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Jakub Zaręba 2023-05-10 22:49:14 +02:00
parent ba6e518100
commit f562d1b0a4
3 changed files with 41 additions and 36 deletions
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4
Dockerfile
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@ -2,7 +2,7 @@ FROM ubuntu
RUN apt-get update && apt-get install -y python3 python3-pip unzip
RUN python3 -m pip install pandas numpy tensorflow imbalanced-learn sklearn sacred pymongo
RUN python3 -m pip install pandas numpy tensorflow imbalanced-learn sklearn sacred pymongo mlflow
RUN apt-get install -y git
COPY train.py /app/train.py
@ -12,6 +12,6 @@ COPY data.csv /app/data.csv
WORKDIR /app
RUN export SACRED_IGNORE_GIT=TRUE
RUN python3 train.py
RUN mlflow run . -P epochs=10
CMD ["python3", "predictions.py"]

10
JenkinsfileDL
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@ -14,7 +14,7 @@ pipeline {
stages {
stage('Preparation') {
steps {
sh 'pip install pandas tensorflow scikit-learn imbalanced-learn sacred pymongo'
sh 'pip install pandas tensorflow scikit-learn imbalanced-learn sacred pymongo mlflow'
}
}
stage('Pobierz dane') {
@ -27,13 +27,17 @@ pipeline {
stage('Trenuj model') {
steps {
script {
sh "python3 train.py" //--epochs $EPOCHS
sh 'mlflow run . -P epochs=$EPOCHS'
}
}
}
stage('Zarchiwizuj model') {
steps {
archiveArtifacts artifacts: 'model.h5', fingerprint: true
sh '''
mkdir -p model
cp -r mlruns/* model/
'''
archiveArtifacts artifacts: 'model/**', fingerprint: true
}
}
}

57
train.py
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@ -1,13 +1,14 @@
from sacred import Experiment
from sacred.observers import MongoObserver, FileStorageObserver
import os
import mlflow
import mlflow.keras
os.environ["SACRED_NO_GIT"] = "1"
ex = Experiment('s487187-training', interactive=True, save_git_info=False)
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
@ex.config
def my_config():
data_file = 'data.csv'
@ -25,45 +26,45 @@ def train_model(data_file, model_file, epochs, batch_size, test_size, random_sta
import tensorflow as tf
from imblearn.over_sampling import SMOTE
smote = SMOTE(random_state=random_state)
data = pd.read_csv(data_file, sep=';')
with mlflow.start_run():
print('Total rows:', len(data))
print('Rows with medal:', len(data.dropna(subset=['Medal'])))
smote = SMOTE(random_state=random_state)
data = pd.read_csv(data_file, sep=';')
data = pd.get_dummies(data, columns=['Sex', 'Medal'])
data = data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])
data = pd.get_dummies(data, columns=['Sex', 'Medal'])
data = data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])
scaler = MinMaxScaler()
data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
scaler = MinMaxScaler()
data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
X = data.filter(regex='Sex|Age')
y = data.filter(regex='Medal')
y = pd.get_dummies(y)
X = data.filter(regex='Sex|Age')
y = data.filter(regex='Medal')
y = pd.get_dummies(y)
X = X.fillna(0)
y = y.fillna(0)
X = X.fillna(0)
y = y.fillna(0)
y = y.values
y = y.values
X_resampled, y_resampled = smote.fit_resample(X, y)
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=test_size, random_state=random_state)
X_resampled, y_resampled = smote.fit_resample(X, y)
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=test_size, random_state=random_state)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(64, input_dim=X_train.shape[1], activation='relu'))
model.add(tf.keras.layers.Dense(32, activation='relu'))
model.add(tf.keras.layers.Dense(y.shape[1], activation='softmax'))
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(64, input_dim=X_train.shape[1], activation='relu'))
model.add(tf.keras.layers.Dense(32, activation='relu'))
model.add(tf.keras.layers.Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)
loss, accuracy = model.evaluate(X_test, y_test)
print('Test accuracy:', accuracy)
print('Test loss:', loss)
model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)
loss, accuracy = model.evaluate(X_test, y_test)
model.save(model_file)
mlflow.log_metric("loss", loss)
mlflow.log_metric("accuracy", accuracy)
return accuracy
mlflow.keras.save_model(model, model_file)
return accuracy
@ex.main
def run_experiment():