DEEPL with docker

Dockerfile

@@ -0,0 +1,15 @@
+FROM ubuntu:latest
+FROM python:3.8
+RUN apt update 
+
+
+COPY ./requirements.txt .
+RUN apt-get update
+RUN pip3 install -r requirements.txt
+RUN apt-get install zip unzip --yes
+
+
+WORKDIR /app
+
+COPY ./deepl.py .
+CMD python3 deepl.py

Jenkinsfile

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+pipeline {
+  	agent {
+		dockerfile {
+			true
+		}
+	}
+    parameters {
+        string (
+            defaultValue: 'mikolajpokrywka',
+            description: 'Kaggle username',
+            name: 'KAGGLE_USERNAME',
+            trim: false
+        )
+        password(
+            defaultValue: '',
+            description: 'Kaggle token',
+            name: 'KAGGLE_KEY'
+        )
+        string (
+            defaultValue: '17000',
+            description: 'cut data',
+            name: 'CUTOFF',
+            trim: false
+        )
+    }
+    stages {
+        stage('checkout: Check out from version control') {
+            steps { 
+                    checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: 's444463', url: 'https://git.wmi.amu.edu.pl/s444463/ium_444463.git']]])
+                }
+        }
+        stage('bash script') {
+            steps {
+                withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
+                         "KAGGLE_KEY=${params.KAGGLE_KEY}",
+                         "CUTOFF=${params.CUTOFF}"]) {
+                            sh 'python3 ./download_data_and_process.py'
+                            archiveArtifacts artifacts: "data_test.csv, data_dev.csv, data_train.csv, column_titles.csv, data.csv"
+                }
+            }
+        }
+    }
+}

README.md

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+# Run with docker
+`docker build  -t ium .`
+
+`docker run -e KAGGLE_USERNAME='your_kaggle_username' -e KAGGLE_KEY='<your_kaggle_key>' -e CUTOFF='1600' -it ium:latest`
+
+Read models results:
+
+`docker run -e KAGGLE_USERNAME='your_kaggle_username' -e KAGGLE_KEY='<your_kaggle_key>' -e CUTOFF='1600' -it ium:latest /bin/bash`
+`python3 deepl.py`
+`cat model_resutls.txt`

deepl.py

@@ -133,6 +133,7 @@ if __name__ == "__main__":
             else:
                 FN.append(d)
     f_score = len(TP) / (len(TP) + 0.5 * (len(FP) + len(FN)))
+    print(f"F- score = {f_score}")
     f = open("model_resutls.txt", "a")
 
     f.write(f"F-SCORE = {f_score}\n")
@@ -155,15 +156,15 @@ if __name__ == "__main__":
         f.write(i+"\n")
     f.close()
 
-    plt.figure(figsize=(12, 5))
-    ax = plt.subplot(121)
-    plt.xlabel('epochs')
-    plt.ylabel('negative log likelihood loss')
-    plt.plot(train_losses, label='Training loss')
-    plt.plot(test_losses, label='Validation loss')
-    plt.legend(frameon=False)
-    plt.subplot(122)
-    plt.xlabel('epochs')
-    plt.ylabel('test accuracy')
-    plt.plot(test_accuracies)
-    plt.show()
+    # plt.figure(figsize=(12, 5))
+    # ax = plt.subplot(121)
+    # plt.xlabel('epochs')
+    # plt.ylabel('negative log likelihood loss')
+    # plt.plot(train_losses, label='Training loss')
+    # plt.plot(test_losses, label='Validation loss')
+    # plt.legend(frameon=False)
+    # plt.subplot(122)
+    # plt.xlabel('epochs')
+    # plt.ylabel('test accuracy')
+    # plt.plot(test_accuracies)
+    # plt.show()