mlflow
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MLproject
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MLproject
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name: ium_s434766-mlflow
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docker_env:
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image: owczarczykp/ium_s434766:latest
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entry_points:
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main:
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parameters:
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num_epochs: {type: int, default: 10}
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batch_size: {type: int, default: 16}
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command: "python3 lab8-mlflow.py -r {batch_size} {num_epochs}"
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lab8-mlflow.py
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lab8-mlflow.py
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import torch
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import sys
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import mlflow
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import torch.nn.functional as F
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from torch import nn
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, mean_squared_error
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import numpy as np
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import pandas as pd
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np.set_printoptions(suppress=False)
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class LogisticRegressionModel(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(LogisticRegressionModel, self).__init__()
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self.linear = nn.Linear(input_dim, output_dim)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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out = self.linear(x)
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return self.sigmoid(out)
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with mlflow.start_run():
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data_train = pd.read_csv("data_train.csv")
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data_test = pd.read_csv("data_test.csv")
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data_val = pd.read_csv("data_val.csv")
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FEATURES = ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
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x_train = data_train[FEATURES].astype(np.float32)
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y_train = data_train['stroke'].astype(np.float32)
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x_test = data_test[FEATURES].astype(np.float32)
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y_test = data_test['stroke'].astype(np.float32)
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fTrain = torch.from_numpy(x_train.values)
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tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
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fTest= torch.from_numpy(x_test.values)
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tTest = torch.from_numpy(y_test.values)
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batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 16
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num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
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learning_rate = 0.001
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input_dim = 6
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output_dim = 1
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model = LogisticRegressionModel(input_dim, output_dim)
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criterion = torch.nn.BCELoss(reduction='mean')
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optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
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for epoch in range(num_epochs):
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# print ("Epoch #",epoch)
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model.train()
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optimizer.zero_grad()
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# Forward pass
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y_pred = model(fTrain)
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# Compute Loss
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loss = criterion(y_pred, tTrain)
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# print(loss.item())
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# Backward pass
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loss.backward()
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optimizer.step()
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y_pred = model(fTest)
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# print("predicted Y value: ", y_pred.data)
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torch.save(model.state_dict(), 'stroke.pth')
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rmse = mean_squared_error(tTest, y_pred.detach().numpy())
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mlflow.log_metric("rmse", rmse)
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mlflow.log_param("Last loss", loss.item())
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mlflow.log_param("epochs", num_epochs)
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mlflow.log_param("batch size", batch_size)
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mlruns/0/0a44577d5489416ba432f0004c88ec5b/meta.yaml
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artifact_uri: file:///home/przemek/ium_434766/mlruns/0/0a44577d5489416ba432f0004c88ec5b/artifacts
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end_time: 1621078832992
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entry_point_name: ''
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experiment_id: '0'
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lifecycle_stage: active
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name: ''
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run_id: 0a44577d5489416ba432f0004c88ec5b
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run_uuid: 0a44577d5489416ba432f0004c88ec5b
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source_name: ''
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source_type: 4
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source_version: ''
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start_time: 1621078832964
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status: 3
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tags: []
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user_id: przemek
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mlruns/0/0a44577d5489416ba432f0004c88ec5b/metrics/rmse
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lab8-mlflow.py
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LOCAL
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przemek
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mlruns/0/8e73be609cf045c9a792e9712a5482d6/meta.yaml
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artifact_uri: file:///home/przemek/ium_434766/mlruns/0/8e73be609cf045c9a792e9712a5482d6/artifacts
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end_time: 1621078794818
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entry_point_name: ''
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experiment_id: '0'
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lifecycle_stage: active
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name: ''
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run_id: 8e73be609cf045c9a792e9712a5482d6
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run_uuid: 8e73be609cf045c9a792e9712a5482d6
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source_name: ''
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source_type: 4
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source_version: ''
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start_time: 1621078794791
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status: 3
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tags: []
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user_id: przemek
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mlruns/0/8e73be609cf045c9a792e9712a5482d6/metrics/rmse
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lab8-mlflow.py
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przemek
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mlruns/0/a5a700d065624643b0e7e7addd6ec479/meta.yaml
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artifact_uri: file:///home/przemek/ium_434766/mlruns/0/a5a700d065624643b0e7e7addd6ec479/artifacts
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lifecycle_stage: active
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name: ''
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run_id: a5a700d065624643b0e7e7addd6ec479
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run_uuid: a5a700d065624643b0e7e7addd6ec479
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source_name: ''
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source_type: 4
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source_version: ''
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start_time: 1621078798285
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status: 3
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tags: []
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user_id: przemek
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mlruns/0/a5a700d065624643b0e7e7addd6ec479/metrics/rmse
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mlruns/0/bb1356a5d8fd4a048612acedc090167f/meta.yaml
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artifact_uri: file:///home/przemek/ium_434766/mlruns/0/bb1356a5d8fd4a048612acedc090167f/artifacts
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end_time: 1621078791156
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entry_point_name: ''
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experiment_id: '0'
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lifecycle_stage: active
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name: ''
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run_id: bb1356a5d8fd4a048612acedc090167f
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run_uuid: bb1356a5d8fd4a048612acedc090167f
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source_name: ''
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source_type: 4
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source_version: ''
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start_time: 1621078791105
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status: 3
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tags: []
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user_id: przemek
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mlruns/0/bb1356a5d8fd4a048612acedc090167f/metrics/rmse
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artifact_location: file:///home/przemek/ium_434766/mlruns/0
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experiment_id: '0'
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lifecycle_stage: active
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name: Default
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BIN
stroke.pth
BIN
stroke.pth
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