update server data
This commit is contained in:
parent
7015b5c1a5
commit
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# train.py
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#
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# author: deng
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# date : 20230221
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import torch
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import torch.nn as nn
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from torch.optim import SGD
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from tqdm import tqdm
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import mlflow
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class Net(nn.Module):
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""" define a simple neural network model """
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def __init__(self):
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super(Net, self).__init__()
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self.fc1 = nn.Linear(10, 5)
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self.fc2 = nn.Linear(5, 1)
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def forward(self, x):
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x = self.fc1(x)
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x = torch.relu(x)
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x = self.fc2(x)
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return x
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def train(model, dataloader, criterion, optimizer, epochs):
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""" define the training function """
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for epoch in tqdm(range(epochs), 'Epochs'):
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for i, (inputs, labels) in enumerate(dataloader):
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# forwarding
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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# update gradient
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# log loss
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mlflow.log_metric('train_loss', loss.item(), step=i)
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return loss
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if __name__ == '__main__':
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# set hyper parameters
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learning_rate = 1e-2
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epochs = 20
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# create a dataloader with fake data
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dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
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dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
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# create the model, criterion, and optimizer
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model = Net()
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criterion = nn.MSELoss()
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optimizer = SGD(model.parameters(), lr=learning_rate)
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# set the tracking URI to the model registry
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mlflow.set_tracking_uri('http://127.0.0.1:5000')
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# start the MLflow run
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with mlflow.start_run():
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# train the model and log the loss
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loss = train(model, dataloader, criterion, optimizer, epochs)
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# log some additional metrics
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mlflow.log_metric('final_loss', loss.item())
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mlflow.log_param('learning_rate', learning_rate)
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# log trained model
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mlflow.pytorch.log_model(model, 'model')
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# log training code
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mlflow.log_artifact('./train.py', 'code')
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print('Completed.')
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artifact_path: model
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flavors:
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python_function:
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data: data
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env: conda.yaml
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loader_module: mlflow.pytorch
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pickle_module_name: mlflow.pytorch.pickle_module
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python_version: 3.10.9
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pytorch:
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code: null
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model_data: data
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pytorch_version: 1.13.1
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mlflow_version: 1.30.0
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model_uuid: 18b69aa38c064c579c9b465d7a826081
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run_id: 410d85525e5f4cfe9839a432d35f9ad2
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utc_time_created: '2023-02-22 00:42:48.668457'
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@ -0,0 +1,11 @@
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channels:
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- conda-forge
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dependencies:
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- python=3.10.9
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- pip<=23.0.1
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- pip:
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- mlflow
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- cloudpickle==2.2.1
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- torch==1.13.1
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- tqdm==4.64.1
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name: mlflow-env
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Binary file not shown.
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@ -0,0 +1 @@
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mlflow.pytorch.pickle_module
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@ -0,0 +1,7 @@
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python: 3.10.9
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build_dependencies:
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- pip==23.0.1
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- setuptools==67.3.2
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- wheel==0.38.4
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dependencies:
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- -r requirements.txt
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@ -0,0 +1,4 @@
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mlflow
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cloudpickle==2.2.1
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torch==1.13.1
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tqdm==4.64.1
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@ -0,0 +1,16 @@
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artifact_path: model
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flavors:
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python_function:
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data: data
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env: conda.yaml
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loader_module: mlflow.pytorch
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pickle_module_name: mlflow.pytorch.pickle_module
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python_version: 3.10.9
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pytorch:
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code: null
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model_data: data
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pytorch_version: 1.13.1
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mlflow_version: 1.30.0
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model_uuid: 957e2f6e4fd048c99aee3150c73c4078
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run_id: c4fd84a025e1474d87cdc2919874b88c
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utc_time_created: '2023-02-22 00:41:33.282088'
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@ -0,0 +1,11 @@
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channels:
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- conda-forge
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dependencies:
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- python=3.10.9
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- pip<=23.0.1
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- pip:
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- mlflow
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- cloudpickle==2.2.1
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- torch==1.13.1
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- tqdm==4.64.1
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name: mlflow-env
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Binary file not shown.
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@ -0,0 +1 @@
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mlflow.pytorch.pickle_module
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@ -0,0 +1,7 @@
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python: 3.10.9
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build_dependencies:
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- pip==23.0.1
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- setuptools==67.3.2
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- wheel==0.38.4
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dependencies:
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- -r requirements.txt
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@ -0,0 +1,4 @@
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mlflow
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cloudpickle==2.2.1
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torch==1.13.1
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tqdm==4.64.1
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# train.py
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#
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# author: deng
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# date : 20230221
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import torch
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import torch.nn as nn
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from torch.optim import SGD
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from tqdm import tqdm
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import mlflow
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class Net(nn.Module):
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""" define a simple neural network model """
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def __init__(self):
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super(Net, self).__init__()
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self.fc1 = nn.Linear(10, 5)
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self.fc2 = nn.Linear(5, 1)
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def forward(self, x):
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x = self.fc1(x)
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x = torch.relu(x)
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x = self.fc2(x)
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return x
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def train(model, dataloader, criterion, optimizer, epochs):
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""" define the training function """
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for epoch in tqdm(range(epochs), 'Epochs'):
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for i, (inputs, labels) in enumerate(dataloader):
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# forwarding
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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# update gradient
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# log loss
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mlflow.log_metric('train_loss', loss.item(), step=i)
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return loss
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if __name__ == '__main__':
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# set hyper parameters
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learning_rate = 1e-2
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epochs = 20
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# create a dataloader with fake data
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dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
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dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
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# create the model, criterion, and optimizer
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model = Net()
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criterion = nn.MSELoss()
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optimizer = SGD(model.parameters(), lr=learning_rate)
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# set the tracking URI to the model registry
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mlflow.set_tracking_uri('http://127.0.0.1:5000')
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# start the MLflow run
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with mlflow.start_run():
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# train the model and log the loss
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loss = train(model, dataloader, criterion, optimizer, epochs)
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# log some additional metrics
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mlflow.log_metric('final_loss', loss.item())
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mlflow.log_param('learning_rate', learning_rate)
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# log trained model
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mlflow.pytorch.log_model(model, 'model')
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# log training code
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mlflow.log_artifact('./train.py')
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print('Completed.')
