evaluation

evaluation.Jenkinsfile

@@ -0,0 +1,23 @@
+pipeline {
+    agent { 
+        dockerfile true 
+    }
+
+    stages {
+        stage('Build') {
+            steps {
+                git 'https://git.wmi.amu.edu.pl/s434749/ium_434749.git'
+                copyArtifacts fingerprintArtifacts: true, projectName: 's434749-training', selector: lastSuccessful()
+                sh 'python3 train_model.py eval'
+            }
+
+            post {
+                success {
+                    def results = readFile "${env.WORKSPACE}/results.txt"
+                    emailext body: 'Evaluation of CNN for english phonetic embeddings has finished successfully!\n'+results, subject: 's434749 evaluation finished', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+                    archiveArtifacts 'results.txt'
+                }
+            }
+        }
+    }
+}

train-model.Jenkinsfile

@@ -8,12 +8,13 @@ pipeline {
             steps {
                 git 'https://git.wmi.amu.edu.pl/s434749/ium_434749.git'
                 copyArtifacts fingerprintArtifacts: true, projectName: 's434749-create-dataset', selector: lastSuccessful()
-                sh 'python3 train_model.py'
+                sh 'python3 train_model.py train'
             }
 
             post {
                 success {
-                    archiveArtifacts 'cnn.pth,results.txt'
+                    emailext body: 'Training of CNN for english phonetic embeddings has finished successfully', subject: 's434749 training finished', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+                    archiveArtifacts 'cnn.pth'
                 }
             }
         }

train_model.py

@@ -13,6 +13,7 @@ from torch.utils.data import Dataset, DataLoader
 import re
 import random
 import os
+import sys
 from tqdm import tqdm
 from Levenshtein import distance as levenshtein_distance
 
@@ -174,9 +175,13 @@ cnn = CNN(kernel_size=3, hidden_layers=14, channels=MAX_LEN, embedding_size=MAX_
 if os.path.isfile('cnn.pth'):
     cnn.load_state_dict(torch.load('cnn.pth', map_location=torch.device('cpu')))
 else:
+    if len(sys.argv) > 1 and sys.argv[1] == 'train':
         train_model(cnn)
         torch.save(cnn.state_dict(), 'cnn.pth')
+    else:
+        print("cnn.pth missing!")
+        exit(2)
 
+if len(sys.argv) > 1 and sys.argv[1] == 'eval':
     cnn.eval()
-print("Training finished! Starting evaluation!")
     evaluate_monte_carlo(cnn, 1)