init
This commit is contained in:
許登傑
commit
e1c2b82109
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artifact_path: model
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flavors:
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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: 2382b7a39c064e7b9b1465cfd84140a3
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run_id: 24469fc083d6470a9cad7f17a6eeeea0
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utc_time_created: '2023-02-21 05:57:41.973454'
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@ -0,0 +1,11 @@
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|
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mlflow.pytorch.pickle_module
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python: 3.10.9
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build_dependencies:
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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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@ -0,0 +1,16 @@
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pytorch:
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model_data: data
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@ -0,0 +1,10 @@
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channels:
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dependencies:
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|
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mlflow.pytorch.pickle_module
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python: 3.10.9
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build_dependencies:
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dependencies:
|
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|
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@ -0,0 +1,3 @@
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mlflow
|
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@ -0,0 +1,16 @@
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pytorch:
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code: null
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model_data: data
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@ -0,0 +1,10 @@
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channels:
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|
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|
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mlflow.pytorch.pickle_module
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|
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@ -0,0 +1,16 @@
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|
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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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utc_time_created: '2023-02-21 05:37:55.904472'
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@ -0,0 +1,10 @@
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channels:
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|
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dependencies:
|
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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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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
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|
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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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|
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|
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|
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|
||||
|
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|
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|
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|
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|
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|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|||
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||||
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|
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|
||||
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|
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|
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|
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||||
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|
||||
|
|
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|
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|
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|
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|
||||
- pytz=2022.7.1
|
||||
- pywin32-on-windows=0.1.0
|
||||
- pyyaml=6.0
|
||||
- querystring_parser=1.2.4
|
||||
- readline=8.1.2
|
||||
- requests=2.28.2
|
||||
- scipy=1.10.0
|
||||
- setuptools=67.3.2
|
||||
- six=1.16.0
|
||||
- smmap=3.0.5
|
||||
- sqlalchemy=1.4.46
|
||||
- sqlparse=0.4.3
|
||||
- svt-av1=1.4.1
|
||||
- tabulate=0.9.0
|
||||
- tk=8.6.12
|
||||
- torchaudio=0.13.1
|
||||
- torchvision=0.14.1
|
||||
- tqdm=4.64.1
|
||||
- typing_extensions=4.4.0
|
||||
- tzdata=2022g
|
||||
- urllib3=1.26.14
|
||||
- websocket-client=1.5.1
|
||||
- werkzeug=2.2.3
|
||||
- wheel=0.38.4
|
||||
- x264=1!164.3095
|
||||
- x265=3.5
|
||||
- xorg-libxau=1.0.9
|
||||
- xorg-libxdmcp=1.1.3
|
||||
- xz=5.2.6
|
||||
- yaml=0.2.5
|
||||
- zipp=3.14.0
|
||||
- zstd=1.5.2
|
||||
prefix: /Users/xiao_deng/miniforge3/envs/torch
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
# predict.py
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
import torch
|
||||
import mlflow
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# set MLflow server
|
||||
mlflow.set_tracking_uri('http://127.0.0.1:5000')
|
||||
|
||||
# load production model
|
||||
model = mlflow.pytorch.load_model('models:/cls_model/production')
|
||||
|
||||
# predict
|
||||
fake_data = torch.randn(10)
|
||||
output = model(fake_data)
|
||||
print(output)
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
#!/bin/bash
|
||||
# start_mlflow_server.sh
|
||||
#
|
||||
# author: deng
|
||||
# date : 20230221
|
||||
|
||||
mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./artifacts
|
||||
|
|
@ -0,0 +1,79 @@
|
|||
# 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')
|
||||
|
||||
print('Completed.')
|
||||
Loading…
Reference in New Issue