zadanka
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69db80f48a
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1
evaluate.py
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evaluate.py
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print('test')
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76
sacred1.py
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sacred1.py
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#! /usr/bin/python3
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from tensorflow.keras.models import Sequential, load_model
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from tensorflow.keras.layers import Dense
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from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import wget
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import numpy as np
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import requests
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from sacred.observers import FileStorageObserver
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from sacred import Experiment
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from datetime import datetime
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import os
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ex = Experiment("ium_s434695", interactive=False)
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ex.observers.append(FileStorageObserver('ium_s434695/my_runs'))
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@ex.config
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def my_config():
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train_size_param = 0.8
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test_size_param = 0.2
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@ex.capture
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def prepare_model(train_size_param, test_size_param, _run):
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_run.info["prepare_model_ts"] = str(datetime.now())
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url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
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r = requests.get(url, allow_redirects=True)
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open('vgsales.csv', 'wb').write(r.content)
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df = pd.read_csv('vgsales.csv')
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def regression_model():
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model = Sequential()
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model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(64,activation = "relu"))
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model.add(Dense(1,activation = "relu"))
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model.compile(optimizer = "adam", loss = "mean_squared_error")
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return model
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df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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df
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y = df.Nintendo
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df=((df-df.min())/(df.max()-df.min()))
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x = df.drop(['Nintendo'],axis = 1)
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x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
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model = regression_model()
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model.fit(x_train, y_train, epochs = 600, verbose = 1)
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y_pred = model.predict(x_test)
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y_pred[:5]
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y_pred = np.around(y_pred, decimals=0)
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y_pred[:5]
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return(classification_report(y_test,y_pred))
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@ex.main
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def my_main(train_size_param, test_size_param):
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print(prepare_model())
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r = ex.run()
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ex.add_artifact("vgsales_model/saved_model/saved_model.pb")
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sacred2.py
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sacred2.py
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#! /usr/bin/python3
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from tensorflow.keras.models import Sequential, load_model
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from tensorflow.keras.layers import Dense
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from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import wget
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import numpy as np
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import requests
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from sacred.observers import FileStorageObserver
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from sacred import Experiment
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from datetime import datetime
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import os
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from sacred.observers import MongoObserver
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ex = Experiment("ium_s434695", interactive=False)
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ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
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db_name='sacred'))
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@ex.config
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def my_config():
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train_size_param = 0.8
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test_size_param = 0.2
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@ex.capture
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def prepare_model(train_size_param, test_size_param, _run):
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_run.info["prepare_model_ts"] = str(datetime.now())
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url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
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r = requests.get(url, allow_redirects=True)
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open('vgsales.csv', 'wb').write(r.content)
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df = pd.read_csv('vgsales.csv')
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def regression_model():
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model = Sequential()
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model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(64,activation = "relu"))
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model.add(Dense(1,activation = "relu"))
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model.compile(optimizer = "adam", loss = "mean_squared_error")
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return model
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df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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df
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y = df.Nintendo
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df=((df-df.min())/(df.max()-df.min()))
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x = df.drop(['Nintendo'],axis = 1)
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x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
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model = regression_model()
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model.fit(x_train, y_train, epochs = 600, verbose = 1)
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y_pred = model.predict(x_test)
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y_pred[:5]
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y_pred = np.around(y_pred, decimals=0)
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y_pred[:5]
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return(classification_report(y_test,y_pred))
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@ex.main
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def my_main(train_size_param, test_size_param):
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print(prepare_model())
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r = ex.run()
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ex.add_artifact("vgsales_model/saved_model/saved_model.pb")
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42
train.py
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train.py
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#! /usr/bin/python3
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from tensorflow.keras.models import Sequential, load_model
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from tensorflow.keras.layers import Dense
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from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import numpy as np
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import requests
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url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
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r = requests.get(url, allow_redirects=True)
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open('vgsales.csv', 'wb').write(r.content)
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df = pd.read_csv('vgsales.csv')
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def regression_model():
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model = Sequential()
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model.add(Dense(16,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(32,activation = "relu"))
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model.add(Dense(1,activation = "relu"))
