mlflow.set_experiment("s430705")
mlflow.set_tracking_uri("http://172.17.0.1:5000")
def prepare_model(train_size_param, test_size_param, epochs, batch_size): def prepare_model(train_size_param, test_size_param, epochs, batch_size):
@ -79,3 +83,11 @@ with mlflow.start_run():
signature = mlflow.models.signature.infer_signature(X_train.values, model.predict(X_train.values)) signature = mlflow.models.signature.infer_signature(X_train.values, model.predict(X_train.values))
mlflow.keras.save_model(model, "movies_imdb", input_example=input_example, signature=signature) mlflow.keras.save_model(model, "movies_imdb", input_example=input_example, signature=signature)
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
if tracking_url_type_store != "file":
mlflow.keras.log_model(model, "movies_imdb", registered_model_name="s430705", signature=signature,
input_example=input_example)
else:
mlflow.keras.log_model(model, "model_movies", signature=signature, input_example=input_example)
mlflow.keras.save_model(model, "movies_mdb", signature=signature, input_example=input_example)