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Wirusik
parent
39f02f99ff
commit
f7c9671206
20
Dockerfile
20
Dockerfile
@ -2,6 +2,7 @@
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FROM ubuntu:latest
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# Install required dependencies
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RUN export PATH="$PATH:/root/.local/bin"
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RUN apt update
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RUN apt-get update
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RUN apt install -y figlet
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@ -13,19 +14,18 @@ RUN pip3 install pandas
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RUN pip3 install pillow --global-option="build_ext" --global-option="--disable-zlib" --global-option="--disable-jpeg"
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RUN pip3 install scikit-learn
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RUN pip3 install matplotlib
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RUN pip3 install torchvision
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# Args
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ARG KAGGLE_USERNAME
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ARG KAGGLE_KEY
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ENV IS_DOCKER=True
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# Create app directory in image
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WORKDIR /app
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# Copy everything from jenkins to /app
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COPY . .
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ARG KAGGLE_USERNAME
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ARG KAGGLE_KEY
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# Download kaggle dataset
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RUN kaggle datasets download -d hakeem/atp-and-wta-tennis-data
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RUN unzip -o atp-and-wta-tennis-data.zip
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# Script executed after docker run
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RUN python3 ./init.py
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RUN chmod a+rwx -R *
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RUN ls -la
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# Create kaggle catalog for authenticate
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RUN mkdir /.kaggle/ && chmod o+w /.kaggle
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33
Jenkinsfile
vendored
33
Jenkinsfile
vendored
@ -1,4 +1,10 @@
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pipeline {
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agent {
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dockerfile {
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additionalBuildArgs '-t ium'
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args '-e KAGGLE_USERNAME=${params.KAGGLE_USERNAME} -e KAGGLE_KEY=${params.KAGGLE_KEY}'
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}
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}
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parameters {
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string (
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defaultValue: 'wirus006',
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@ -12,22 +18,23 @@ pipeline {
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name: 'KAGGLE_KEY'
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)
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}
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agent {
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dockerfile {
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additionalBuildArgs "--build-arg KAGGLE_USERNAME=${params.KAGGLE_USERNAME} --build-arg KAGGLE_KEY=${params.KAGGLE_KEY} -t s444498-create-dataset"
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}
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options {
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copyArtifactPermission('s444498-training');
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}
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stages {
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stage('Archive dataset') {
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stage('Init datasets') {
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steps {
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${params.KAGGLE_KEY}"]) {
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sh 'echo hello world | figlet'
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sh 'chmod a+rwx -R *'
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sh 'pwd && ls'
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sh 'ls /app/data/'
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archiveArtifacts artifacts: '/app/data/*', onlyIfSuccessful: true
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}
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sh 'python3 init.py'
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}
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}
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stage('Archive datasets') {
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steps {
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archiveArtifacts artifacts: 'atp_test.csv, atp_train.csv', onlyIfSuccessful: true
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}
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}
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stage('Run training job') {
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steps {
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build job: "s444498-training/master"
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}
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}
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}
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@ -1,4 +1,8 @@
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pipeline {
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agent {
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dockerfile true
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}
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parameters {
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string(
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defaultValue: '64',
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@ -12,7 +16,6 @@ pipeline {
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name: 'EPOCHS',
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trim: true
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)
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gitParameter branchFilter: 'origin/(.*)', defaultValue: 'main', name: 'BRANCH', type: 'PT_BRANCH'
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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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@ -20,39 +23,40 @@ pipeline {
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)
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}
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agent {
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docker {
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image 's444498-create-dataset'
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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: 's444498-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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}
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}
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stage('Train model') {
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steps {
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sh "python neutral_network.py -e ${params.EPOCHS} -b ${params.BATCHSIZE}"
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sh "chmod u+x ./neutral_network.py"
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sh "python3 neutral_network.py -e ${params.EPOCHS} -b ${params.BATCHSIZE}"
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}
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}
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stage('Archive model') {
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steps {
