32 lines
1.1 KiB
Plaintext
32 lines
1.1 KiB
Plaintext
pipeline {
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agent {
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docker {
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image 'agakul/ium:mlflow'}
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}
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parameters {
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying artifacts',
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name: 'BUILD_SELECTOR'
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)
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string(
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defaultValue: '{\\"inputs\\": [[167.39999389648438, 72.18000030517578, 40.0, 21.0, 94.0], [162.3000030517578, 67.30000305175781, 18.0, 52.0, 219.0], [178.5, 90.5, 14.699999809265137, 45.0, 262.0], [180.89999389648438, 77.0999984741211, 25.399999618530273, 43.0, 224.0], [177.3000030517578, 88.4800033569336, 35.599998474121094, 18.0, 183.0]]}',
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description: 'Inputs',
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name: 'INPUT'
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)
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}
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stages {
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stage('Copy artifacts') {
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steps {
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copyArtifacts fingerprintArtifacts: true, projectName: 's444421-training/training_and_evaluation', selector: buildParameter('BUILD_SELECTOR')
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}
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}
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stage('Predict') {
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steps {
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sh "echo ${params.INPUT} > input_example.json"
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sh "python predict_444501.py"
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}
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}
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}
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}
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