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# train.py
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#
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# author: deng
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# date : 20230221
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import torch
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import torch.nn as nn
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from torch.optim import SGD
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from tqdm import tqdm
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import mlflow
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class Net(nn.Module):
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""" define a simple neural network model """
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def __init__(self):
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super(Net, self).__init__()
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self.fc1 = nn.Linear(10, 5)
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self.fc2 = nn.Linear(5, 1)
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def forward(self, x):
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x = self.fc1(x)
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x = torch.relu(x)
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x = self.fc2(x)
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return x
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def train(model, dataloader, criterion, optimizer, epochs):
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""" define the training function """
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for epoch in tqdm(range(epochs), 'Epochs'):
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for i, (inputs, labels) in enumerate(dataloader):
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# forwarding
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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# update gradient
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# log loss
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mlflow.log_metric('train_loss', loss.item(), step=i)
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return loss
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if __name__ == '__main__':
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# set hyper parameters
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learning_rate = 1e-2
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epochs = 20
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# create a dataloader with fake data
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dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
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dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
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# create the model, criterion, and optimizer
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model = Net()
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criterion = nn.MSELoss()
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optimizer = SGD(model.parameters(), lr=learning_rate)
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# set the tracking URI to the model registry
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mlflow.set_tracking_uri('http://127.0.0.1:5000')
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mlflow.set_experiment('/mlflow_testing')
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# start the MLflow run
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with mlflow.start_run():
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# train the model
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loss = train(model, dataloader, criterion, optimizer, epochs)
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# log some additional metrics
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mlflow.log_metric('final_loss', loss.item())
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mlflow.log_param('learning_rate', learning_rate)
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# log trained model
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mlflow.pytorch.log_model(model, 'model')
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# log training code
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mlflow.log_artifact('./train.py', 'code')
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print('Completed.')
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@ -0,0 +1,16 @@
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artifact_path: model
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flavors:
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python_function:
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data: data
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env: conda.yaml
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loader_module: mlflow.pytorch
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pickle_module_name: mlflow.pytorch.pickle_module
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python_version: 3.10.9
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pytorch:
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code: null
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model_data: data
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pytorch_version: 1.13.1
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mlflow_version: 1.30.0
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model_uuid: ff8b845d6a174ffabfc49a18673c6c04
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run_id: c248a4299f97423987a9496a2241ab1a
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utc_time_created: '2023-02-22 01:10:55.971443'
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@ -0,0 +1,11 @@
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channels:
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- conda-forge
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dependencies:
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- python=3.10.9
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- pip<=23.0.1
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- pip:
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- mlflow
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- cloudpickle==2.2.1
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- torch==1.13.1
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- tqdm==4.64.1
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name: mlflow-env
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Binary file not shown.
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@ -0,0 +1 @@
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mlflow.pytorch.pickle_module
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@ -0,0 +1,7 @@
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python: 3.10.9
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build_dependencies:
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- pip==23.0.1
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- setuptools==67.3.2
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- wheel==0.38.4
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dependencies:
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- -r requirements.txt
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|
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@ -0,0 +1,4 @@
|
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mlflow
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cloudpickle==2.2.1
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torch==1.13.1
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tqdm==4.64.1
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@ -0,0 +1,83 @@
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# train.py
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#
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# author: deng
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# date : 20230221
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|
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import torch
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import torch.nn as nn
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from torch.optim import SGD
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from tqdm import tqdm
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import mlflow
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|
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|
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class Net(nn.Module):
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""" define a simple neural network model """
|
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def __init__(self):
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super(Net, self).__init__()
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self.fc1 = nn.Linear(10, 5)
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self.fc2 = nn.Linear(5, 1)
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|
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def forward(self, x):
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x = self.fc1(x)
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x = torch.relu(x)
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x = self.fc2(x)
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return x
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|
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|
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def train(model, dataloader, criterion, optimizer, epochs):
|
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""" define the training function """
|
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for epoch in tqdm(range(epochs), 'Epochs'):
|
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|
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for i, (inputs, labels) in enumerate(dataloader):
|
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|
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# forwarding
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outputs = model(inputs)
|
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loss = criterion(outputs, labels)
|
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|
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# update gradient
|
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optimizer.zero_grad()
|
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loss.backward()
|
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optimizer.step()
|
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|
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# log loss
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mlflow.log_metric('train_loss', loss.item(), step=i)
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return loss
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|
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|
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if __name__ == '__main__':
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|
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# set hyper parameters
|
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learning_rate = 1e-2
|
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epochs = 20
|
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|
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# create a dataloader with fake data
|
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dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
|
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dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
|
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|
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# create the model, criterion, and optimizer
|
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model = Net()
|
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criterion = nn.MSELoss()
|
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optimizer = SGD(model.parameters(), lr=learning_rate)
|
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|
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# set the tracking URI to the model registry
|
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mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
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mlflow.set_experiment('mlflow_testing')
|
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|
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# start the MLflow run
|
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with mlflow.start_run():
|
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|
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# train the model
|
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loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model')
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|
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# log training code
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mlflow.log_artifact('./train.py', 'code')
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print('Completed.')
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@ -0,0 +1,16 @@
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artifact_path: model
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flavors:
|
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python_function:
|
||||
data: data
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||||
env: conda.yaml
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||||
loader_module: mlflow.pytorch
|
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pickle_module_name: mlflow.pytorch.pickle_module
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python_version: 3.10.9
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pytorch:
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code: null
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model_data: data
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pytorch_version: 1.13.1
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mlflow_version: 1.30.0
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model_uuid: 27a96ad04f5a4578a3e1302500ad9a11
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run_id: b7d7395b6b53404497f7656b07b71bf8
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utc_time_created: '2023-02-22 01:11:36.809812'
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|
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@ -0,0 +1,11 @@
|
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channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.10.9
|
||||
- pip<=23.0.1
|
||||
- pip:
|
||||
- mlflow
|
||||
- cloudpickle==2.2.1
|
||||
- torch==1.13.1
|
||||
- tqdm==4.64.1
|
||||
name: mlflow-env
|
||||
Binary file not shown.
