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deng
2023-02-22 16:26:02 +08:00
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82
artifacts/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/code/train.py Normal file
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# 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')
# start the MLflow run
with mlflow.start_run():
# train the model and log the loss
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.')

16
artifacts/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/model/MLmodel Normal file
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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: 18b69aa38c064c579c9b465d7a826081
run_id: 410d85525e5f4cfe9839a432d35f9ad2
utc_time_created: '2023-02-22 00:42:48.668457'

11
artifacts/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/model/conda.yaml Normal file
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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

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artifacts/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/model/data/pickle_module_info.txt Normal file
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mlflow.pytorch.pickle_module

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artifacts/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/model/python_env.yaml Normal file
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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

4
artifacts/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/model/requirements.txt Normal file
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mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

16
artifacts/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/model/MLmodel Normal file
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artifact_path: model
flavors:
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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: 957e2f6e4fd048c99aee3150c73c4078
run_id: c4fd84a025e1474d87cdc2919874b88c
utc_time_created: '2023-02-22 00:41:33.282088'

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artifacts/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/model/conda.yaml Normal file
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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

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artifacts/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/model/data/pickle_module_info.txt Normal file
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mlflow.pytorch.pickle_module

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artifacts/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/model/python_env.yaml Normal file
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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

4
artifacts/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/model/requirements.txt Normal file
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@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

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artifacts/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/train.py Normal file
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@ -0,0 +1,82 @@
# 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')
# start the MLflow run
with mlflow.start_run():
# train the model and log the loss
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')
print('Completed.')

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artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/code/train.py Normal file
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# 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.')

16
artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/model/MLmodel Normal file
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@ -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: ff8b845d6a174ffabfc49a18673c6c04
run_id: c248a4299f97423987a9496a2241ab1a
utc_time_created: '2023-02-22 01:10:55.971443'

11
artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/model/conda.yaml Normal file
View File

@ -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

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artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/model/data/model.pth Normal file
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artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/model/data/pickle_module_info.txt Normal file
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mlflow.pytorch.pickle_module

7
artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/model/python_env.yaml Normal file
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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

4
artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/model/requirements.txt Normal file
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@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

83
artifacts/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/code/train.py Normal file
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@ -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.')

16
artifacts/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/model/MLmodel Normal file
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@ -0,0 +1,16 @@
artifact_path: model
flavors:
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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: 27a96ad04f5a4578a3e1302500ad9a11
run_id: b7d7395b6b53404497f7656b07b71bf8
utc_time_created: '2023-02-22 01:11:36.809812'

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artifacts/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/model/conda.yaml Normal file
View File

@ -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

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artifacts/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/model/data/pickle_module_info.txt Normal file
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mlflow.pytorch.pickle_module

7
artifacts/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/model/python_env.yaml Normal file
View File

@ -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

4
artifacts/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/model/requirements.txt Normal file
View File

@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

83
artifacts/2/c293e8294f4f46adacd21465be08c608/artifacts/code/train.py Normal file
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@ -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.')

16
artifacts/2/c293e8294f4f46adacd21465be08c608/artifacts/model/MLmodel Normal file
View File

@ -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'

11
artifacts/2/c293e8294f4f46adacd21465be08c608/artifacts/model/conda.yaml Normal file
View File

@ -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

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artifacts/2/c293e8294f4f46adacd21465be08c608/artifacts/model/data/pickle_module_info.txt Normal file
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mlflow.pytorch.pickle_module

7
artifacts/2/c293e8294f4f46adacd21465be08c608/artifacts/model/python_env.yaml Normal file
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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

4
artifacts/2/c293e8294f4f46adacd21465be08c608/artifacts/model/requirements.txt Normal file
View File

@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

83
artifacts/2/d548729629634031a93a46d6dab8b7da/artifacts/code/train.py Normal file
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@ -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.')