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model.compile(optimizer = "adam", loss = "mean_squared_error")
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return model
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df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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df
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y = df.Nintendo
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df=((df-df.min())/(df.max()-df.min()))
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x = df.drop(['Nintendo'],axis = 1)
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x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
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model = regression_model()
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model.fit(x_train, y_train, epochs = 600, verbose = 1)
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y_pred = model.predict(x_test)
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model.save('model1')
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train_evaluate/Dockerfile
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train_evaluate/Dockerfile
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# Nasz obraz będzie dzidziczył z obrazu Ubuntu w wersji latest
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FROM ubuntu:latest
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# Instalujemy niezbędne zależności. Zwróć uwagę na flagę "-y" (assume yes)
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RUN apt update && apt install -y figlet
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RUN apt install -y git
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RUN apt install -y python3-pip
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RUN pip3 install --user kaggle
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RUN pip3 install --user pandas
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RUN pip3 install --user numpy
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RUN pip3 install --user seaborn
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RUN pip3 install --user sklearn
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RUN pip3 install --user matplotlib
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RUN pip3 install --user tensorflow
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RUN pip3 install --user sacred
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RUN pip3 install --user wget
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WORKDIR /app
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COPY ./../train.py ./
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COPY ./../evaluate.py ./
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COPY ./../sacred1.py ./
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COPY ./../sacred2.py ./
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COPY ./../skrypt.sh ./
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COPY ./../zadanie2.py ./
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COPY ./../zadanie5.py ./
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train_evaluate/Jenkinsfile_evaluate
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train_evaluate/Jenkinsfile_evaluate
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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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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying data artifacts',
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name: 'WHICH_BUILD_DATA'
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)
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying train artifacts',
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name: 'WHICH_BUILD_TRAIN'
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)
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying current project artifacts',
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name: 'WHICH_BUILD_THIS'
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)
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}
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stages {
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stage('copy artifacts')
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{
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steps
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{
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copyArtifacts(fingerprintArtifacts: true, projectName: 's434695-create-dataset', selector: buildParameter('WHICH_BUILD_DATA'))
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copyArtifacts(fingerprintArtifacts: true, projectName: 's434695-training', selector: buildParameter('WHICH_BUILD_TRAIN'))
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copyArtifacts(fingerprintArtifacts: true, optional: true, projectName: 's434695-evaluation', selector: buildParameter('WHICH_BUILD_THIS'))
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}
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}
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stage('evaluate')
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{
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steps
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{
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catchError {
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sh 'python3 evaluate.py'
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}
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}
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}
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stage('send email') {
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steps {
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emailext body: currentBuild.result ?: 'SUCCESS',
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subject: 's434760 - evaluation',
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to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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}
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}
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}
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train_evaluate/Jenkinsfile_train
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train_evaluate/Jenkinsfile_train
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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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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying artifacts',
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name: 'WHICH_BUILD'
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)
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string(
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defaultValue: '16',
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description: 'batch size',
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name: 'BATCH_SIZE'
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)
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string(
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defaultValue: '15',
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description: 'epochs',
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name: 'EPOCHS'
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)
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}
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stages {
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stage('checkout') {
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steps {
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copyArtifacts fingerprintArtifacts: true, projectName: 's434695-create-dataset', selector: buildParameter('WHICH_BUILD')
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}
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}
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stage('Docker'){
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steps{
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sh 'python3 "./train.py"'
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sh 'python3 "./sacred1.py"'
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sh 'python3 "./sacred2.py"'
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}
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}
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stage('archiveArtifacts') {
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steps{
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archiveArtifacts 'ium_s434695/**'
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archiveArtifacts 'model1'
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}
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}
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}
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post {
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success {
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build job: 's434695-evaluation/master'
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mail body: 'SUCCESS TRAINING', subject: 's434695', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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failure {
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mail body: 'FAILURE TRAINING', subject: 's434695', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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}
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}
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def regression_model():
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model = Sequential()
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model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(64,activation = "relu"))
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model.add(Dense(16,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(32,activation = "relu"))
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model.add(Dense(1,activation = "relu"))
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model.compile(optimizer = "adam", loss = "mean_squared_error")
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