|
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@ -0,0 +1 @@
|
|||
mlflow.pytorch.pickle_module
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||||
|
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@ -0,0 +1,7 @@
|
|||
python: 3.10.9
|
||||
build_dependencies:
|
||||
- pip==23.0.1
|
||||
- setuptools==67.3.2
|
||||
- wheel==0.38.4
|
||||
dependencies:
|
||||
- -r requirements.txt
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||||
|
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@ -0,0 +1,4 @@
|
|||
mlflow
|
||||
cloudpickle==2.2.1
|
||||
torch==1.13.1
|
||||
tqdm==4.64.1
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
# train.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD
|
||||
from tqdm import tqdm
|
||||
import mlflow
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
""" define a simple neural network model """
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.fc1 = nn.Linear(10, 5)
|
||||
self.fc2 = nn.Linear(5, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
def train(model, dataloader, criterion, optimizer, epochs):
|
||||
""" define the training function """
|
||||
for epoch in tqdm(range(epochs), 'Epochs'):
|
||||
|
||||
for i, (inputs, labels) in enumerate(dataloader):
|
||||
|
||||
# forwarding
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# update gradient
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# log loss
|
||||
mlflow.log_metric('train_loss', loss.item(), step=i)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set hyper parameters
|
||||
learning_rate = 1e-2
|
||||
epochs = 20
|
||||
|
||||
# create a dataloader with fake data
|
||||
dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
|
||||
dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
|
||||
|
||||
# create the model, criterion, and optimizer
|
||||
model = Net()
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = SGD(model.parameters(), lr=learning_rate)
|
||||
|
||||
# set the tracking URI to the model registry
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
mlflow.set_experiment('mlflow_testing')
|
||||
|
||||
# start the MLflow run
|
||||
with mlflow.start_run():
|
||||
|
||||
# train the model
|
||||
loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model')
|
||||
|
||||
# log training code
|
||||
mlflow.log_artifact('./train.py', 'code')
|
||||
|
||||
print('Completed.')
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
data: data
|
||||
env: conda.yaml
|
||||
loader_module: mlflow.pytorch
|
||||
pickle_module_name: mlflow.pytorch.pickle_module
|
||||
python_version: 3.10.9
|
||||
pytorch:
|
||||
code: null
|
||||
model_data: data
|
||||
pytorch_version: 1.13.1
|
||||
mlflow_version: 1.30.0
|
||||
model_uuid: 2625ea164ff248c194686ed5afb9a510
|
||||
run_id: c293e8294f4f46adacd21465be08c608
|
||||
utc_time_created: '2023-02-22 01:11:28.646127'
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.10.9
|
||||
- pip<=23.0.1
|
||||
- pip:
|
||||
- mlflow
|
||||
- cloudpickle==2.2.1
|
||||
- torch==1.13.1
|
||||
- tqdm==4.64.1
|
||||
name: mlflow-env
|
||||
Binary file not shown.
|
|
@ -0,0 +1 @@
|
|||
mlflow.pytorch.pickle_module
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
python: 3.10.9
|
||||
build_dependencies:
|
||||
- pip==23.0.1
|
||||
- setuptools==67.3.2
|
||||
- wheel==0.38.4
|
||||
dependencies:
|
||||
- -r requirements.txt
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mlflow
|
||||
cloudpickle==2.2.1
|
||||
torch==1.13.1
|
||||
tqdm==4.64.1
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
# train.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD
|
||||
from tqdm import tqdm
|
||||
import mlflow
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
""" define a simple neural network model """
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.fc1 = nn.Linear(10, 5)
|
||||
self.fc2 = nn.Linear(5, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
def train(model, dataloader, criterion, optimizer, epochs):
|
||||
""" define the training function """
|
||||
for epoch in tqdm(range(epochs), 'Epochs'):
|
||||
|
||||
for i, (inputs, labels) in enumerate(dataloader):
|
||||
|
||||
# forwarding
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# update gradient
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# log loss
|
||||
mlflow.log_metric('train_loss', loss.item(), step=i)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set hyper parameters
|
||||
learning_rate = 1e-2
|
||||
epochs = 20
|
||||
|
||||
# create a dataloader with fake data
|
||||
dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
|
||||
dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
|
||||
|
||||
# create the model, criterion, and optimizer
|
||||
model = Net()
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = SGD(model.parameters(), lr=learning_rate)
|
||||
|
||||
# set the tracking URI to the model registry
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
mlflow.set_experiment('mlflow_testing')
|
||||
|
||||
# start the MLflow run
|
||||
with mlflow.start_run():
|
||||
|
||||
# train the model
|
||||
loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model')
|
||||
|
||||
# log training code
|
||||
mlflow.log_artifact('./train.py', 'code')
|
||||
|
||||
print('Completed.')
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
data: data
|
||||
env: conda.yaml
|
||||
loader_module: mlflow.pytorch
|
||||
pickle_module_name: mlflow.pytorch.pickle_module
|
||||
python_version: 3.10.9
|
||||
pytorch:
|
||||
code: null
|
||||
model_data: data
|
||||
pytorch_version: 1.13.1
|
||||
mlflow_version: 1.30.0
|
||||
model_uuid: 2ee49fdb3ec647a58b1235498b186722
|
||||
run_id: d548729629634031a93a46d6dab8b7da
|
||||
utc_time_created: '2023-02-22 01:11:33.149151'
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.10.9
|
||||
- pip<=23.0.1
|
||||
- pip:
|
||||
- mlflow
|
||||
- cloudpickle==2.2.1
|
||||
- torch==1.13.1
|
||||
- tqdm==4.64.1
|
||||
name: mlflow-env
|
||||
Binary file not shown.
