Dodano do dockerfile i jenkinsa obsluge skryptu z pytorchem.
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12
Dockerfile
12
Dockerfile
@ -8,12 +8,18 @@ RUN pip3 install kaggle
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RUN apt install -y unzip
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RUN apt install -y unzip
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RUN mkdir /.kaggle
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RUN mkdir /.kaggle
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RUN chmod -R 777 /.kaggle
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RUN chmod -R 777 /.kaggle
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#RUN export KAGGLE_CONFIG_DIR=~/.kaggle
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COPY ./requirments.txt ./
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RUN pip3 install -r requirments.txt
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RUN pip3 install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
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# Stwórzmy w kontenerze (jeśli nie istnieje) katalog /app i przejdźmy do niego (wszystkie kolejne polecenia RUN, CMD, ENTRYPOINT, COPY i ADD będą w nim wykonywane)
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# Stwórzmy w kontenerze (jeśli nie istnieje) katalog /app i przejdźmy do niego (wszystkie kolejne polecenia RUN, CMD, ENTRYPOINT, COPY i ADD będą w nim wykonywane)
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WORKDIR /app
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WORKDIR /app
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# Skopiujmy nasz skrypt do katalogu /app w kontenerze
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# Skopiujmy nasz skrypt do katalogu /app w kontenerze
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COPY ./skrypt.sh ./
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# COPY ./skrypt.sh ./
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RUN chmod +x skrypt.sh
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# RUN chmod +x skrypt.sh
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RUN dos2unix skrypt.sh
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# RUN dos2unix skrypt.sh
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COPY ./dlgssdpytorch.py ./
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RUN chmod +x dlgssdpytorch.py
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6
Jenkinsfile
vendored
6
Jenkinsfile
vendored
@ -33,8 +33,8 @@ pipeline {
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script {
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script {
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def img = docker.build('rokoch/ium:01')
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def img = docker.build('rokoch/ium:01')
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img.inside {
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img.inside {
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sh 'chmod +x skrypt.sh'
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sh 'chmod +x dlgssdpytorch.py'
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sh './skrypt.sh $CUTOFF'
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sh 'python3 ./dlgssdpytorch.py | tee output.txt'
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}
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}
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}
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}
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}
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}
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@ -44,7 +44,7 @@ pipeline {
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stage('end') {
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stage('end') {
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steps {
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steps {
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//Zarchiwizuj wynik
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//Zarchiwizuj wynik
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archiveArtifacts 'output.txt,Global_Superstore22.csv,Global_Superstore2.csv.dev,Global_Superstore2.csv.test,Global_Superstore2.csv.train'
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archiveArtifacts 'output.txt'
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}
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}
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}
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}
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}
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}
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@ -149,4 +149,4 @@ for epoch in range(n_epochs):
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# Checks model's parameters
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# Checks model's parameters
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print(model.state_dict())
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print(model.state_dict())
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print("Mean squared error for training: ", np.mean(losses))
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print("Mean squared error for training: ", np.mean(losses))
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print("Mean squared error for valid: ", np.mean(val_losses))
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print("Mean squared error for validating: ", np.mean(val_losses))
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@ -21,9 +21,6 @@ six==1.15.0
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sklearn==0.0
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sklearn==0.0
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text-unidecode==1.3
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text-unidecode==1.3
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threadpoolctl==2.1.0
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threadpoolctl==2.1.0
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torch==1.8.1+cpu
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torchaudio==0.8.1
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torchvision==0.9.1+cpu
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torchviz==0.0.2
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torchviz==0.0.2
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tqdm==4.60.0
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tqdm==4.60.0
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typing-extensions==3.7.4.3
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typing-extensions==3.7.4.3
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