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@ -36,9 +36,6 @@ pipeline {
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stage('Train model') {
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stage('Train model') {
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steps {
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steps {
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sh "python train_model.py -e ${params.EPOCHS} -b ${params.BATCHSIZE}"
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sh "python train_model.py -e ${params.EPOCHS} -b ${params.BATCHSIZE}"
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archiveArtifacts artifacts: 'model_out', onlyIfSuccessful: true
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archiveArtifacts artifacts: 'mlruns/**', onlyIfSuccessful: true
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sh 'rm -r mlruns'
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}
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}
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}
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}
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stage('Evaluate model') {
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stage('Evaluate model') {
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@ -14,7 +14,7 @@ default_epochs = 5
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device = "cuda" if torch.cuda.is_available() else "cpu"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# mlflow.set_tracking_uri("http://172.17.0.1:5000")
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mlflow.set_tracking_uri("http://172.17.0.1:5000")
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mlflow.set_experiment("s444409")
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mlflow.set_experiment("s444409")
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@ -80,8 +80,8 @@ if __name__ == "__main__":
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registered_model_name="s444409",
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registered_model_name="s444409",
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signature=signature,
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signature=signature,
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input_example=input_example,
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input_example=input_example,
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extra_files=['model.py']
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code_paths=['model.py']
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)
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)
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else:
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else:
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mlflow.pytorch.log_model(model, "s444409", signature=signature, input_example=input_example,
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mlflow.pytorch.log_model(model, "s444409", signature=signature, input_example=input_example,
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extra_files=['model.py'])
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code_paths=['model.py'])
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