|
|
@ -0,0 +1 @@
|
|||
mlflow.pytorch.pickle_module
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
python: 3.10.9
|
||||
build_dependencies:
|
||||
- pip==23.0.1
|
||||
- setuptools==67.3.2
|
||||
- wheel==0.38.4
|
||||
dependencies:
|
||||
- -r requirements.txt
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mlflow
|
||||
cloudpickle==2.2.1
|
||||
torch==1.13.1
|
||||
tqdm==4.64.1
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
# train.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD
|
||||
from tqdm import tqdm
|
||||
import mlflow
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
""" define a simple neural network model """
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.fc1 = nn.Linear(10, 5)
|
||||
self.fc2 = nn.Linear(5, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
def train(model, dataloader, criterion, optimizer, epochs):
|
||||
""" define the training function """
|
||||
for epoch in tqdm(range(epochs), 'Epochs'):
|
||||
|
||||
for i, (inputs, labels) in enumerate(dataloader):
|
||||
|
||||
# forwarding
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# update gradient
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# log loss
|
||||
mlflow.log_metric('train_loss', loss.item(), step=i)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set hyper parameters
|
||||
learning_rate = 1e-2
|
||||
epochs = 20
|
||||
|
||||
# create a dataloader with fake data
|
||||
dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
|
||||
dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
|
||||
|
||||
# create the model, criterion, and optimizer
|
||||
model = Net()
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = SGD(model.parameters(), lr=learning_rate)
|
||||
|
||||
# set the tracking URI to the model registry
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
mlflow.set_experiment('train_fortune_predict_model')
|
||||
|
||||
# start the MLflow run
|
||||
with mlflow.start_run():
|
||||
|
||||
# train the model
|
||||
loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model')
|
||||
|
||||
# log training code
|
||||
mlflow.log_artifact('./train.py', 'code')
|
||||
|
||||
print('Completed.')
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
data: data
|
||||
env: conda.yaml
|
||||
loader_module: mlflow.pytorch
|
||||
pickle_module_name: mlflow.pytorch.pickle_module
|
||||
python_version: 3.10.9
|
||||
pytorch:
|
||||
code: null
|
||||
model_data: data
|
||||
pytorch_version: 1.13.1
|
||||
mlflow_version: 1.30.0
|
||||
model_uuid: 75aa23c5bb33452c978feeeffcdcb393
|
||||
run_id: 0be79b1f3f7d480a9c7f497312887a37
|
||||
utc_time_created: '2023-02-22 01:12:26.682417'
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.10.9
|
||||
- pip<=23.0.1
|
||||
- pip:
|
||||
- mlflow
|
||||
- cloudpickle==2.2.1
|
||||
- torch==1.13.1
|
||||
- tqdm==4.64.1
|
||||
name: mlflow-env
|
||||
Binary file not shown.
|
|
@ -0,0 +1 @@
|
|||
mlflow.pytorch.pickle_module
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
python: 3.10.9
|
||||
build_dependencies:
|
||||
- pip==23.0.1
|
||||
- setuptools==67.3.2
|
||||
- wheel==0.38.4
|
||||
dependencies:
|
||||
- -r requirements.txt
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mlflow
|
||||
cloudpickle==2.2.1
|
||||
torch==1.13.1
|
||||
tqdm==4.64.1
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
# train.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD
|
||||
from tqdm import tqdm
|
||||
import mlflow
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
""" define a simple neural network model """
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.fc1 = nn.Linear(10, 5)
|
||||
self.fc2 = nn.Linear(5, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
def train(model, dataloader, criterion, optimizer, epochs):
|
||||
""" define the training function """
|
||||
for epoch in tqdm(range(epochs), 'Epochs'):
|
||||
|
||||
for i, (inputs, labels) in enumerate(dataloader):
|
||||
|
||||
# forwarding
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# update gradient
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# log loss
|
||||
mlflow.log_metric('train_loss', loss.item(), step=i)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set hyper parameters
|
||||
learning_rate = 1e-2
|
||||
epochs = 20
|
||||
|
||||
# create a dataloader with fake data
|
||||
dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
|
||||
dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
|
||||
|
||||
# create the model, criterion, and optimizer
|
||||
model = Net()
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = SGD(model.parameters(), lr=learning_rate)
|
||||
|
||||
# set the tracking URI to the model registry
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
mlflow.set_experiment('train_fortune_predict_model')
|
||||
|
||||
# start the MLflow run
|
||||
with mlflow.start_run():
|
||||
|
||||
# train the model
|
||||
loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model')
|
||||
|
||||
# log training code
|
||||
mlflow.log_artifact('./train.py', 'code')
|
||||
|
||||
print('Completed.')
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
data: data
|
||||
env: conda.yaml
|
||||
loader_module: mlflow.pytorch
|
||||
pickle_module_name: mlflow.pytorch.pickle_module
|
||||
python_version: 3.10.9
|
||||
pytorch:
|
||||
code: null
|
||||
model_data: data
|
||||
pytorch_version: 1.13.1
|
||||
mlflow_version: 1.30.0
|
||||
model_uuid: 20196b21b65b400eac57135de7cb2bac
|
||||
run_id: 2af28d45b569431ab10056469d63cf44
|
||||
utc_time_created: '2023-02-22 01:12:30.183766'
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.10.9
|
||||
- pip<=23.0.1
|
||||
- pip:
|
||||
- mlflow
|
||||
- cloudpickle==2.2.1
|
||||
- torch==1.13.1
|
||||
- tqdm==4.64.1
|
||||
name: mlflow-env
|
||||
Binary file not shown.
|
|
@ -0,0 +1 @@
|
|||
mlflow.pytorch.pickle_module
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
python: 3.10.9
|
||||
build_dependencies:
|
||||
- pip==23.0.1
|
||||
- setuptools==67.3.2
|
||||
- wheel==0.38.4
|
||||
dependencies:
|
||||
- -r requirements.txt
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mlflow
|
||||
cloudpickle==2.2.1
|
||||
torch==1.13.1
|
||||
tqdm==4.64.1
|
||||
|
|
@ -0,0 +1,98 @@
|
|||
# train.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD
|
||||
import mlflow
|
||||
from mlflow.models.signature import ModelSignature
|
||||
from mlflow.types.schema import Schema, ColSpec
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
""" define a simple neural network model """
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.fc1 = nn.Linear(5, 3)
|
||||
self.fc2 = nn.Linear(3, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
def train(model, dataloader, criterion, optimizer, epochs):
|
||||
""" define the training function """
|
||||
for epoch in tqdm(range(epochs), 'Epochs'):
|
||||
|
||||
for i, (inputs, labels) in enumerate(dataloader):
|
||||
|
||||
# forwarding
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# update gradient
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# log loss
|
||||
mlflow.log_metric('train_loss', loss.item(), step=epoch)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set hyper parameters
|
||||
learning_rate = 1e-2
|
||||
batch_size = 10
|
||||
epochs = 20
|
||||
|
||||
# create a dataloader with fake data
|
||||
dataloader = [(torch.randn(5), torch.randn(1)) for _ in range(100)]
|
||||
dataloader = torch.utils.data.DataLoader(dataloader, batch_size=batch_size)
|
||||
|
||||
# create the model, criterion, and optimizer
|
||||
model = Net()
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = SGD(model.parameters(), lr=learning_rate)
|
||||
|
||||
# set the tracking URI to the model registry
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
mlflow.set_experiment('train_fortune_predict_model')
|
||||
|
||||
# start a new MLflow run
|
||||
with mlflow.start_run():
|
||||
|
||||
# train the model
|
||||
loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
mlflow.log_param('batch_size', batch_size)
|
||||
|
||||
# create a signature to record model input and output
|
||||
input_schema = Schema([
|
||||
ColSpec('float', 'age'),
|
||||
ColSpec('float', 'mood level'),
|
||||
ColSpec('float', 'health level'),
|
||||
ColSpec('float', 'hungry level'),
|
||||
ColSpec('float', 'sexy level')
|
||||
])
|
||||
output_schema = Schema([ColSpec('float', 'fortune')])
|
||||
signature = ModelSignature(inputs=input_schema, outputs=output_schema)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model', signature=signature)
|
||||
|
||||
# log training code
|
||||
mlflow.log_artifact('./train.py', 'code')
|
||||
|
||||
print('Completed.')
