diff --git a/Jenkinsfile b/Jenkinsfile
index 4faa5a8..63f9b49 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -29,16 +29,13 @@ node {
               "KAGGLE_KEY=${params.KAGGLE_KEY}",
               "CUTOFF=${params.CUTOFF}"]) {
             sh "./script.sh ${CUTOFF}"
-            sh "./learning.py"
      }
-
  }
 
 
  stage('artifacts') {
          echo 'saving artifacts'
          archiveArtifacts 'output.txt'
-         archiveArtifacts 'model.pt'
 }
 }
 }
diff --git a/JenkinsfileTrain b/JenkinsfileTrain
new file mode 100644
index 0000000..b4743eb
--- /dev/null
+++ b/JenkinsfileTrain
@@ -0,0 +1,35 @@
+node {
+    checkout scm
+    def dockerimage = docker.build("titanic-image")
+    dockerimage.inside {
+ stage('Preparation') {
+     properties([
+         parameters([
+             string(
+                 defaultValue: 'default',
+                 description: 'Number of head lines to be taken from test file',
+                 name: 'LEARNING_PARAMETERS',
+                 trim: false)
+         ])
+     ])
+     copyArtifacts projectName: 's470618-create-dataset', filter: '*.csv', fingerprintArtifacts: true, selector: lastSuccessful(), target: '.'
+ }
+ stage('Build') {
+     withEnv(["LEARNING_PARAMETERS"=${params.LEARNING_PARAMETERS}]) {
+            sh "./learning.py ${LEARNING_PARAMETERS}"
+     }
+ }
+
+
+ stage('artifacts') {
+         echo 'saving artifacts'
+         archiveArtifacts 'model.pt'
+}
+stage('Trigger Learning pipeline') {
+            steps {
+                build 's470618-training'
+            }
+        }
+}
+}
+
diff --git a/learning.py b/learning.py
index e0c2250..6ccaf74 100755
--- a/learning.py
+++ b/learning.py
@@ -4,6 +4,7 @@ import torch
 from torch import nn
 import pandas as pd
 import subprocess
+import sys
 
 from sklearn.model_selection import train_test_split
 import torch.nn.functional as F
@@ -27,7 +28,15 @@ def print_(loss):
     print ("The loss calculated: ", loss)
 
 if __name__ == "__main__":
-    df = pd.read_csv("train.csv")
+
+    if sys.argv[1]=='default':
+        alpha = 0.003 #learning rate
+        epochs = 1000
+    else:
+        pass
+        #TODO split args string to make hyperparameters work
+
+    df = pd.read_csv("output.csv")
     df = df.dropna() #drop NA values
 
     columns_to_normalize=['Age','Fare'] #NORMALIZATION
@@ -52,9 +61,8 @@ if __name__ == "__main__":
     Yt = torch.tensor(Y_train, dtype=torch.long)
 
     model = Model(Xt.shape[1])
-    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+    optimizer = torch.optim.Adam(model.parameters(), lr=alpha)
     loss_fn = nn.CrossEntropyLoss()
-    epochs = 1000
 
     #TRAINING LOOP
     for epoch in range(1, epochs+1):
diff --git a/script.sh b/script.sh
index 8077f6a..56792f2 100755
--- a/script.sh
+++ b/script.sh
@@ -3,5 +3,5 @@ kaggle competitions download -c titanic
 echo 'kaggle download completed'
 unzip titanic.zip
 wc -l train.csv
-head -$1 train.csv | shuf > output.txt
+head -$1 train.csv | shuf > output.csv
 echo 'script done'