fix mlflow
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MLFLOW/Dockerfile
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13
MLFLOW/Dockerfile
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FROM python:3.11
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RUN apt-get update && apt-get -y upgrade
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RUN apt-get install -y build-essential
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RUN python -m pip install --upgrade pip
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COPY ../requirements.txt /tmp
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RUN python -m pip install -r /tmp/requirements.txt
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WORKDIR ./app
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COPY train.py ./
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CMD bash
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3
MLFLOW/requirements.txt
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MLFLOW/requirements.txt
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pandas
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tensorflow
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mlflow
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@ -3,8 +3,6 @@ from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras import layers
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import argparse
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import argparse
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import mlflow
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import mlflow
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class RegressionModel:
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class RegressionModel:
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def __init__(self, optimizer="adam", loss="mean_squared_error"):
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def __init__(self, optimizer="adam", loss="mean_squared_error"):
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self.model = keras.Sequential([
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self.model = keras.Sequential([
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@ -48,11 +46,11 @@ parser = argparse.ArgumentParser()
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parser.add_argument('--epochs')
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parser.add_argument('--epochs')
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args = parser.parse_args()
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args = parser.parse_args()
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mlflow.set_tracking_uri("http://localhost:5000")
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model = RegressionModel()
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with mlflow.start_run() as run:
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with mlflow.start_run() as run:
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model = RegressionModel()
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model.load_data("df_train.csv", "df_test.csv")
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model.train(epochs=int(args.epochs))
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model.train(epochs=int(args.epochs))
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mlflow.log_param("epochs", int(args.epochs))
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rmse = model.evaluate()
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rmse = model.evaluate()
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mlflow.log_param("epoch", int(args.epochs))
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mlflow.log_metric("rmse", rmse)
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mlflow.log_metric("rmse", rmse)
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model.save_model()
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model.save_model()
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@ -5,9 +5,9 @@ pd.set_option('display.max_columns', None)
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pd.set_option('display.max_rows', None)
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pd.set_option('display.max_rows', None)
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df = pd.read_csv('./dataset.csv')
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df = pd.read_csv('MLFLOW/dataset.csv')
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df_train = pd.read_csv('./df_train.csv')
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df_train = pd.read_csv('MLFLOW/df_train.csv')
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df_test = pd.read_csv('./df_test.csv')
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df_test = pd.read_csv('MLFLOW/df_test.csv')
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with open('stats.txt', 'w') as f:
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with open('stats.txt', 'w') as f:
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@ -6,7 +6,7 @@ import matplotlib.pyplot as plt
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np.set_printoptions(threshold=np.inf)
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np.set_printoptions(threshold=np.inf)
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data = pd.read_csv("df_test.csv")
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data = pd.read_csv("MLFLOW/df_test.csv")
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X_test = data.drop("Performance Index", axis=1)
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X_test = data.drop("Performance Index", axis=1)
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y_test = data["Performance Index"]
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y_test = data["Performance Index"]
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