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
data: data
|
||||
env: conda.yaml
|
||||
loader_module: mlflow.pytorch
|
||||
pickle_module_name: mlflow.pytorch.pickle_module
|
||||
python_version: 3.10.9
|
||||
pytorch:
|
||||
code: null
|
||||
model_data: data
|
||||
pytorch_version: 1.13.1
|
||||
mlflow_version: 1.30.0
|
||||
model_uuid: 4dc69e8ee0404f70b0c17f8119986704
|
||||
run_id: 581cf34366f74ee7b90b18f0a606ddf8
|
||||
signature:
|
||||
inputs: '[{"name": "age", "type": "float"}, {"name": "mood level", "type": "float"},
|
||||
{"name": "health level", "type": "float"}, {"name": "hungry level", "type": "float"},
|
||||
{"name": "sexy level", "type": "float"}]'
|
||||
outputs: '[{"name": "fortune", "type": "float"}]'
|
||||
utc_time_created: '2023-02-22 02:46:27.876394'
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.10.9
|
||||
- pip<=23.0.1
|
||||
- pip:
|
||||
- mlflow
|
||||
- cloudpickle==2.2.1
|
||||
- torch==1.13.1
|
||||
- tqdm==4.64.1
|
||||
name: mlflow-env
|
||||
Binary file not shown.
|
|
@ -0,0 +1 @@
|
|||
mlflow.pytorch.pickle_module
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
python: 3.10.9
|
||||
build_dependencies:
|
||||
- pip==23.0.1
|
||||
- setuptools==67.3.2
|
||||
- wheel==0.38.4
|
||||
dependencies:
|
||||
- -r requirements.txt
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mlflow
|
||||
cloudpickle==2.2.1
|
||||
torch==1.13.1
|
||||
tqdm==4.64.1
|
||||
|
|
@ -0,0 +1,98 @@
|
|||
# train.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD
|
||||
import mlflow
|
||||
from mlflow.models.signature import ModelSignature
|
||||
from mlflow.types.schema import Schema, ColSpec
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
""" define a simple neural network model """
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.fc1 = nn.Linear(5, 3)
|
||||
self.fc2 = nn.Linear(3, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
def train(model, dataloader, criterion, optimizer, epochs):
|
||||
""" define the training function """
|
||||
for epoch in tqdm(range(epochs), 'Epochs'):
|
||||
|
||||
for i, (inputs, labels) in enumerate(dataloader):
|
||||
|
||||
# forwarding
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# update gradient
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# log loss
|
||||
mlflow.log_metric('train_loss', loss.item(), step=epoch)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set hyper parameters
|
||||
learning_rate = 1e-2
|
||||
batch_size = 10
|
||||
epochs = 20
|
||||
|
||||
# create a dataloader with fake data
|
||||
dataloader = [(torch.randn(5), torch.randn(1)) for _ in range(100)]
|
||||
dataloader = torch.utils.data.DataLoader(dataloader, batch_size=batch_size)
|
||||
|
||||
# create the model, criterion, and optimizer
|
||||
model = Net()
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = SGD(model.parameters(), lr=learning_rate)
|
||||
|
||||
# set the tracking URI to the model registry
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
mlflow.set_experiment('train_fortune_predict_model')
|
||||
|
||||
# start a new MLflow run
|
||||
with mlflow.start_run():
|
||||
|
||||
# train the model
|
||||
loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
mlflow.log_param('batch_size', batch_size)
|
||||
|
||||
# create a signature to record model input and output
|
||||
input_schema = Schema([
|
||||
ColSpec('float', 'age'),
|
||||
ColSpec('float', 'mood level'),
|
||||
ColSpec('float', 'health level'),
|
||||
ColSpec('float', 'hungry level'),
|
||||
ColSpec('float', 'sexy level')
|
||||
])
|
||||
output_schema = Schema([ColSpec('float', 'fortune')])
|
||||
signature = ModelSignature(inputs=input_schema, outputs=output_schema)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model', signature=signature)
|
||||
|
||||
# log training code
|
||||
mlflow.log_artifact('./train.py', 'code')
|
||||
|
||||
print('Completed.')
