mlflow save model
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@ -16,7 +16,7 @@ node {
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}
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stage('Clone repo') {
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/* try {*/ docker.image("karopa/ium:30").inside {
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/* try {*/ docker.image("karopa/ium:31").inside {
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stage('Test') {
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checkout([$class: 'GitSCM', branches: [[name: '*/mlflow']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
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copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_SELECTOR")
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@ -24,6 +24,7 @@ node {
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#!/usr/bin/env bash
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chmod 777 neural_network.sh
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./neural_network.sh $EPOCHS | tee output.txt
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mlflow run ium_434765 -e main
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'''
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archiveArtifacts 'output.txt'
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archiveArtifacts 'model/**/*.*'
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@ -1,7 +1,7 @@
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name: tutorial
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docker_env:
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image: karopa/ium:30
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image: karopa/ium:31
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entry_points:
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main:
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@ -73,4 +73,4 @@ with mlflow.start_run() as run:
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"channel_title", "views", "likes", "dislikes",
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"comment_count"]).dropna()
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X_test = data.loc[:, data.columns == "views"].astype(int)
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mlflow.keras.save_model(model, "model", signature=signature, input_example=X_test)
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mlflow.keras.save_model(model, "model", registered_model_name="model", signature=signature, input_example=X_test)
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