sacred

Dockerfile

@@ -2,7 +2,7 @@ FROM ubuntu:latest
 
 RUN apt-get update && apt-get install -y python3-pip && pip3 install setuptools && pip3 install numpy && pip3 install pandas && pip3 install wget && pip3 install scikit-learn && pip3 install matplotlib && rm -rf /var/lib/apt/lists/*
 RUN pip3 install torch torchvision torchaudio
-RUN pip3 install sacred 
+RUN pip3 install sacred && pip3 install GitPython
 WORKDIR /app
 
 COPY ./create.py ./

pytorch-training-eval/Dockerfile

@@ -1,7 +1,8 @@
 FROM ubuntu:latest
 
-RUN apt-get update && apt-get install -y python3-pip && pip3 install setuptools && pip3 install numpy && pip3 install pandas && pip3 install wget && pip3 install scikit-learn && pip3 install matplotlib && rm -rf /var/lib/apt/lists/*
+RUN apt-get update && apt-get install -y python3-pip && pip3 install setuptools && pip3 install numpy && pip3 install pandas && pip3 install wget && pip3 install scikit-learn && pip3 install matplotlib  && rm -rf /var/lib/apt/lists/*
 RUN pip3 install torch torchvision torchaudio 
+RUN pip3 install sacred && pip3 install GitPython
 WORKDIR /app 
 
 COPY ./../create.py ./

sacred-pytorch1.py

@@ -0,0 +1,85 @@
+import torch
+import sys
+import torch.nn.functional as F
+from torch import nn
+from sklearn.metrics import accuracy_score, mean_squared_error
+import numpy as np
+import pandas as pd
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+
+np.set_printoptions(suppress=False)
+
+ex = Experiment("ium_s434766", interactive=False, save_git_info=False)
+ex.observers.append(FileStorageObserver("ium_s434766/my_runs"))
+
+@ex.config
+def my_config():
+    num_epochs = 15
+    batch_size = 16
+    learning_rate = 0.001
+
+class LogisticRegressionModel(nn.Module):
+    def __init__(self, input_dim, output_dim):
+        super(LogisticRegressionModel, self).__init__()
+        self.linear = nn.Linear(input_dim, output_dim)
+        self.sigmoid = nn.Sigmoid()
+    def forward(self, x):
+        out = self.linear(x)
+        return self.sigmoid(out)
+
+@ex.capture
+def train(num_epochs, batch_size, learning_rate, _run):
+    data_train = pd.read_csv("data_train.csv")
+    data_test  = pd.read_csv("data_test.csv")
+    FEATURES   =  ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
+
+    x_train = data_train[FEATURES].astype(np.float32)
+    y_train = data_train['stroke'].astype(np.float32)
+
+    x_test = data_test[FEATURES].astype(np.float32)
+    y_test = data_test['stroke'].astype(np.float32)
+
+    fTrain = torch.from_numpy(x_train.values)
+    tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
+
+    fTest= torch.from_numpy(x_test.values)
+    tTest = torch.from_numpy(y_test.values)
+
+
+    input_dim = 6
+    output_dim = 1
+    info_params = "Batch size = " + str(batch_size) + " Epochs = " + str(num_epochs)
+    _run.info(info_params)
+    model = LogisticRegressionModel(input_dim, output_dim)
+
+    criterion = torch.nn.BCELoss(reduction='mean') 
+    optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
+
+    for epoch in range(num_epochs):
+        # print ("Epoch #",epoch)
+        model.train()
+        optimizer.zero_grad()
+        # Forward pass
+        y_pred = model(fTrain)
+        # Compute Loss
+        loss = criterion(y_pred, tTrain)
+        # print(loss.item())
+        # Backward pass
+        loss.backward()
+        optimizer.step()
+        
+    info_loss = "Last loss = " + str(loss.item())
+    _run.info(info_loss)
+    y_pred = model(fTest)
+    print("predicted Y value: ", y_pred.data)
+
+    torch.save(model.state_dict(), 'stroke.pth')
+
+
+@ex.automain
+def my_main(num_epochs, batch_size, learning_rate, _run):
+    train()
+
+r = ex.run()
+ex.add_artifact("stroke_model/stroke.pth")

