should be good now
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@ -14,6 +14,8 @@ RUN pip3 install torch
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RUN pip3 install sacred
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RUN pip3 install sacred
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RUN pip3 install pymongo
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RUN pip3 install pymongo
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RUN pip3 install mlflow
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RUN pip3 install mlflow
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RUN pip3 install GitPython
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ARG CUTOFF
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ARG CUTOFF
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ARG KAGGLE_USERNAME
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ARG KAGGLE_USERNAME
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@ -21,7 +21,7 @@ pipeline {
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}
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}
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stage('Train model with sacred') {
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stage('Train model with sacred') {
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steps {
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steps {
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sh 'python3 ./biblioteka_DL/dllib.py "epochs=$EPOCHS"'
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sh 'python3 ./biblioteka_DL/dllib.py "$EPOCHS"'
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archiveArtifacts artifacts: 'model.pkl, result.csv', followSymlinks: false
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archiveArtifacts artifacts: 'model.pkl, result.csv', followSymlinks: false
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archiveArtifacts artifacts: 'mlruns/**'
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archiveArtifacts artifacts: 'mlruns/**'
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archiveArtifacts artifacts: 'my_model/**'
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archiveArtifacts artifacts: 'my_model/**'
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@ -102,10 +102,10 @@ def my_main(epochs):
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X_train = X_train.to_numpy()
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X_train = X_train.to_numpy()
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y_train = pd.DataFrame(data_train["Gross"], dtype=np.float64)
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y_train = pd.DataFrame(data_train["Gross"], dtype=np.float64)
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y_train = y_train.to_numpy()
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y_train = y_train.to_numpy()
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X_train = X_train.reshape(-1, 1)
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X_train_data = X_train.reshape(-1, 1)
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y_train = y_train.reshape(-1, 1)
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y_train_data = y_train.reshape(-1, 1)
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X_train = torch.from_numpy(X_train.astype(np.float32)).view(-1, 1)
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X_train = torch.from_numpy(X_train_data.astype(np.float32)).view(-1, 1)
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y_train = torch.from_numpy(y_train.astype(np.float32)).view(-1, 1)
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y_train = torch.from_numpy(y_train_data.astype(np.float32)).view(-1, 1)
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input_size = 1
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input_size = 1
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output_size = 1
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output_size = 1
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model = nn.Linear(input_size, output_size)
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model = nn.Linear(input_size, output_size)
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@ -149,7 +149,7 @@ def my_main(epochs):
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torch.save(model, "model.pkl")
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torch.save(model, "model.pkl")
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input_example = gross_test_g
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input_example = gross_test_g
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siganture = infer_signature(gross_test_g, X_train)
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siganture = infer_signature(X_train_data, y_train_data)
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tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
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tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
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# print(tracking_url_type_store)
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# print(tracking_url_type_store)
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