mlflow save model
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@ -16,7 +16,7 @@ node {
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}
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}
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stage('Clone repo') {
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stage('Clone repo') {
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/* try {*/ docker.image("karopa/ium:27").inside {
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/* try {*/ docker.image("karopa/ium:28").inside {
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stage('Test') {
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stage('Test') {
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checkout([$class: 'GitSCM', branches: [[name: '*/master']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
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checkout([$class: 'GitSCM', branches: [[name: '*/master']], 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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copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_SELECTOR")
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@ -5,8 +5,8 @@ import numpy as np
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from tensorflow import keras
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from tensorflow import keras
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import sys
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import sys
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import mlflow.sklearn
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import mlflow
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import mlflow.models
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import logging
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import logging
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from evaluate_network import evaluate_model
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from evaluate_network import evaluate_model
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@ -66,3 +66,11 @@ with mlflow.start_run() as run:
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error = evaluate_model()
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error = evaluate_model()
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mlflow.log_metric("rmse", error)
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mlflow.log_metric("rmse", error)
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signature = mlflow.models.signature.infer_signature(X, model.predict(y))
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data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
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skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
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"publish_date", "publish_hour", "category_id",
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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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