Dodano do dockerfile i jenkinsa obsluge skryptu z pytorchem.

Dockerfile

@@ -8,12 +8,18 @@ RUN pip3 install kaggle
 RUN apt install -y unzip
 RUN mkdir /.kaggle
 RUN chmod -R 777 /.kaggle
+#RUN export KAGGLE_CONFIG_DIR=~/.kaggle
+COPY ./requirments.txt ./
+RUN pip3 install -r requirments.txt
+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
 
 # 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)
 WORKDIR /app
 
 # Skopiujmy nasz skrypt do katalogu /app w kontenerze
-COPY ./skrypt.sh ./
-RUN chmod +x skrypt.sh
-RUN dos2unix skrypt.sh
+# COPY ./skrypt.sh ./
+# RUN chmod +x skrypt.sh
+# RUN dos2unix skrypt.sh
 
+COPY ./dlgssdpytorch.py ./
+RUN chmod +x dlgssdpytorch.py

Jenkinsfile

@@ -33,8 +33,8 @@ pipeline {
 					script {
 						def img = docker.build('rokoch/ium:01')
 						img.inside {
-							sh 'chmod +x skrypt.sh'
-							sh './skrypt.sh $CUTOFF'
+							sh 'chmod +x dlgssdpytorch.py'
+							sh 'python3 ./dlgssdpytorch.py | tee output.txt'
 						}
 					}
 				}
@@ -44,7 +44,7 @@ pipeline {
 		stage('end') {
             steps {
 				//Zarchiwizuj wynik
-				archiveArtifacts 'output.txt,Global_Superstore22.csv,Global_Superstore2.csv.dev,Global_Superstore2.csv.test,Global_Superstore2.csv.train'
+				archiveArtifacts 'output.txt'
 			}
 		}
 	}

dlgssdpytorch.py

@@ -149,4 +149,4 @@ for epoch in range(n_epochs):
 # Checks model's parameters
 print(model.state_dict())
 print("Mean squared error for training: ", np.mean(losses))
-print("Mean squared error for valid: ", np.mean(val_losses))   
+print("Mean squared error for validating: ", np.mean(val_losses))   

requirments.txt

@@ -21,9 +21,6 @@ six==1.15.0
 sklearn==0.0
 text-unidecode==1.3
 threadpoolctl==2.1.0
-torch==1.8.1+cpu
-torchaudio==0.8.1
-torchvision==0.9.1+cpu
 torchviz==0.0.2
 tqdm==4.60.0
 typing-extensions==3.7.4.3