mlflow save model

Dockerfile

@@ -8,9 +8,9 @@ RUN pip3 install pandas
 RUN pip3 install kaggle
 RUN pip3 install tensorflow
 RUN pip3 install sklearn
-COPY ./data_train ./
+RUN pip3 install matplotlib
+RUN pip3 install mlflow
 COPY ./data_dev ./
-COPY ./neural_network.sh ./
+COPY ./evaluate_network.py ./
-COPY ./neural_network.py ./
 RUN mkdir /.kaggle
 RUN chmod -R 777 /.kaggle

Jenkinsfile

@@ -39,4 +39,9 @@ node {
                     }
 
         }
+        stage ("build training") {		//an arbitrary stage name
+            steps {
+                build 's434765-training/master/'	//this is where we specify which job to invoke.
+            }
+        }
     }

JenkinsfileEval

@@ -0,0 +1,51 @@
+node {
+        stage('Preparation') {
+
+                properties([
+                    parameters([
+                        buildSelector(defaultSelector: lastSuccessful(),
+                            description: 'Which build to use for copying datasets',
+                            name: 'BUILD_DATASET'),
+                       buildSelector(defaultSelector: lastSuccessful(),
+                            description: 'Which build to use for copying model',
+                            name: 'BUILD_MODEL'),
+                       gitParameter(branchFilter: 'origin/(.*)',
+                            defaultValue: 'master',
+                            name: 'BRANCH',
+                            type: 'PT_BRANCH')
+                       ])
+                ]
+                )
+
+        }
+        stage('Clone repo') {
+                    try {
+                        docker.image("karopa/ium:20").inside {
+
+                            stage('Test') {
+                                checkout([$class: 'GitSCM', branches: [[name: '*/evaluation']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
+                                copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_DATASET")
+                                copyArtifacts fingerprintArtifacts: true, projectName: 's434765-training/' + params.BRANCH + '/', selector: buildParameter("BUILD_MODEL")
+                                copyArtifacts fingerprintArtifacts: true, projectName: 's434765-evaluation/evaluation/', selector: lastSuccessful()
+                                sh '''
+                                    #!/usr/bin/env bash
+                                    chmod 777 evaluate_network.py
+                                    python3 evaluate_network.py | tee output.txt
+                                '''
+                                archiveArtifacts 'output.txt'
+                                archiveArtifacts 'rmse.txt'
+                                archiveArtifacts 'evaluation.png'
+                            }
+                            emailext body: 'Successful evaluation',
+                            subject: "s434765",
+                            to: "26ab8f35.uam.onmicrosoft.com@emea.teams.ms"
+                        }
+                     }
+                     catch (e) {
+                            emailext body: 'Failed evaluation',
+                            subject: "s434765",
+                            to: "26ab8f35.uam.onmicrosoft.com@emea.teams.ms"
+                            throw e
+                     }
+        }
+}

JenkinsfileMLflow

@@ -0,0 +1,29 @@
+node {
+         stage('Preparation') {
+
+                properties([
+                    parameters([
+                        buildSelector(defaultSelector: lastSuccessful(),
+                            description: 'Which build to use for copying artifacts',
+                            name: 'BUILD_SELECTOR')
+                            ])
+                ]
+                )
+
+        }
+        stage('Clone repo') {
+                        docker.image("karopa/ium:31").inside {
+                            stage('Test') {
+                                checkout([$class: 'GitSCM', branches: [[name: '*/mlflow']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
+                                copyArtifacts fingerprintArtifacts: true, projectName: 's437622-training/master/', selector: buildParameter("BUILD_SELECTOR")
+                                sh '''
+                                    #!/usr/bin/env bash
+                                    chmod 777 mlflow_partner.sh
+                                    rm -r model
+                                    ./mlflow_partner.sh | tee output.txt
+                                '''
+                                archiveArtifacts 'output.txt'
+                            }
+                        }
+        }
+}

JenkinsfileNeural

@@ -16,19 +16,21 @@ node {
 
         }
         stage('Clone repo') {
-                    try { docker.image("karopa/ium:11").inside {
+                   /* try {*/ docker.image("karopa/ium:32").inside {
                         stage('Test') {
-                            checkout([$class: 'GitSCM', branches: [[name: '*/master']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
+                            checkout([$class: 'GitSCM', branches: [[name: '*/mlflow']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
                             copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_SELECTOR")
                             sh '''
                                 #!/usr/bin/env bash
                                 chmod 777 neural_network.sh
+                                rm -r youtube_model
                                 ./neural_network.sh $EPOCHS | tee output.txt
                             '''
                             archiveArtifacts 'output.txt'
-                            archiveArtifacts 'model/**/*.*'
+                            archiveArtifacts 'youtube_model/**'
                         }
-                        emailext body: 'Successful build',
+                        }
+                        /*emailext body: 'Successful build',
                         subject: "s434765",
                         to: "26ab8f35.uam.onmicrosoft.com@emea.teams.ms"
                         }
@@ -41,4 +43,7 @@ node {
                             throw e
                      }
         }
+        stage ("build evaluation") {
+                build 's434765-evaluation/evaluation/'*/
         }
+}