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
data: data
|
||||
env: conda.yaml
|
||||
loader_module: mlflow.pytorch
|
||||
pickle_module_name: mlflow.pytorch.pickle_module
|
||||
python_version: 3.10.9
|
||||
pytorch:
|
||||
code: null
|
||||
model_data: data
|
||||
pytorch_version: 1.13.1
|
||||
mlflow_version: 1.30.0
|
||||
model_uuid: 1334c03425a6454fafce1db8fa3a2428
|
||||
run_id: 5c8b249efc724568aca1b8a4d8750b43
|
||||
signature:
|
||||
inputs: '[{"name": "age", "type": "float"}, {"name": "mood level", "type": "float"},
|
||||
{"name": "health level", "type": "float"}, {"name": "hungry level", "type": "float"},
|
||||
{"name": "sexy level", "type": "float"}]'
|
||||
outputs: '[{"name": "fortune", "type": "float"}]'
|
||||
utc_time_created: '2023-02-22 02:46:24.939018'
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.10.9
|
||||
- pip<=23.0.1
|
||||
- pip:
|
||||
- mlflow
|
||||
- cloudpickle==2.2.1
|
||||
- torch==1.13.1
|
||||
- tqdm==4.64.1
|
||||
name: mlflow-env
|
||||
Binary file not shown.
|
|
@ -0,0 +1 @@
|
|||
mlflow.pytorch.pickle_module
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
python: 3.10.9
|
||||
build_dependencies:
|
||||
- pip==23.0.1
|
||||
- setuptools==67.3.2
|
||||
- wheel==0.38.4
|
||||
dependencies:
|
||||
- -r requirements.txt
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mlflow
|
||||
cloudpickle==2.2.1
|
||||
torch==1.13.1
|
||||
tqdm==4.64.1
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
# train.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD
|
||||
import mlflow
|
||||
from mlflow.models.signature import ModelSignature
|
||||
from mlflow.types.schema import Schema, ColSpec
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
""" define a simple neural network model """
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.fc1 = nn.Linear(5, 3)
|
||||
self.fc2 = nn.Linear(3, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
def train(model, dataloader, criterion, optimizer, epochs):
|
||||
""" define the training function """
|
||||
for epoch in tqdm(range(epochs), 'Epochs'):
|
||||
|
||||
for i, (inputs, labels) in enumerate(dataloader):
|
||||
|
||||
# forwarding
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# update gradient
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# log loss
|
||||
mlflow.log_metric('train_loss', loss.item(), step=epoch)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set hyper parameters
|
||||
learning_rate = 1e-2
|
||||
epochs = 20
|
||||
|
||||
# create a dataloader with fake data
|
||||
dataloader = [(torch.randn(5), torch.randn(1)) for _ in range(100)]
|
||||
dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
|
||||
|
||||
# create the model, criterion, and optimizer
|
||||
model = Net()
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = SGD(model.parameters(), lr=learning_rate)
|
||||
|
||||
# set the tracking URI to the model registry
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
mlflow.set_experiment('train_fortune_predict_model')
|
||||
|
||||
# start a new MLflow run
|
||||
with mlflow.start_run():
|
||||
|
||||
# train the model
|
||||
loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
|
||||
# create a signature to record model input and output
|
||||
input_schema = Schema([
|
||||
ColSpec('float', 'age'),
|
||||
ColSpec('float', 'mood level'),
|
||||
ColSpec('float', 'health level'),
|
||||
ColSpec('float', 'hungry level'),
|
||||
ColSpec('float', 'sexy level')
|
||||
])
|
||||
output_schema = Schema([ColSpec('float', 'fortune')])
|
||||
signature = ModelSignature(inputs=input_schema, outputs=output_schema)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model', signature=signature)
|
||||
|
||||
# log training code
|
||||
mlflow.log_artifact('./train.py', 'code')
|
||||
|
||||
print('Completed.')
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
data: data
|
||||
env: conda.yaml
|
||||
loader_module: mlflow.pytorch
|
||||
pickle_module_name: mlflow.pytorch.pickle_module
|
||||
python_version: 3.10.9
|
||||
pytorch:
|
||||
code: null
|
||||
model_data: data
|
||||
pytorch_version: 1.13.1
|
||||
mlflow_version: 1.30.0
|
||||
model_uuid: 3f51fe9a1b40461d8c445f37695d72e2
|
||||
run_id: 7f0b13a30b10483589f7ca922050e721
|
||||
signature:
|
||||
inputs: '[{"name": "age", "type": "float"}, {"name": "mood level", "type": "float"},
|
||||
{"name": "health level", "type": "float"}, {"name": "hungry level", "type": "float"},
|
||||
{"name": "sexy level", "type": "float"}]'
|
||||
outputs: '[{"name": "fortune", "type": "float"}]'
|
||||
utc_time_created: '2023-02-22 02:43:39.269378'
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.10.9
|
||||
- pip<=23.0.1
|
||||
- pip:
|
||||
- mlflow
|
||||
- cloudpickle==2.2.1
|
||||
- torch==1.13.1
|
||||
- tqdm==4.64.1
|
||||
name: mlflow-env
|
||||
Binary file not shown.
|
|
@ -0,0 +1 @@
|
|||
mlflow.pytorch.pickle_module
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
python: 3.10.9
|
||||
build_dependencies:
|
||||
- pip==23.0.1
|
||||
- setuptools==67.3.2
|
||||
- wheel==0.38.4
|
||||
dependencies:
|
||||
- -r requirements.txt
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mlflow
|
||||
cloudpickle==2.2.1
|
||||
torch==1.13.1
|
||||
tqdm==4.64.1
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
# train.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD
|
||||
from tqdm import tqdm
|
||||
import mlflow
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
""" define a simple neural network model """
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.fc1 = nn.Linear(10, 5)
|
||||
self.fc2 = nn.Linear(5, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
def train(model, dataloader, criterion, optimizer, epochs):
|
||||
""" define the training function """
|
||||
for epoch in tqdm(range(epochs), 'Epochs'):
|
||||
|
||||
for i, (inputs, labels) in enumerate(dataloader):
|
||||
|
||||
# forwarding
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# update gradient
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# log loss
|
||||
mlflow.log_metric('train_loss', loss.item(), step=i)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set hyper parameters
|
||||
learning_rate = 1e-2
|
||||
epochs = 20
|
||||
|
||||
# create a dataloader with fake data
|
||||
dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
|
||||
dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
|
||||
|
||||
# create the model, criterion, and optimizer
|
||||
model = Net()
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = SGD(model.parameters(), lr=learning_rate)
|
||||
|
||||
# set the tracking URI to the model registry
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
mlflow.set_experiment('train_fortune_predict_model')
|
||||
|
||||
# start the MLflow run
|
||||
with mlflow.start_run():
|
||||
|
||||
# train the model
|
||||
loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model')
|
||||
|
||||
# log training code
|
||||
mlflow.log_artifact('./train.py', 'code')
|
||||
|
||||
print('Completed.')
