Added customizable model params

Jenkinsfile-train

@@ -1,4 +1,19 @@
 pipeline {
+    parameters {
+        string(
+            defaultValue: '64',
+            description: 'Batch size used in ADAM',
+            name: 'BATCHSIZE',
+            trim: true
+        )
+        string(
+            defaultValue: '5',
+            description: 'Number of iterations',
+            name: 'EPOCHS',
+            trim: true
+        )
+    }
+
     agent {
         docker {
             image 's444409-create-dataset'
@@ -8,7 +23,7 @@ pipeline {
     stages {
         stage('Train model') {
             steps {
-                sh "python train_model.py"
+                sh "python train_model.py -e ${params.EPOCHS} -b ${params.BATCHSIZE}"
             }
         }
     }

train_model.py

@@ -1,10 +1,17 @@
+from ast import arg
+from sqlite3 import paramstyle
 import numpy as np
 import pandas as pd
 import torch
+import argparse
 from torch import nn
 from torch.utils.data import DataLoader, Dataset
 
 
+default_batch_size = 64
+default_epochs = 5
+
+
 def hour_to_int(text: str):
     return float(text.replace(':', ''))
 
@@ -82,10 +89,20 @@ def test(dataloader, model, loss_fn):
     print(f"Avg loss: {test_loss:>8f} \n")
 
 
+def setup_args():
+    args_parser = argparse.ArgumentParser(prefix_chars='-')
+    args_parser.add_argument('-b', '--batchSize', type=int, default=default_batch_size)
+    args_parser.add_argument('-e', '--epochs', type=int, default=default_epochs)
+
+    return args_parser.parse_args()
+
+
 device = "cuda" if torch.cuda.is_available() else "cpu"
 print(f"Using {device} device")
 
-batch_size = 64
+args = setup_args()
+
+batch_size = args.batchSize
 
 plant_test = PlantsDataset('data/Plant_1_Generation_Data.csv.test')
 plant_train = PlantsDataset('data/Plant_1_Generation_Data.csv.train')
@@ -103,7 +120,7 @@ print(model)
 
 loss_fn = nn.MSELoss()
 optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
-epochs = 5
+epochs = args.epochs
 for t in range(epochs):
     print(f"Epoch {t + 1}\n-------------------------------")
     train(train_dataloader, model, loss_fn, optimizer)