MLproject

@@ -0,0 +1,12 @@
+name: tutorial
+
+docker_env:
+  image: karopa/ium:31
+
+entry_points:
+  main:
+    parameters:
+      epochs: {type: float, default: 30}
+    command: "python3 neural_network.py {epochs}"
+  test:
+    command: "python3 evaluate_network.py"

evaluate_network.py

@@ -0,0 +1,53 @@
+import pandas as pd
+import numpy as np
+from sklearn.metrics import mean_squared_error
+from tensorflow import keras
+import matplotlib.pyplot as plt
+
+
+def evaluate_model():
+    model = keras.models.load_model('model')
+    data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
+                       skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
+                                                                "publish_date", "publish_hour", "category_id",
+                                                                "channel_title", "views", "likes", "dislikes",
+                                                                "comment_count"]).dropna()
+    X_test = data.loc[:, data.columns == "views"].astype(int)
+    y_test = data.loc[:, data.columns == "likes"].astype(int)
+
+    min_val_sub = np.min(X_test)
+    max_val_sub = np.max(X_test)
+    X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
+    print(min_val_sub)
+    print(max_val_sub)
+
+    min_val_like = np.min(y_test)
+    max_val_like = np.max(y_test)
+    print(min_val_like)
+    print(max_val_like)
+
+    prediction = model.predict(X_test)
+
+    prediction_denormalized = []
+    for pred in prediction:
+        denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
+        prediction_denormalized.append(denorm)
+
+    f = open("predictions.txt", "w")
+    for (pred, test) in zip(prediction_denormalized, y_test.values):
+        f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
+
+    error = mean_squared_error(y_test, prediction_denormalized)
+    print(error)
+
+    with open("rmse.txt", "a") as file:
+        file.write(str(error) + "\n")
+
+    with open("rmse.txt", "r") as file:
+        lines = file.readlines()
+        plt.plot(range(len(lines)), [line[:-2] for line in lines])
+        plt.tight_layout()
+        plt.ylabel('RMSE')
+        plt.xlabel('evaluation no')
+        plt.savefig('evaluation.png')
+    return error

mlflow_partner.py

@@ -0,0 +1,4 @@
+from mlflow import keras
+
+model = keras.load_model('saved_model.pb')
+predict = model.predict([13, 1, 1500, 1500])

neural_network.py

@@ -1,9 +1,25 @@
+import warnings
+
 import pandas as pd
 import numpy as np
-from sklearn.metrics import mean_squared_error
 
 from tensorflow import keras
 import sys
+import mlflow
+import mlflow.models
+import logging
+
+from evaluate_network import evaluate_model
+
+logging.basicConfig(level=logging.WARN)
+logger = logging.getLogger(__name__)
+
+mlflow.set_tracking_uri("http://172.17.0.1:5000")
+mlflow.set_experiment("s434765")
+
+warnings.filterwarnings("ignore")
+np.random.seed(40)
+
 
 def normalize_data(data):
     return (data - np.min(data)) / (np.max(data) - np.min(data))
@@ -29,50 +45,36 @@ y = (y - min_val_like) / (max_val_like - min_val_like)
 print(min_val_like)
 print(max_val_like)
 
+with mlflow.start_run() as run:
+    print("MLflow run experiment_id: {0}".format(run.info.experiment_id))
+    print("MLflow run artifact_uri: {0}".format(run.info.artifact_uri))
 
-model = keras.Sequential([
+    mlflow.keras.autolog()
+    mlflow.log_param("epochs", int(sys.argv[1]))
+    model = keras.Sequential([
         keras.layers.Dense(512,input_dim = X.shape[1], activation='relu'),
         keras.layers.Dense(256, activation='relu'),
         keras.layers.Dense(256, activation='relu'),
         keras.layers.Dense(128, activation='relu'),
         keras.layers.Dense(1,activation='linear'),
-])
+    ])
 
-model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
+    model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
 
-model.fit(X, y, epochs=int(sys.argv[1]), validation_split = 0.3)
+    model.fit(X, y, epochs=int(sys.argv[1]), validation_split = 0.3)
 
-data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
+    model.save('model')
+
+    error = evaluate_model()
+    mlflow.log_metric("rmse", error)
+    signature = mlflow.models.signature.infer_signature(X, model.predict(y))
+    data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
                        skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
                                                                 "publish_date", "publish_hour", "category_id",
                                                                 "channel_title", "views", "likes", "dislikes",
                                                                 "comment_count"]).dropna()
-X_test = data.loc[:,data.columns == "views"].astype(int)
+    X_test = data.loc[:, data.columns == "views"].astype(int)
-y_test = data.loc[:,data.columns == "likes"].astype(int)
+    mlflow.keras.log_model(model, "youtube_model", registered_model_name="youtube_model", input_example=X_test,
-
+                           signature=signature)
-min_val_sub = np.min(X_test)
+    mlflow.keras.save_model(model, "youtube_model", registered_model_name="youtube_model", signature=signature,
-max_val_sub = np.max(X_test)
+                            input_example=X_test)
-X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
-print(min_val_sub)
-print(max_val_sub)
-
-min_val_like = np.min(y_test)
-max_val_like = np.max(y_test)
-print(min_val_like)
-print(max_val_like)
-
-prediction = model.predict(X_test)
-
-prediction_denormalized = []
-for pred in prediction:
-    denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
-    prediction_denormalized.append(denorm)
-
-f = open("predictions.txt", "w")
-for (pred, test) in zip(prediction_denormalized, y_test.values):
-    f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
-
-error = mean_squared_error(y_test, prediction_denormalized)
-print(error)
-
-model.save('model')

predictions.txt

@@ -1,422 +1,422 @@
-predicted: 440.75201439857483 expected: 617
+predicted: 413.1008868217468 expected: 617
-predicted: 87.643430352211 expected: 172
+predicted: 47.86212486028671 expected: 172
-predicted: 1361.890419960022 expected: 611
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-predicted: 168.88216811418533 expected: 269
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-predicted: 1361.890419960022 expected: 1095
+predicted: 1386.1633577346802 expected: 1095
-predicted: 168.88216811418533 expected: 68
+predicted: 130.3939750790596 expected: 68
-predicted: 50.43718174099922 expected: 5
+predicted: 19.90906798839569 expected: 5
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-predicted: 242.13134622573853 expected: 262
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