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
data: data
|
||||
env: conda.yaml
|
||||
loader_module: mlflow.pytorch
|
||||
pickle_module_name: mlflow.pytorch.pickle_module
|
||||
python_version: 3.10.9
|
||||
pytorch:
|
||||
code: null
|
||||
model_data: data
|
||||
pytorch_version: 1.13.1
|
||||
mlflow_version: 1.30.0
|
||||
model_uuid: a94b93c2d2674c97a5db43fda608ec76
|
||||
run_id: 80447ac20c814d10a470208e303ea0ec
|
||||
utc_time_created: '2023-02-22 01:12:22.906728'
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.10.9
|
||||
- pip<=23.0.1
|
||||
- pip:
|
||||
- mlflow
|
||||
- cloudpickle==2.2.1
|
||||
- torch==1.13.1
|
||||
- tqdm==4.64.1
|
||||
name: mlflow-env
|
||||
Binary file not shown.
|
|
@ -0,0 +1 @@
|
|||
mlflow.pytorch.pickle_module
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
python: 3.10.9
|
||||
build_dependencies:
|
||||
- pip==23.0.1
|
||||
- setuptools==67.3.2
|
||||
- wheel==0.38.4
|
||||
dependencies:
|
||||
- -r requirements.txt
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mlflow
|
||||
cloudpickle==2.2.1
|
||||
torch==1.13.1
|
||||
tqdm==4.64.1
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
# train.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD
|
||||
import mlflow
|
||||
from mlflow.models.signature import ModelSignature
|
||||
from mlflow.types.schema import Schema, ColSpec
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
""" define a simple neural network model """
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.fc1 = nn.Linear(5, 3)
|
||||
self.fc2 = nn.Linear(3, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
def train(model, dataloader, criterion, optimizer, epochs):
|
||||
""" define the training function """
|
||||
for epoch in tqdm(range(epochs), 'Epochs'):
|
||||
|
||||
for i, (inputs, labels) in enumerate(dataloader):
|
||||
|
||||
# forwarding
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# update gradient
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# log loss
|
||||
mlflow.log_metric('train_loss', loss.item(), step=epoch)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set hyper parameters
|
||||
learning_rate = 1e-4
|
||||
epochs = 20
|
||||
|
||||
# create a dataloader with fake data
|
||||
dataloader = [(torch.randn(5), torch.randn(1)) for _ in range(100)]
|
||||
dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
|
||||
|
||||
# create the model, criterion, and optimizer
|
||||
model = Net()
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = SGD(model.parameters(), lr=learning_rate)
|
||||
|
||||
# set the tracking URI to the model registry
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
mlflow.set_experiment('train_fortune_predict_model')
|
||||
|
||||
# start a new MLflow run
|
||||
with mlflow.start_run():
|
||||
|
||||
# train the model
|
||||
loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
|
||||
# create a signature to record model input and output
|
||||
input_schema = Schema([
|
||||
ColSpec('float', 'age'),
|
||||
ColSpec('float', 'mood level'),
|
||||
ColSpec('float', 'health level'),
|
||||
ColSpec('float', 'hungry level'),
|
||||
ColSpec('float', 'sexy level')
|
||||
])
|
||||
output_schema = Schema([ColSpec('float', 'fortune')])
|
||||
signature = ModelSignature(inputs=input_schema, outputs=output_schema)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model', signature=signature)
|
||||
|
||||
# log training code
|
||||
mlflow.log_artifact('./train.py', 'code')
|
||||
|
||||
print('Completed.')
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
data: data
|
||||
env: conda.yaml
|
||||
loader_module: mlflow.pytorch
|
||||
pickle_module_name: mlflow.pytorch.pickle_module
|
||||
python_version: 3.10.9
|
||||
pytorch:
|
||||
code: null
|
||||
model_data: data
|
||||
pytorch_version: 1.13.1
|
||||
mlflow_version: 1.30.0
|
||||
model_uuid: 461190610cac47f5970e3386972070da
|
||||
run_id: 936ba15a1bf74591a10e1711133f4be4
|
||||
signature:
|
||||
inputs: '[{"name": "age", "type": "float"}, {"name": "mood level", "type": "float"},
|
||||
{"name": "health level", "type": "float"}, {"name": "hungry level", "type": "float"},
|
||||
{"name": "sexy level", "type": "float"}]'
|
||||
outputs: '[{"name": "fortune", "type": "float"}]'
|
||||
utc_time_created: '2023-02-22 02:42:59.306161'
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.10.9
|
||||
- pip<=23.0.1
|
||||
- pip:
|
||||
- mlflow
|
||||
- cloudpickle==2.2.1
|
||||
- torch==1.13.1
|
||||
- tqdm==4.64.1
|
||||
name: mlflow-env
|
||||
Binary file not shown.
|
|
@ -0,0 +1 @@
|
|||
mlflow.pytorch.pickle_module
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
python: 3.10.9
|
||||
build_dependencies:
|
||||
- pip==23.0.1
|
||||
- setuptools==67.3.2
|
||||
- wheel==0.38.4
|
||||
dependencies:
|
||||
- -r requirements.txt
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mlflow
|
||||
cloudpickle==2.2.1
|
||||
torch==1.13.1
|
||||
tqdm==4.64.1
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
# train.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD
|
||||
import mlflow
|
||||
from mlflow.models.signature import ModelSignature
|
||||
from mlflow.types.schema import Schema, ColSpec
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
""" define a simple neural network model """
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.fc1 = nn.Linear(5, 3)
|
||||
self.fc2 = nn.Linear(3, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
def train(model, dataloader, criterion, optimizer, epochs):
|
||||
""" define the training function """
|
||||
for epoch in tqdm(range(epochs), 'Epochs'):
|
||||
|
||||
for i, (inputs, labels) in enumerate(dataloader):
|
||||
|
||||
# forwarding
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# update gradient
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# log loss
|
||||
mlflow.log_metric('train_loss', loss.item(), step=i)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set hyper parameters
|
||||
learning_rate = 1e-2
|
||||
epochs = 20
|
||||
|
||||
# create a dataloader with fake data
|
||||
dataloader = [(torch.randn(5), torch.randn(1)) for _ in range(100)]
|
||||
dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
|
||||
|
||||
# create the model, criterion, and optimizer
|
||||
model = Net()
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = SGD(model.parameters(), lr=learning_rate)
|
||||
|
||||
# set the tracking URI to the model registry
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
mlflow.set_experiment('train_fortune_predict_model')
|
||||
|
||||
# start a new MLflow run
|
||||
with mlflow.start_run():
|
||||
|
||||
# train the model
|
||||
loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
|
||||
# create a signature to record model input and output
|
||||
input_schema = Schema([
|
||||
ColSpec('float', 'age'),
|
||||
ColSpec('float', 'mood level'),
|
||||
ColSpec('float', 'health level'),
|
||||
ColSpec('float', 'hungry level'),
|
||||
ColSpec('float', 'sexy level')
|
||||
])
|
||||
output_schema = Schema([ColSpec('float', 'fortune')])
|
||||
signature = ModelSignature(inputs=input_schema, outputs=output_schema)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model', signature=signature)
|
||||
|
||||
# log training code
|
||||
mlflow.log_artifact('./train.py', 'code')
|
||||
|
||||
print('Completed.')