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archiveArtifacts artifacts: "model.zip", onlyIfSuccessful: true
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}
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}
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}
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environment {
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NOTIFICATION_ADDRESS = 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
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}
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post {
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success {
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emailext body: 'SUCCESS', subject: "${env.JOB_NAME}", to: "${env.NOTIFICATION_ADDRESS}"
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emailext body: "SUCCESS", subject: "s444498-training", to: "e19191c5.uam.onmicrosoft.com@emea.teams.ms"
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}
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failure {
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emailext body: 'FAILURE', subject: "${env.JOB_NAME}", to: "${env.NOTIFICATION_ADDRESS}"
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emailext body: "FAILURE", subject: "s444498-training", to: "e19191c5.uam.onmicrosoft.com@emea.teams.ms"
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}
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unstable {
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emailext body: 'UNSTABLE', subject: "${env.JOB_NAME}", to: "${env.NOTIFICATION_ADDRESS}"
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emailext body: 'UNSTABLE', subject: "s444498-training", to: "e19191c5.uam.onmicrosoft.com@emea.teams.ms"
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}
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changed {
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emailext body: 'CHANGED', subject: "${env.JOB_NAME}", to: "${env.NOTIFICATION_ADDRESS}"
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emailext body: 'CHANGED', subject: "s444498-training", to: "e19191c5.uam.onmicrosoft.com@emea.teams.ms"
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}
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}
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}
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26
init.py
26
init.py
@ -7,41 +7,36 @@ import matplotlib
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from pathlib import Path
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# Inicjalizacja danych
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file_exists = exists('./df_atp.csv')
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if not file_exists:
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subprocess.run(["kaggle", "datasets", "download", "-d", "hakeem/atp-and-wta-tennis-data"])
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subprocess.run(["unzip", "-o", "atp-and-wta-tennis-data.zip"])
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atp_data = pd.read_csv('df_atp.csv')
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print(atp_data)
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# Średnia ilość gemów w pierwszym secie zwycięzców meczu
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print(atp_data[["Winner", "W1"]].mean())
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# Minimalna ilość wygranych gemów w pierwszym secie osób wygrywających mecz
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print(atp_data[["Winner", "W1"]].min())
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# Maksymalna ilość wygranych gemów w pierwszym secie osób wygrywających mecz
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print(atp_data[["Winner", "W1"]].max())
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# Odchylenie standardowe wygranych gemów w pierwszym secie osób wygrywających mecz
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print(atp_data[["Winner", "W1"]].std())
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# Mediana wygranych gemów w pierwszym secie osób wygrywających mecz
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print(atp_data[["Winner", "W1"]].median())
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# Zmiana nazwy nienazwanej kolumny
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atp_data.rename(columns={'Unnamed: 0':'ID'}, inplace=True)
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# Jak często kto był zwycięzcą
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print(atp_data.groupby("Winner")["ID"].nunique())
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# Normalizacja rund -1: Finał, -2: Półfinał, -3: Ćwiartka, -4: Każdy z każdym
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# 1: pierwsza runda, 2: druga runda, 3: trzecia runda, 4: czwarta runda
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atp_data.loc[atp_data["Round"] == 'The Final', "Round"] = -1
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atp_data.loc[atp_data["Round"] == 'Semifinals', "Round"] = -2
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atp_data.loc[atp_data["Round"] == 'Quarterfinals', "Round"] = -3
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@ -53,28 +48,19 @@ atp_data.loc[atp_data["Round"] == '4th Round', "Round"] = 4
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print(atp_data["Round"])
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# Czyszczenie: W polu z datą zamienimy ######## na pustego stringa
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atp_data.loc[atp_data["Date"] == '########', "Date"] = ''
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print(atp_data["Date"])
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# Podział na podzbiory: trenujący, testowy, walidujący w proporcjach 6:2:2
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atp_train, atp_test = train_test_split(atp_data, test_size=0.4, random_state=1)
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atp_dev, atp_test = train_test_split(atp_test, test_size=0.5, random_state=1)
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# Wielkość zbioru i podzbiorów
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print("\nElements of total set: " + str(len(atp_data)))
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print("\nElements of test set: " + str(len(atp_test)))
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print("\nElements of dev set: " + str(len(atp_dev)))
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print("\nElements of train set: " + str(len(atp_train)))
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# Stworzenie plików z danymi trenującymi i testowymi
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filepath1 = Path('data/atp_test.csv')
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filepath2 = Path('data/atp_train.csv')
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filepath1.parent.mkdir(parents=True, exist_ok=True)
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filepath2.parent.mkdir(parents=True, exist_ok=True)
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atp_test.to_csv(filepath1)
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atp_train.to_csv(filepath2)
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atp_test.to_csv('atp_test.csv', encoding="utf-8", index=False)
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atp_train.to_csv('atp_train.csv', encoding="utf-8", index=False)
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@ -87,8 +87,8 @@ print(f"Using {device} device")
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args = setup_args()
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batch_size = args.batchSize
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plant_test = AtpDataset('data/atp_test.csv')
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plant_train = AtpDataset('data/atp_train.csv')
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plant_test = AtpDataset('atp_test.csv')
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plant_train = AtpDataset('atp_train.csv')
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train_dataloader = DataLoader(plant_train, batch_size=batch_size)
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test_dataloader = DataLoader(plant_test, batch_size=batch_size)
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