forked from s464914/ium_464914
add parameter
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parent
ac93029123
commit
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7
Jenkinsfile
vendored
7
Jenkinsfile
vendored
@ -1,11 +1,13 @@
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pipeline {
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pipeline {
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agent any
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agent any
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}
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parameters {
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parameters {
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buildSelector (
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buildSelector (
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defaultSelector: lastSuccessful(),
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defaultSelector: lastSuccessful(),
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description: 'Build for copying artifacts',
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description: 'Build for copying artifacts',
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name: 'BUILD_SELECTOR'
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name: 'BUILD_SELECTOR'
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)
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)
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string(name: 'EPOCHS', defaultValue: '10', description: 'epochs')
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}
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}
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stages {
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stages {
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stage('Git Checkout') {
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stage('Git Checkout') {
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@ -23,11 +25,10 @@ pipeline {
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script {
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script {
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def customImage = docker.build("custom-image")
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def customImage = docker.build("custom-image")
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customImage.inside {
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customImage.inside {
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sh 'python3 ./model.py'
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sh 'python3 ./model.py ${params.EPOCHS}'
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archiveArtifacts artifacts: 'model.pth, predictions.txt', onlyIfSuccessful: true
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archiveArtifacts artifacts: 'model.pth, predictions.txt', onlyIfSuccessful: true
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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3
model.py
3
model.py
@ -6,6 +6,7 @@ import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import LabelEncoder
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from sklearn.preprocessing import LabelEncoder
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import torch.nn.functional as F
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import torch.nn.functional as F
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import os
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device = (
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device = (
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@ -59,7 +60,7 @@ def main():
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val_loader = DataLoader(list(zip(X_val, y_val)), batch_size=64)
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val_loader = DataLoader(list(zip(X_val, y_val)), batch_size=64)
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# Training loop
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# Training loop
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epochs = 10
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epochs = os.getenv("EPOCHS")
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for epoch in range(epochs):
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for epoch in range(epochs):
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model.train() # Set model to training mode
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model.train() # Set model to training mode
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running_loss = 0.0
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running_loss = 0.0
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