sacred
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Sheaza 2024-06-09 17:55:06 +02:00
parent 50c8a6095f
commit 7a3e24a150
5 changed files with 99 additions and 1 deletions

5
.env Normal file
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MONGO_INITDB_ROOT_USERNAME=admin
MONGO_INITDB_ROOT_PASSWORD=IUM_2021
ME_CONFIG_BASICAUTH_USERNAME=mongo_express_user
ME_CONFIG_BASICAUTH_PASSWORD=mongo_express_pw
MONGO_DATABASE=sacred

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@ -5,3 +5,5 @@ numpy
matplotlib
mlflow
dvc
sacred
pymongo

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sacredboard/Dockerfile Normal file
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FROM python:3.6-jessie
RUN pip install https://github.com/chovanecm/sacredboard/archive/develop.zip
ENTRYPOINT sacredboard -mu mongodb://$MONGO_INITDB_ROOT_USERNAME:$MONGO_INITDB_ROOT_PASSWORD@mongo:27017/?authMechanism=SCRAM-SHA-1 $MONGO_DATABASE

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train_sacred.py Normal file
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import pandas as pd
from tensorflow import keras
from tensorflow.keras import layers
import argparse
from sacred import Experiment
from sacred.observers import FileStorageObserver, MongoObserver
ex = Experiment("464980", interactive=True, save_git_info=False)
ex.observers.append(FileStorageObserver('experiments'))
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017',
db_name='sacred'))
@ex.capture
def capture_params(epochs):
print(f"epochs: {epochs}")
class RegressionModel:
def __init__(self, optimizer="adam", loss="mean_squared_error"):
self.model = keras.Sequential([
layers.Input(shape=(5,)), # Input layer
layers.Dense(32, activation='relu'), # Hidden layer with 32 neurons and ReLU activation
layers.Dense(1) # Output layer with a single neuron (for regression)
])
self.optimizer = optimizer
self.loss = loss
self.X_train = None
self.X_test = None
self.y_train = None
self.y_test = None
def load_data(self, train_path, test_path):
data_train = pd.read_csv(train_path)
data_test = pd.read_csv(test_path)
self.X_train = data_train.drop("Performance Index", axis=1)
self.y_train = data_train["Performance Index"]
self.X_test = data_test.drop("Performance Index", axis=1)
self.y_test = data_test["Performance Index"]
def train(self, epochs=30):
self.model.compile(optimizer=self.optimizer, loss=self.loss)
self.model.fit(self.X_train, self.y_train, epochs=epochs, batch_size=32, validation_data=(self.X_test, self.y_test))
capture_params(epochs)
def predict(self, data):
prediction = self.model.predict(data)
return prediction
def evaluate(self):
test_loss = self.model.evaluate(self.X_test, self.y_test)
print(f"Test Loss: {test_loss:.4f}")
return test_loss
def save_model(self):
self.model.save("model.keras")
ex.add_artifact("model.keras")
@ex.main
def main(_run):
parser = argparse.ArgumentParser()
parser.add_argument('--epochs')
args = parser.parse_args()
model = RegressionModel()
model.load_data("df_train.csv", "df_test.csv")
model.train(epochs=int(args.epochs))
_run.log_scalar("testing.mean_square_error", model.evaluate())
model.save_model()
ex.run()

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training/Jenkinsfile vendored
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@ -37,6 +37,19 @@ pipeline {
archiveArtifacts artifacts: 'model.keras', onlyIfSuccessful: true
}
}
stage('Experiments') {
agent {
dockerfile {
filename 'Dockerfile'
reuseNode true
}
}
steps {
sh "chmod +x ./train_sacred.py"
sh "python ./train_sacred.py --epochs ${params.EPOCHS}"
archiveArtifacts artifacts: 'experiments', onlyIfSuccessful: true
}
}
stage('Run training'){
steps{
script {