This commit is contained in:
Jakub Zaręba 2023-05-11 00:32:25 +02:00
parent bc646e2982
commit 7dc5e630f6
4 changed files with 130 additions and 4 deletions

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@ -2,7 +2,7 @@ pipeline {
agent {
docker {
image 'python:3.11'
args '-v /root/.cache:/root/.cache -u root'
args '-v /root/.cache:/root/.cache -u root -v /tmp/mlruns:/tmp/mlruns -v /mlruns:/mlruns'
}
}
parameters {
@ -11,7 +11,7 @@ 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') {
@ -24,7 +24,6 @@ pipeline {
stage('Trenuj model') {
steps {
script {
// sh "python3 train.py --epochs $EPOCHS"
sh "python3 train.py"
}
}

38
old_JenkinsfileDL Normal file
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@ -0,0 +1,38 @@
pipeline {
agent {
docker {
image 'python:3.11'
args '-v /root/.cache:/root/.cache -u root'
}
}
parameters {
string(name: 'EPOCHS', defaultValue: '10', description: 'Liczba Epok')
}
stages {
stage('Preparation') {
steps {
sh 'pip install pandas tensorflow scikit-learn imbalanced-learn sacred pymongo'
}
}
stage('Pobierz dane') {
steps {
script {
copyArtifacts(projectName: 's487187-create-dataset', fingerprintArtifacts: true)
}
}
}
stage('Trenuj model') {
steps {
script {
// sh "python3 train.py --epochs $EPOCHS"
sh "python3 train.py"
}
}
}
stage('Zarchiwizuj model') {
steps {
archiveArtifacts artifacts: 'model.h5', fingerprint: true
}
}
}
}

74
old_train.py Normal file
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@ -0,0 +1,74 @@
from sacred import Experiment
from sacred.observers import MongoObserver, FileStorageObserver
import os
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'
model_file = 'model.h5'
epochs = 10
batch_size = 32
test_size = 0.2
random_state = 42
@ex.capture
def train_model(data_file, model_file, epochs, batch_size, test_size, random_state):
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
from imblearn.over_sampling import SMOTE
smote = SMOTE(random_state=random_state)
data = pd.read_csv(data_file, sep=';')
print('Total rows:', len(data))
print('Rows with medal:', len(data.dropna(subset=['Medal'])))
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)
X = data.filter(regex='Sex|Age')
y = data.filter(regex='Medal')
y = pd.get_dummies(y)
X = X.fillna(0)
y = y.fillna(0)
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)
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.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.save(model_file)
return accuracy
@ex.main
def run_experiment():
accuracy = train_model()
ex.log_scalar('accuracy', accuracy)
ex.add_artifact('model.h5')
ex.run()

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@ -1,3 +1,8 @@
import mlflow
import mlflow.keras
from mlflow.models.signature import infer_signature
from mlflow.models import Model
import pandas as pd
from sacred import Experiment
from sacred.observers import MongoObserver, FileStorageObserver
import os
@ -7,6 +12,9 @@ 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'))
mlflow.set_tracking_uri("http://172.17.0.1:5000")
mlflow.set_experiment("s487187")
@ex.config
def my_config():
@ -63,6 +71,13 @@ def train_model(data_file, model_file, epochs, batch_size, test_size, random_sta
model.save(model_file)
mlflow.keras.log_model(model, "model")
mlflow.log_artifact("model.h5")
signature = infer_signature(X_train, model.predict(X_train))
input_example = pd.DataFrame(X_train[:1])
mlflow.keras.save_model(model, "model", signature=signature, input_example=input_example)
return accuracy
@ex.main
@ -71,4 +86,4 @@ def run_experiment():
ex.log_scalar('accuracy', accuracy)
ex.add_artifact('model.h5')
ex.run()
ex.run()