sacred-pytorch2.py

@@ -0,0 +1,86 @@
+import torch
+import sys
+import torch.nn.functional as F
+from torch import nn
+from sklearn.metrics import accuracy_score, mean_squared_error
+import numpy as np
+import pandas as pd
+from sacred import Experiment
+from sacred.observers import MongoObserver
+
+np.set_printoptions(suppress=False)
+
+ex = Experiment("ium_s434766", interactive=False, save_git_info=False)
+ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
+                                  db_name='sacred'))
+
+@ex.config
+def my_config():
+    num_epochs = 15
+    batch_size = 16
+    learning_rate = 0.001
+
+class LogisticRegressionModel(nn.Module):
+    def __init__(self, input_dim, output_dim):
+        super(LogisticRegressionModel, self).__init__()
+        self.linear = nn.Linear(input_dim, output_dim)
+        self.sigmoid = nn.Sigmoid()
+    def forward(self, x):
+        out = self.linear(x)
+        return self.sigmoid(out)
+
+@ex.capture
+def train(num_epochs, batch_size, learning_rate, _run):
+    data_train = pd.read_csv("data_train.csv")
+    data_test  = pd.read_csv("data_test.csv")
+    FEATURES   =  ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
+
+    x_train = data_train[FEATURES].astype(np.float32)
+    y_train = data_train['stroke'].astype(np.float32)
+
+    x_test = data_test[FEATURES].astype(np.float32)
+    y_test = data_test['stroke'].astype(np.float32)
+
+    fTrain = torch.from_numpy(x_train.values)
+    tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
+
+    fTest= torch.from_numpy(x_test.values)
+    tTest = torch.from_numpy(y_test.values)
+
+
+    input_dim = 6
+    output_dim = 1
+    info_params = "Batch size = " + str(batch_size) + " Epochs = " + str(num_epochs)
+    _run.info(info_params)
+    model = LogisticRegressionModel(input_dim, output_dim)
+
+    criterion = torch.nn.BCELoss(reduction='mean') 
+    optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
+
+    for epoch in range(num_epochs):
+        # print ("Epoch #",epoch)
+        model.train()
+        optimizer.zero_grad()
+        # Forward pass
+        y_pred = model(fTrain)
+        # Compute Loss
+        loss = criterion(y_pred, tTrain)
+        # print(loss.item())
+        # Backward pass
+        loss.backward()
+        optimizer.step()
+        
+    info_loss = "Last loss = " + str(loss.item())
+    _run.info(info_loss)
+    y_pred = model(fTest)
+    print("predicted Y value: ", y_pred.data)
+
+    torch.save(model.state_dict(), 'stroke.pth')
+
+
+@ex.automain
+def my_main(num_epochs, batch_size, learning_rate, _run):
+    train()
+
+r = ex.run()
+ex.add_artifact("stroke_model/stroke.pth")

stroke-pytorch.py

@@ -13,10 +13,7 @@ from sacred import Experiment
 from sacred.observers import FileStorageObserver
 np.set_printoptions(suppress=False)
 
-# ex = Experiment("stroke-pytorch", interactive=True) 
-# ex.observers.append(FileStorageObserver('ium_s434766O_files'))
-# ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017',
-#                                   db_name='sacred'))
+
 class LogisticRegressionModel(nn.Module):
     def __init__(self, input_dim, output_dim):
         super(LogisticRegressionModel, self).__init__()
@@ -26,8 +23,7 @@ class LogisticRegressionModel(nn.Module):
         out = self.linear(x)
         return self.sigmoid(out)
 
-# @ex.main
-# def my_main(_log):
+
 data_train = pd.read_csv("data_train.csv")
 data_test  = pd.read_csv("data_test.csv")
 data_val   = pd.read_csv("data_val.csv")
@@ -50,8 +46,7 @@ num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
 learning_rate = 0.001
 input_dim = 6
 output_dim = 1
-info_params = "Batch size = " + str(batch_size) + " Epochs = " + str(num_epochs)
-# _log.info(info_params)
+
 model = LogisticRegressionModel(input_dim, output_dim)
 
 criterion = torch.nn.BCELoss(reduction='mean') 
@@ -69,11 +64,8 @@ for epoch in range(num_epochs):
     # Backward pass
     loss.backward()
     optimizer.step()
-info_loss = "Last loss = " + str(loss.item())
-    # _log.info(info_loss)
 y_pred = model(fTest)
 print("predicted Y value: ", y_pred.data)
 
 torch.save(model.state_dict(), 'stroke.pth')
 
-# ex.run()