16
artifacts/2/d548729629634031a93a46d6dab8b7da/artifacts/model/MLmodel Normal file
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@ -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'

11
artifacts/2/d548729629634031a93a46d6dab8b7da/artifacts/model/conda.yaml Normal file
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@ -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

BIN
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1
artifacts/2/d548729629634031a93a46d6dab8b7da/artifacts/model/data/pickle_module_info.txt Normal file
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mlflow.pytorch.pickle_module

7
artifacts/2/d548729629634031a93a46d6dab8b7da/artifacts/model/python_env.yaml Normal file
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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

4
artifacts/2/d548729629634031a93a46d6dab8b7da/artifacts/model/requirements.txt Normal file
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@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

83
artifacts/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/code/train.py Normal file
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@ -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.')

16
artifacts/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/model/MLmodel Normal file
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@ -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'

11
artifacts/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/model/conda.yaml Normal file
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@ -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

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1
artifacts/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/model/data/pickle_module_info.txt Normal file
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mlflow.pytorch.pickle_module

7
artifacts/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/model/python_env.yaml Normal file
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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

4
artifacts/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/model/requirements.txt Normal file
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@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

83
artifacts/3/2af28d45b569431ab10056469d63cf44/artifacts/code/train.py Normal file
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@ -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.')

16
artifacts/3/2af28d45b569431ab10056469d63cf44/artifacts/model/MLmodel Normal file
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@ -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'

11
artifacts/3/2af28d45b569431ab10056469d63cf44/artifacts/model/conda.yaml Normal file
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@ -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

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1
artifacts/3/2af28d45b569431ab10056469d63cf44/artifacts/model/data/pickle_module_info.txt Normal file
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@ -0,0 +1 @@
mlflow.pytorch.pickle_module

7
artifacts/3/2af28d45b569431ab10056469d63cf44/artifacts/model/python_env.yaml Normal file
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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

4
artifacts/3/2af28d45b569431ab10056469d63cf44/artifacts/model/requirements.txt Normal file
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@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

98
artifacts/3/581cf34366f74ee7b90b18f0a606ddf8/artifacts/code/train.py Normal file
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@ -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.')

21
artifacts/3/581cf34366f74ee7b90b18f0a606ddf8/artifacts/model/MLmodel Normal file
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@ -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'

11
artifacts/3/581cf34366f74ee7b90b18f0a606ddf8/artifacts/model/conda.yaml Normal file
View File

@ -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

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1
artifacts/3/581cf34366f74ee7b90b18f0a606ddf8/artifacts/model/data/pickle_module_info.txt Normal file
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@ -0,0 +1 @@
mlflow.pytorch.pickle_module

7
artifacts/3/581cf34366f74ee7b90b18f0a606ddf8/artifacts/model/python_env.yaml Normal file
View File

@ -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

4
artifacts/3/581cf34366f74ee7b90b18f0a606ddf8/artifacts/model/requirements.txt Normal file
View File

@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

98
artifacts/3/5c8b249efc724568aca1b8a4d8750b43/artifacts/code/train.py Normal file
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@ -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.')

21
artifacts/3/5c8b249efc724568aca1b8a4d8750b43/artifacts/model/MLmodel Normal file
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@ -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'

11
artifacts/3/5c8b249efc724568aca1b8a4d8750b43/artifacts/model/conda.yaml Normal file
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@ -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

BIN
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1
artifacts/3/5c8b249efc724568aca1b8a4d8750b43/artifacts/model/data/pickle_module_info.txt Normal file
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@ -0,0 +1 @@
mlflow.pytorch.pickle_module

7
artifacts/3/5c8b249efc724568aca1b8a4d8750b43/artifacts/model/python_env.yaml Normal file
View File

@ -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

4
artifacts/3/5c8b249efc724568aca1b8a4d8750b43/artifacts/model/requirements.txt Normal file
View File

@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

96
artifacts/3/7f0b13a30b10483589f7ca922050e721/artifacts/code/train.py Normal file
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@ -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.')

