mlflow

MLproject

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+name: ium_s434766-mlflow
+
+docker_env:
+  image: owczarczykp/ium_s434766:latest
+
+entry_points:
+  main:
+    parameters:
+      num_epochs: {type: int, default: 10}
+      batch_size: {type: int, default: 16}
+    command: "python3 lab8-mlflow.py -r {batch_size} {num_epochs}"

lab8-mlflow.py

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+import torch
+import sys
+import mlflow
+import torch.nn.functional as F
+from torch import nn
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, mean_squared_error
+import numpy as np
+import pandas as pd
+np.set_printoptions(suppress=False)
+
+
+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)
+
+with mlflow.start_run():
+    data_train = pd.read_csv("data_train.csv")
+    data_test  = pd.read_csv("data_test.csv")
+    data_val   = pd.read_csv("data_val.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)
+
+    batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 16
+    num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
+    learning_rate = 0.001
+    input_dim = 6
+    output_dim = 1
+
+    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()
+    y_pred = model(fTest)
+    # print("predicted Y value: ", y_pred.data)
+
+    torch.save(model.state_dict(), 'stroke.pth')
+
+    rmse = mean_squared_error(tTest, y_pred.detach().numpy())
+    mlflow.log_metric("rmse", rmse)
+    mlflow.log_param("Last loss", loss.item())
+    mlflow.log_param("epochs", num_epochs)
+    mlflow.log_param("batch size", batch_size)
+

mlruns/0/0a44577d5489416ba432f0004c88ec5b/meta.yaml

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+experiment_id: '0'
+lifecycle_stage: active
+name: ''
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+run_uuid: 0a44577d5489416ba432f0004c88ec5b
+source_name: ''
+source_type: 4
+source_version: ''
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+status: 3
+tags: []
+user_id: przemek

mlruns/0/0a44577d5489416ba432f0004c88ec5b/metrics/rmse

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mlruns/0/0a44577d5489416ba432f0004c88ec5b/params/Last loss

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mlruns/0/0a44577d5489416ba432f0004c88ec5b/params/batch size

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+16

mlruns/0/0a44577d5489416ba432f0004c88ec5b/params/epochs

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+5

mlruns/0/0a44577d5489416ba432f0004c88ec5b/tags/mlflow.source.git.commit

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mlruns/0/0a44577d5489416ba432f0004c88ec5b/tags/mlflow.source.name

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+lab8-mlflow.py

mlruns/0/0a44577d5489416ba432f0004c88ec5b/tags/mlflow.source.type

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+LOCAL

mlruns/0/0a44577d5489416ba432f0004c88ec5b/tags/mlflow.user

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mlruns/0/8e73be609cf045c9a792e9712a5482d6/meta.yaml

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+status: 3
+tags: []
+user_id: przemek

mlruns/0/8e73be609cf045c9a792e9712a5482d6/metrics/rmse

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mlruns/0/8e73be609cf045c9a792e9712a5482d6/params/Last loss

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mlruns/0/8e73be609cf045c9a792e9712a5482d6/params/batch size

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mlruns/0/8e73be609cf045c9a792e9712a5482d6/params/epochs

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mlruns/0/8e73be609cf045c9a792e9712a5482d6/tags/mlflow.source.git.commit

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mlruns/0/8e73be609cf045c9a792e9712a5482d6/tags/mlflow.source.name

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mlruns/0/8e73be609cf045c9a792e9712a5482d6/tags/mlflow.source.type

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+LOCAL

mlruns/0/8e73be609cf045c9a792e9712a5482d6/tags/mlflow.user

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+przemek

mlruns/0/a5a700d065624643b0e7e7addd6ec479/meta.yaml

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mlruns/0/a5a700d065624643b0e7e7addd6ec479/metrics/rmse

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mlruns/0/a5a700d065624643b0e7e7addd6ec479/params/Last loss

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mlruns/0/a5a700d065624643b0e7e7addd6ec479/params/batch size

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mlruns/0/a5a700d065624643b0e7e7addd6ec479/params/epochs

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mlruns/0/a5a700d065624643b0e7e7addd6ec479/tags/mlflow.source.git.commit

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mlruns/0/a5a700d065624643b0e7e7addd6ec479/tags/mlflow.source.name

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+lab8-mlflow.py

mlruns/0/a5a700d065624643b0e7e7addd6ec479/tags/mlflow.source.type

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+LOCAL

mlruns/0/a5a700d065624643b0e7e7addd6ec479/tags/mlflow.user

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mlruns/0/bb1356a5d8fd4a048612acedc090167f/meta.yaml

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mlruns/0/bb1356a5d8fd4a048612acedc090167f/metrics/rmse

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mlruns/0/bb1356a5d8fd4a048612acedc090167f/params/Last loss

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mlruns/0/bb1356a5d8fd4a048612acedc090167f/params/batch size

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mlruns/0/bb1356a5d8fd4a048612acedc090167f/params/epochs

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mlruns/0/bb1356a5d8fd4a048612acedc090167f/tags/mlflow.source.git.commit

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mlruns/0/bb1356a5d8fd4a048612acedc090167f/tags/mlflow.source.name

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mlruns/0/bb1356a5d8fd4a048612acedc090167f/tags/mlflow.source.type

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+LOCAL

mlruns/0/bb1356a5d8fd4a048612acedc090167f/tags/mlflow.user

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+przemek

mlruns/0/meta.yaml

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