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
data: data
|
||||
env: conda.yaml
|
||||
loader_module: mlflow.pytorch
|
||||
pickle_module_name: mlflow.pytorch.pickle_module
|
||||
python_version: 3.10.9
|
||||
pytorch:
|
||||
code: null
|
||||
model_data: data
|
||||
pytorch_version: 1.13.1
|
||||
mlflow_version: 1.30.0
|
||||
model_uuid: eb6ac492a8a5426d815dce50de4d2545
|
||||
run_id: 9ea595f0043a477bb635754b7f77fa48
|
||||
signature:
|
||||
inputs: '[{"name": "age", "type": "float"}, {"name": "mood level", "type": "float"},
|
||||
{"name": "health level", "type": "float"}, {"name": "hungry level", "type": "float"},
|
||||
{"name": "sexy level", "type": "float"}]'
|
||||
outputs: '[{"name": "fortune", "type": "float"}]'
|
||||
utc_time_created: '2023-02-22 02:26:27.043234'
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.10.9
|
||||
- pip<=23.0.1
|
||||
- pip:
|
||||
- mlflow
|
||||
- cloudpickle==2.2.1
|
||||
- torch==1.13.1
|
||||
- tqdm==4.64.1
|
||||
name: mlflow-env
|
||||
Binary file not shown.
|
|
@ -0,0 +1 @@
|
|||
mlflow.pytorch.pickle_module
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
python: 3.10.9
|
||||
build_dependencies:
|
||||
- pip==23.0.1
|
||||
- setuptools==67.3.2
|
||||
- wheel==0.38.4
|
||||
dependencies:
|
||||
- -r requirements.txt
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mlflow
|
||||
cloudpickle==2.2.1
|
||||
torch==1.13.1
|
||||
tqdm==4.64.1
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
# train.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.optim import SGD
|
||||
import mlflow
|
||||
from mlflow.models.signature import ModelSignature
|
||||
from mlflow.types.schema import Schema, ColSpec
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
""" define a simple neural network model """
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.fc1 = nn.Linear(5, 3)
|
||||
self.fc2 = nn.Linear(3, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = torch.relu(x)
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
def train(model, dataloader, criterion, optimizer, epochs):
|
||||
""" define the training function """
|
||||
for epoch in tqdm(range(epochs), 'Epochs'):
|
||||
|
||||
for i, (inputs, labels) in enumerate(dataloader):
|
||||
|
||||
# forwarding
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
|
||||
# update gradient
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# log loss
|
||||
mlflow.log_metric('train_loss', loss.item(), step=i)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set hyper parameters
|
||||
learning_rate = 1e-2
|
||||
epochs = 20
|
||||
|
||||
# create a dataloader with fake data
|
||||
dataloader = [(torch.randn(5), torch.randn(1)) for _ in range(100)]
|
||||
dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
|
||||
|
||||
# create the model, criterion, and optimizer
|
||||
model = Net()
|
||||
criterion = nn.MSELoss()
|
||||
optimizer = SGD(model.parameters(), lr=learning_rate)
|
||||
|
||||
# set the tracking URI to the model registry
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
mlflow.set_experiment('train_fortune_predict_model')
|
||||
|
||||
# start a new MLflow run
|
||||
with mlflow.start_run():
|
||||
|
||||
# train the model
|
||||
loss = train(model, dataloader, criterion, optimizer, epochs)
|
||||
|
||||
# log some additional metrics
|
||||
mlflow.log_metric('final_loss', loss.item())
|
||||
mlflow.log_param('learning_rate', learning_rate)
|
||||
|
||||
# create a signature to record model input and output
|
||||
input_schema = Schema([
|
||||
ColSpec('float', 'age'),
|
||||
ColSpec('float', 'mood level'),
|
||||
ColSpec('float', 'health level'),
|
||||
ColSpec('float', 'hungry level'),
|
||||
ColSpec('float', 'sexy level')
|
||||
])
|
||||
output_schema = Schema([ColSpec('float', 'fortune')])
|
||||
signature = ModelSignature(inputs=input_schema, outputs=output_schema)
|
||||
|
||||
# log trained model
|
||||
mlflow.pytorch.log_model(model, 'model', signature=signature)
|
||||
|
||||
# log training code
|
||||
mlflow.log_artifact('./train.py', 'code')
|
||||
|
||||
print('Completed.')
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
artifact_path: model
|
||||
flavors:
|
||||
python_function:
|
||||
data: data
|
||||
env: conda.yaml
|
||||
loader_module: mlflow.pytorch
|
||||
pickle_module_name: mlflow.pytorch.pickle_module
|
||||
python_version: 3.10.9
|
||||
pytorch:
|
||||
code: null
|
||||
model_data: data
|
||||
pytorch_version: 1.13.1
|
||||
mlflow_version: 1.30.0
|
||||
model_uuid: d240fe9348c24d1f871506e8d1155f56
|
||||
run_id: ab28ca803756454881d83e7e85945df0
|
||||
signature:
|
||||
inputs: '[{"name": "age", "type": "float"}, {"name": "mood level", "type": "float"},
|
||||
{"name": "health level", "type": "float"}, {"name": "hungry level", "type": "float"},
|
||||
{"name": "sexy level", "type": "float"}]'
|
||||
outputs: '[{"name": "fortune", "type": "float"}]'
|
||||
utc_time_created: '2023-02-22 02:26:30.657849'
|
||||
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Reference in New Issue