21
artifacts/3/7f0b13a30b10483589f7ca922050e721/artifacts/model/MLmodel Normal file
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@ -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'

11
artifacts/3/7f0b13a30b10483589f7ca922050e721/artifacts/model/conda.yaml Normal file
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@ -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

BIN
artifacts/3/7f0b13a30b10483589f7ca922050e721/artifacts/model/data/model.pth Normal file
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1
artifacts/3/7f0b13a30b10483589f7ca922050e721/artifacts/model/data/pickle_module_info.txt Normal file
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@ -0,0 +1 @@
mlflow.pytorch.pickle_module

7
artifacts/3/7f0b13a30b10483589f7ca922050e721/artifacts/model/python_env.yaml Normal file
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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

4
artifacts/3/7f0b13a30b10483589f7ca922050e721/artifacts/model/requirements.txt Normal file
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@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

83
artifacts/3/80447ac20c814d10a470208e303ea0ec/artifacts/code/train.py Normal file
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@ -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.')

16
artifacts/3/80447ac20c814d10a470208e303ea0ec/artifacts/model/MLmodel Normal file
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@ -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'

11
artifacts/3/80447ac20c814d10a470208e303ea0ec/artifacts/model/conda.yaml Normal file
View File

@ -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

BIN
artifacts/3/80447ac20c814d10a470208e303ea0ec/artifacts/model/data/model.pth Normal file
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1
artifacts/3/80447ac20c814d10a470208e303ea0ec/artifacts/model/data/pickle_module_info.txt Normal file
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@ -0,0 +1 @@
mlflow.pytorch.pickle_module

7
artifacts/3/80447ac20c814d10a470208e303ea0ec/artifacts/model/python_env.yaml Normal file
View File

@ -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

4
artifacts/3/80447ac20c814d10a470208e303ea0ec/artifacts/model/requirements.txt Normal file
View File

@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

96
artifacts/3/936ba15a1bf74591a10e1711133f4be4/artifacts/code/train.py Normal file
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@ -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.')

21
artifacts/3/936ba15a1bf74591a10e1711133f4be4/artifacts/model/MLmodel Normal file
View File

@ -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'

11
artifacts/3/936ba15a1bf74591a10e1711133f4be4/artifacts/model/conda.yaml Normal file
View File

@ -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

BIN
artifacts/3/936ba15a1bf74591a10e1711133f4be4/artifacts/model/data/model.pth Normal file
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Binary file not shown.

1
artifacts/3/936ba15a1bf74591a10e1711133f4be4/artifacts/model/data/pickle_module_info.txt Normal file
View File

@ -0,0 +1 @@
mlflow.pytorch.pickle_module

7
artifacts/3/936ba15a1bf74591a10e1711133f4be4/artifacts/model/python_env.yaml Normal file
View File

@ -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

4
artifacts/3/936ba15a1bf74591a10e1711133f4be4/artifacts/model/requirements.txt Normal file
View File

@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

96
artifacts/3/9ea595f0043a477bb635754b7f77fa48/artifacts/code/train.py Normal file
View File

@ -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.')

21
artifacts/3/9ea595f0043a477bb635754b7f77fa48/artifacts/model/MLmodel Normal file
View File

@ -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'

11
artifacts/3/9ea595f0043a477bb635754b7f77fa48/artifacts/model/conda.yaml Normal file
View File

@ -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

BIN
artifacts/3/9ea595f0043a477bb635754b7f77fa48/artifacts/model/data/model.pth Normal file
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1
artifacts/3/9ea595f0043a477bb635754b7f77fa48/artifacts/model/data/pickle_module_info.txt Normal file
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@ -0,0 +1 @@
mlflow.pytorch.pickle_module

7
artifacts/3/9ea595f0043a477bb635754b7f77fa48/artifacts/model/python_env.yaml Normal file
View File

@ -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

4
artifacts/3/9ea595f0043a477bb635754b7f77fa48/artifacts/model/requirements.txt Normal file
View File

@ -0,0 +1,4 @@
mlflow
cloudpickle==2.2.1
torch==1.13.1
tqdm==4.64.1

96
artifacts/3/ab28ca803756454881d83e7e85945df0/artifacts/code/train.py Normal file
View File

@ -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.')

21
artifacts/3/ab28ca803756454881d83e7e85945df0/artifacts/model/MLmodel Normal file
View File

@ -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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